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ANN
Terms modified by ANN Selected AbstractsStructural Health Monitoring via Measured Ritz Vectors Utilizing Artificial Neural NetworksCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 4 2006Heung-Fai Lam Unlike most other pattern recognition methods, an artificial neural network (ANN) technique is employed as a tool for systematically identifying the damage pattern corresponding to an observed feature. An important aspect of using an ANN is its design but this is usually skipped in the literature on ANN-based SHM. The design of an ANN has significant effects on both the training and performance of the ANN. As the multi-layer perceptron ANN model is adopted in this work, ANN design refers to the selection of the number of hidden layers and the number of neurons in each hidden layer. A design method based on a Bayesian probabilistic approach for model selection is proposed. The combination of the pattern recognition method and the Bayesian ANN design method forms a practical SHM methodology. A truss model is employed to demonstrate the proposed methodology. [source] Prediction of Onset of Corrosion in Concrete Bridge Decks Using Neural Networks and Case-Based ReasoningCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 2 2005G. Morcous It is based on the integration of artificial neural network (ANN), case-based reasoning (CBR), mechanistic model, and Monte Carlo simulation (MCS). A probabilistic mechanistic model is used to generate the distribution of the time to corrosion initiation based on statistical models of the governing parameters obtained from field data. The proposed ANN and CBR models act as universal functional mapping tools to approximate the relationship between the input and output of the mechanistic model. These tools are integrated with the MCS technique to generate the distribution of the corrosion initiation time using the distributions of the governing parameters. The proposed methodology is applied to predict the time to corrosion initiation of the top reinforcing steel in the concrete deck of the Dickson Bridge in Montreal. This study demonstrates the feasibility, adequate reliability, and computational efficiency of the proposed integrated ANN-MCS and CBR-MCS approaches for preliminary project-level and also network-level analyses. [source] Nonparametric Identification of a Building Structure from Experimental Data Using Wavelet Neural NetworkCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2003Shih-Lin Hung By combining wavelet decomposition and artificial neural networks (ANN), wavelet neural networks (WNN) are used for solving chaotic signal processing. The basic operations and training method of wavelet neural networks are briefly introduced, since these networks can approximate universal functions. The feasibility of structural behavior modeling and the possibility of structural health monitoring using wavelet neural networks are investigated. The practical application of a wavelet neural network to the structural dynamic modeling of a building frame in shaking tests is considered in an example. Structural acceleration responses under various levels of the strength of the Kobe earthquake were used to train and then test the WNNs. The results reveal that the WNNs not only identify the structural dynamic model, but also can be applied to monitor the health condition of a building structure under strong external excitation. [source] Probabilistic Neural Network for Reliability Assessment of Oil and Gas PipelinesCOMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, Issue 5 2002Sunil K. Sinha A fuzzy artificial neural network (ANN),based approach is proposed for reliability assessment of oil and gas pipelines. The proposed ANN model is trained with field observation data collected using magnetic flux leakage (MFL) tools to characterize the actual condition of aging pipelines vulnerable to metal loss corrosion. The objective of this paper is to develop a simulation-based probabilistic neural network model to estimate the probability of failure of aging pipelines vulnerable to corrosion. The approach is to transform a simulation-based probabilistic analysis framework to estimate the pipeline reliability into an adaptable connectionist representation, using supervised training to initialize the weights so that the adaptable neural network predicts the probability of failure for oil and gas pipelines. This ANN model uses eight pipe parameters as input variables. The output variable is the probability of failure. The proposed method is generic, and it can be applied to several decision problems related with the maintenance of aging engineering systems. [source] Simultaneous Quantification of Heavy Metals Using a Solid State Potentiometric Sensor ArrayELECTROANALYSIS, Issue 8 2009Jesús Gismera Abstract A potentiometric sensor array of four nonspecific electrodes with solid-state membranes is developed and tested for simultaneous analysis of copper(II), mercury(II), and silver(I) ions. The cross-sensitivity responses of the sensors for these ions are evaluated. The array potentiometric signals are processed by partial least-squares regression (PLS) and back propagation artificial neural networks (ANN) to determinate analyte concentrations. The ANN configuration is optimized and two different training algorithms of the ANN are also evaluated. Best results are obtained when the potentiometric sensors are activated and the data are processed using ANN and the gradient descent adaptive algorithm. The system is used to quantify these heavy metals in synthetic samples and in dental amalgams with successful results. [source] Tensammetric Analysis of Nonionic Surfactant Mixtures by Artificial Neural NetworkELECTROANALYSIS, Issue 12 2005A. Safavi Abstract An artificial neural network (ANN) model has been developed for tensammetric determination of a series of Brijes (Brij 30, Brij 35, Brij 56, Brij 96) as nonionic surfactants. The tensammetric method is based on the measurement of the capacitive current of the mercury electrode after adsorption of surfactants. All Brijes were analyzed in the concentration range of 1.0,100.0,,g mL,1. The proposed method shows good sensitivity and applicability to the simultaneous determination of mixtures of four Brijes in aqueous solutions. [source] Two variable semi-empirical and artificial neural-network-based modeling of peptide mobilities in CZE: The effect of temperature and organic modifier concentrationELECTROPHORESIS, Issue 5 2009Stefan Mittermayr Abstract This work was focused on investigating the effects of two separation influencing parameters in CZE, namely temperature and organic additive concentration upon the electrophoretic migration properties of model tripeptides. Two variable semi-empirical (TVSE) models and back-propagation artificial neural networks (ANN) were applied to predict the electrophoretic mobilities of the tripeptides with non-polar, polar, positively charged, negatively charged and aromatic R group characteristics. Previously published work on the subject did not account for the effect of temperature and buffer organic modifier concentration on peptide mobility, in spite of the fact that both were considered to be influential factors in peptide analysis. In this work, a substantial data set was generated consisting of actual electrophoretic mobilities of the model tripeptides in 30,mM phosphate buffer at pH 7.5, at 20, 25, 30, 35 and 40°C and at four different organic additive containing running buffers (0, 5, 10 and 15% MeOH) applying two electric field strengths (12 and 16,kV) to assess our mobility predicting models. Based on the Arrhenius plots of natural logarithm of mobility versus reciprocal absolute temperature of the various experimental setups, the corresponding activation energy values were derived and evaluated. Calculated mobilities by TVSE and back-propagation ANN models were compared with each other and to the experimental data, respectively. Neural network approaches were able to model the complex impact of both temperature and organic additive concentrations and resulted in considerably higher predictive power over the TVSE models, justifying that the effect of these two factors should not be neglected. [source] Stepwise geographical traceability of virgin olive oils by chemical profiles using artificial neural network modelsEUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY, Issue 10 2009Diego L. García-González Abstract The geographical traceability of virgin olive oils implies the use of analytical methods that allow the identification of the origin of the oil and the authentication of the information boasted on the labels. In this work, the geographical identification of the virgin olive oils has been addressed by complete chemical characterisation of samples (64 compounds analysed by GC and HPLC) and the design of artificial neural network (ANN) models for each one of the levels of a proposed classification scheme. A high number of samples (687) from Spain, Italy and Portugal served as training and test sets for the ANN models. The highest classification level, focused on the grouping of samples by country, was achieved through analysis of fatty acids, with 99.9% of samples classified. Other levels (region, province, Protected Designations of Origin or PDO) were focused on Spanish oils and required additional series of compounds (sterols, alcohols, hydrocarbons) as well as the fatty acids to obtain classification rates higher than 90%. The classification of oils into different PDOs , the last and most difficult level of classification , showed the highest root mean square errors. The classification percentages, however, were still higher than 90% in the test set, which proves the application of the traceability methodology for a chemical verification of PDO claims. [source] Random forest can predict 30-day mortality of spontaneous intracerebral hemorrhage with remarkable discriminationEUROPEAN JOURNAL OF NEUROLOGY, Issue 7 2010S. -Y. Background and purpose:, Risk-stratification models based on patient and disease characteristics are useful for aiding clinical decisions and for comparing the quality of care between different physicians or hospitals. In addition, prediction of mortality is beneficial for optimizing resource utilization. We evaluated the accuracy and discriminating power of the random forest (RF) to predict 30-day mortality of spontaneous intracerebral hemorrhage (SICH). Methods:, We retrospectively studied 423 patients admitted to the Taichung Veterans General Hospital who were diagnosed with spontaneous SICH within 24 h of stroke onset. The initial evaluation data of the patients were used to train the RF model. Areas under the receiver operating characteristic curves (AUC) were used to quantify the predictive performance. The performance of the RF model was compared to that of an artificial neural network (ANN), support vector machine (SVM), logistic regression model, and the ICH score. Results:, The RF had an overall accuracy of 78.5% for predicting the mortality of patients with SICH. The sensitivity was 79.0%, and the specificity was 78.4%. The AUCs were as follows: RF, 0.87 (0.84,0.90); ANN, 0.81 (0.77,0.85); SVM, 0.79 (0.75,0.83); logistic regression, 0.78 (0.74,0.82); and ICH score, 0.72 (0.68,0.76). The discriminatory power of RF was superior to that of the other prediction models. Conclusions:, The RF provided the best predictive performance amongst all of the tested models. We believe that the RF is a suitable tool for clinicians to use in predicting the 30-day mortality of patients after SICH. [source] Zero-sequence-based relaying technique for protecting power transformers and its performance assessment using unsupervised learning ANNEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 2 2006Guzmán Díaz Abstract In this paper a simple and robust new relaying technique for protecting transformers from internal winding faults is proposed. Based on the measurement of zero sequence current inside a delta winding, the technique greatly simplifies the conventional differential relaying arrangement when a delta winding is available. Despite the number of windings of the transformer and the location of the fault, only measurement of induced zero sequence current within the delta winding is needed. Since the proposed technique has been shown to be prone to generate false pick-up signals during inrush, a simple restraining criterion is proposed and analysed. Additionally, use of projection techniques based on self-organizing maps (SOM) is proposed in this paper as a valuable tool for analysing multivariable data which are generated from the huge number of possible combinations existing between switching instant and fault location. Finite element simulations and laboratory tests have been combined into SOM to validate the proposed relaying technique and the restraining criterion. Copyright © 2005 John Wiley & Sons, Ltd. [source] Fault detection and classification technique in EHV transmission lines based on artificial neural networksEUROPEAN TRANSACTIONS ON ELECTRICAL POWER, Issue 5 2005Tahar Bouthiba Abstract This paper investigates a new approach based on Artificial Neural Networks (ANNs) for real-time fault detection and classification in power transmission lines which can be used in digital power system protection. The Fault Detector and Classifier (FDC) consists of four independent ANNs. The technique uses consecutive magnitude current and voltage data at one terminal as inputs to the corresponding ANN. The ANN outputs are used to indicate simultaneously the presence and the type of the fault. The FDC is tested under different fault types, fault locations, fault resistances and fault inception angles. All test results show that the proposed FDC can be used for very high speed digital relaying. Copyright © 2005 John Wiley & Sons, Ltd. [source] A study on non-invasive detection of blood glucose concentration from human palm perspiration by using artificial neural networksEXPERT SYSTEMS, Issue 3 2010Hamdi Melih Sarao Abstract: In this paper the relationship between blood glucose concentration and palm perspiration rate is studied as a non-invasive method. A glucose concentration range from 83 mg/dl to 116.5 mg/dl is examined. An artificial neural network (ANN) trained by the Levenberg,Marquardt algorithm is developed to detect the performance indices based on the one- and two-input variables. A data set for 72 volunteers is used for this study. Data of 36 volunteers are used for training the ANN and data of 36 volunteers were reserved for testing. Results of the study are acceptable with an error of 8.38% for the Elman neural network and 8.77% for the multilayer neural network. Therefore, the palm perspiration rate may be used as a good indicator for detecting glucose concentration in blood. This non-invasive method has advantages such as time saving, cost etc. over other methods and it is painless. The results of clinical experiments, follow-up methods and other applications are presented. [source] Feedforward networks in financial predictions: the future that modifies the presentEXPERT SYSTEMS, Issue 3 2000Massimo Budcema The main goal of this paper is to show how relatively minor modifications of well-known algorithms (in particular, back propagation) can dramatically increase the performance of an artificial neural network (ANN) for time series prediction. We denote our proposed sets of modifications as the 'self-momentum', 'Freud' and 'Jung' rules. In our opinion, they provide an example of an alternative approach to the design of learning strategies for ANNs, one that focuses on basic mathematical conceptualization rather than on formalism and demonstration. The complexity of actual prediction problems makes it necessary to experiment with modelling possibilities whose inherent mathematical properties are often not well understood yet. The problem of time series prediction in stock markets is a case in point. It is well known that asset price dynamics in financial markets are difficult to trace, let alone to predict with an operationally interesting degree of accuracy. We therefore take financial prediction as a meaningful test bed for the validation of our techniques. We discuss in some detail both the theoretical underpinnings of the technique and our case study about financial prediction, finding encouraging evidence that supports the theoretical and operational viability of our new ANN specifications. Ours is clearly only a preliminary step. Further developments of ANN architectures with more and more sophisticated 'learning to learn' characteristics are now under study and test. [source] Artificial neural network inversion of magnetotelluric data in terms of three-dimensional earth macroparametersGEOPHYSICAL JOURNAL INTERNATIONAL, Issue 1 2000Vjacheslav Spichak The possibility of solving the three-dimensional (3-D) inverse problem of geoelectrics using the artificial neural network (ANN) approach is investigated. The properties of a supervised ANN based on the back-propagation scheme with three layers of neurons are studied, and the ANN architecture is adjusted. A model class consisting of a dipping dyke in the basement of a two-layer earth with the dyke in contact with the overburden is used for numerical experiments. Six macroparameters of the 3-D model, namely the thickness of the top layer, which coincides with the depth of the dyke (D), the conductivity ratio between the first and second layers (C1,/C2,), the conductivity contrast of the dyke (C/C2,), and the width (W ), length (L ) and dip angle of the dyke (A), are used. Various groups of magnetotelluric field components and their transformations are studied in order to estimate the effect of the data type used on the ANN recognition ability. It is found that use of only the xy - and yx -components of impedance phases results in reasonable recognition errors for all unknown parameters (D: 0.02 per cent, C1/C2: 8.4 per cent, C/C2: 26.8 per cent, W : 0.02 per cent, L : 0.02 per cent, A: 0.24 per cent). The influence of the size and shape of the training data pool (including the ,gaps in education' and ,no target' effects) on the recognition properties is studied. Results from numerous ANN tests demonstrate that the ANN possesses good enough interpolation and extrapolation abilities if the training data pool contains a sufficient number of representative data sets. The effect of noise is estimated by means of mixing the synthetic data with 30, 50 and 100 per cent Gaussian noise. The unusual behaviour of the recognition errors for some of the model parameters when the data become more noisy (in particular, the fact that an increase in error is followed by a decrease) indicates that the use of standard techniques of noise reduction may give an opposite result, so the development of a special noise treatment methodology is required. Thus, it is shown that ANN-based recognition can be successfully used for inversion if the data correspond to the model class familiar to the ANN. No initial guess regarding the parameters of the 3-D target or 1-D layering is required. The ability of the ANN to teach itself using real geophysical (not only electromagnetic) data measured at a given location over a sufficiently long period means that there is the potential to use this approach for interpreting monitoring data. [source] BIOMOD , optimizing predictions of species distributions and projecting potential future shifts under global changeGLOBAL CHANGE BIOLOGY, Issue 10 2003Wilfried ThuillerArticle first published online: 9 OCT 200 Abstract A new computation framework (BIOMOD: BIOdiversity MODelling) is presented, which aims to maximize the predictive accuracy of current species distributions and the reliability of future potential distributions using different types of statistical modelling methods. BIOMOD capitalizes on the different techniques used in static modelling to provide spatial predictions. It computes, for each species and in the same package, the four most widely used modelling techniques in species predictions, namely Generalized Linear Models (GLM), Generalized Additive Models (GAM), Classification and Regression Tree analysis (CART) and Artificial Neural Networks (ANN). BIOMOD was applied to 61 species of trees in Europe using climatic quantities as explanatory variables of current distributions. On average, all the different modelling methods yielded very good agreement between observed and predicted distributions. However, the relative performance of different techniques was idiosyncratic across species, suggesting that the most accurate model varies between species. The results of this evaluation also highlight that slight differences between current predictions from different modelling techniques are exacerbated in future projections. Therefore, it is difficult to assess the reliability of alternative projections without validation techniques or expert opinion. It is concluded that rather than using a single modelling technique to predict the distribution of several species, it would be more reliable to use a framework assessing different models for each species and selecting the most accurate one using both evaluation methods and expert knowledge. [source] Investigating the impact of the Chi-Chi earthquake on the occurrence of debris flows using artificial neural networksHYDROLOGICAL PROCESSES, Issue 19 2009Fi-John Chang Abstract Debris flows have caused enormous losses of property and human life in Taiwan during the last two decades. An efficient and reliable method for predicting the occurrence of debris flows is required. The major goal of this study is to explore the impact of the Chi-Chi earthquake on the occurrence of debris flows by applying the artificial neural network (ANN) that takes both hydrological and geomorphologic influences into account. The Chen-Yu-Lan River watershed, which is located in central Taiwan, is chosen for evaluating the critical rainfall triggering debris flows. A total of 1151 data sets were collected for calibrating model parameters with two training strategies. Significant differences before and after the earthquake have been found: (1) The size of landslide area is proportioned to the occurrence of debris flows; (2) the amount of critical rainfall required for triggering debris flows has reduced significantly, about half of the original critical rainfall in the study case; and (3) the frequency of the occurrence of debris flows is largely increased. The overall accuracy of model prediction in testing phase has reached 96·5%; moreover, the accuracy of occurrence prediction is largely increased from 24 to 80% as the network trained with data from before the Chi-Chi earthquake sets and with data from the lumped before and after the earthquake sets. The results demonstrated that the ANN is capable of learning the complex mechanism of debris flows and producing satisfactory predictions. Copyright © 2009 John Wiley & Sons, Ltd. [source] Daily pan evaporation modelling using multi-layer perceptrons and radial basis neural networksHYDROLOGICAL PROCESSES, Issue 2 2009Özgür Ki Abstract This paper reports on investigations of the abilities of three different artificial neural network (ANN) techniques, multi-layer perceptrons (MLP), radial basis neural networks (RBNN) and generalized regression neural networks (GRNN) to estimate daily pan evaporation. Different MLP models comprising various combinations of daily climatic variables, that is, air temperature, solar radiation, wind speed, pressure and humidity were developed to evaluate the effect of each of these variables on pan evaporation. The MLP estimates are compared with those of the RBNN and GRNN techniques. The Stephens-Stewart (SS) method is also considered for the comparison. The performances of the models are evaluated using root mean square errors (RMSE), mean absolute error (MAE) and determination coefficient (R2) statistics. Based on the comparisons, it was found that the MLP and RBNN computing techniques could be employed successfully to model the evaporation process using the available climatic data. The GRNN was found to perform better than the SS method. Copyright © 2008 John Wiley & Sons, Ltd. [source] A data-driven algorithm for constructing artificial neural network rainfall-runoff modelsHYDROLOGICAL PROCESSES, Issue 6 2002K. P. Sudheer Abstract A new approach for designing the network structure in an artificial neural network (ANN)-based rainfall-runoff model is presented. The method utilizes the statistical properties such as cross-, auto- and partial-auto-correlation of the data series in identifying a unique input vector that best represents the process for the basin, and a standard algorithm for training. The methodology has been validated using the data for a river basin in India. The results of the study are highly promising and indicate that it could significantly reduce the effort and computational time required in developing an ANN model. Copyright © 2002 John Wiley & Sons, Ltd. [source] Improving extreme hydrologic events forecasting using a new criterion for artificial neural network selectionHYDROLOGICAL PROCESSES, Issue 8 2001Paulin Coulibaly Abstract The issue of selecting appropriate model input parameters is addressed using a peak and low flow criterion (PLC). The optimal artificial neural network (ANN) models selected using the PLC significantly outperform those identified with the classical root-mean-square error (RMSE) or the conventional Nash,Sutcliffe coefficient (NSC) statistics. The comparative forecast results indicate that the PLC can help to design an appropriate ANN model to improve extreme hydrologic events (peak and low flow) forecast accuracy. Copyright © 2001 John Wiley & Sons, Ltd. [source] Back analysis of model parameters in geotechnical engineering by means of soft computingINTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 14 2003B. Pichler Abstract In this paper, a parameter identification (PI) method for determination of unknown model parameters in geotechnical engineering is proposed. It is based on measurement data provided by the construction site. Model parameters for finite element (FE) analyses are identified such that the results of these calculations agree with the available measurement data as well as possible. For determination of the unknown model parameters, use of an artificial neural network (ANN) is proposed. The network is trained to approximate the results of FE simulations. A genetic algorithm (GA) uses the trained ANN to provide an estimate of optimal model parameters which, finally, has to be assessed by an additional FE analysis. The presented mode of PI renders back analysis of model parameters feasible even for large-scale models as used in geotechnical engineering. The advantages of theoretical developments concerning both the structure and the training of the ANN are illustrated by the identification of material properties from experimental data. Finally, the performance of the proposed PI method is demonstrated by two problems taken from geotechnical engineering. The impact of back analysis on the actual construction process is outlined. Copyright © 2003 John Wiley & Sons, Ltd. [source] Elucidation of a protein signature discriminating six common types of adenocarcinomaINTERNATIONAL JOURNAL OF CANCER, Issue 4 2007Gregory C. Bloom Abstract Pathologists are commonly facing the problem of attempting to identify the site of origin of a metastatic cancer when no primary tumor has been identified, yet few markers have been identified to date. Multitumor classifiers based on microarray based RNA expression have recently been described. Here we describe the first approximation of a tumor classifier based entirely on protein expression quantified by two-dimensional gel electrophoresis (2DE). The 2DE was used to analyze the proteomic expression pattern of 77 similarly appearing (using histomorphology) adenocarcinomas encompassing 6 types or sites of origin: ovary, colon, kidney, breast, lung and stomach. Discriminating sets of proteins were identified and used to train an artificial neural network (ANN). A leave-one-out cross validation (LOOCV) method was used to test the ability of the constructed network to predict the single held out sample from each iteration with a maximum predictive accuracy of 87% and an average predictive accuracy of 82% over the range of proteins chosen for its construction. These findings demonstrate the use of proteomics to construct a highly accurate ANN-based classifier for the detection of an individual tumor type, as well as distinguishing between 6 common tumor types in an unknown primary diagnosis setting. © 2006 Wiley-Liss, Inc. [source] Simulation of seasonal precipitation and raindays over Greece: a statistical downscaling technique based on artificial neural networks (ANNs)INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 7 2007K. Tolika Abstract A statistical downscaling technique based on artificial neural network (ANN) was employed for the estimation of local changes on seasonal (winter, spring) precipitation and raindays for selected stations over Greece. Empirical transfer functions were derived between large-scale predictors from the NCEP/NCAR reanalysis and local rainfall parameters. Two sets of predictors were used: (1) the circulation-based 500 hPa and (2) its combination along with surface specific humidity and raw precipitation data (nonconventional predictor). The simulated time series were evaluated against observational data and the downscaling model was found efficient in generating winter and spring precipitation and raindays. The temporal evolution of the estimated variables was well captured, for both seasons. Generally, the use of the nonconventional predictors are attributed to the improvement of the simulated results. Subsequently, the present day and future changes on precipitation conditions were examined using large-scale data from the atmospheric general circulation model HadAM3P to the statistical model. The downscaled climate change signal for both precipitation and raindays, partly for winter and especially for spring, is similar to the signal from the HadAM3P direct output: a decrease of the parameters is predicted over the study area. However, the amplitude of the changes was different. Copyright © 2006 Royal Meteorological Society [source] Towards ice-core-based synoptic reconstructions of west antarctic climate with artificial neural networksINTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 5 2005David B. Reusch Abstract Ice cores have, in recent decades, produced a wealth of palaeoclimatic insights over widely ranging temporal and spatial scales. Nonetheless, interpretation of ice-core-based climate proxies is still problematic due to a variety of issues unrelated to the quality of the ice-core data. Instead, many of these problems are related to our poor understanding of key transfer functions that link the atmosphere to the ice. This study uses two tools from the field of artificial neural networks (ANNs) to investigate the relationship between the atmosphere and surface records of climate in West Antarctica. The first, self-organizing maps (SOMs), provides an unsupervised classification of variables from the mid-troposphere (700 hPa temperature, geopotential height and specific humidity) into groups of similar synoptic patterns. An SOM-based climatology at annual resolution (to match ice-core data) has been developed for the period 1979,93 based on the European Centre for Medium-Range Weather Forecasts (ECMWF) 15-year reanalysis (ERA-15) dataset. This analysis produced a robust mapping of years to annual-average synoptic conditions as generalized atmospheric patterns or states. Feed-forward ANNs, our second ANN-based tool, were then used to upscale from surface data to the SOM-based classifications, thereby relating the surface sampling of the atmosphere to the large-scale circulation of the mid-troposphere. Two recorders of surface climate were used in this step: automatic weather stations (AWSs) and ice cores. Six AWS sites provided 15 years of near-surface temperature and pressure data. Four ice-core sites provided 40 years of annual accumulation and major ion chemistry. Although the ANN training methodology was properly designed and followed standard principles, limited training data and noise in the ice-core data reduced the effectiveness of the upscaling predictions. Despite these shortcomings, which might be expected to preclude successful analyses, we find that the combined techniques do allow ice-core reconstruction of annual-average synoptic conditions with some skill. We thus consider the ANN-based approach to upscaling to be a useful tool, but one that would benefit from additional training data. Copyright © 2005 Royal Meteorological Society. [source] Thermodynamic analysis of subcooling and superheating effects of alternative refrigerants for vapour compression refrigeration cyclesINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 5 2006encan Abstract This paper presents a computer-based first law and exergy analysis applied to vapour compression refrigeration systems for determining subcooling and superheating effects of environmentally safe new refrigerants. Three refrigerants are considered: R134a, R407c and R410a. It is found that subcooling and superheating temperatures directly influence the system performance as both condenser and evaporator temperatures are affected. The thermodynamic properties of the refrigerants are formulated using artificial neural network (ANN) methodology. Six ANNs were trained to predict various properties of the three refrigerants. The training and validation of the ANNs were performed with good accuracy. The correlation coefficient obtained when unknown data were used to the networks were found to be equal or very near to 1 which is very satisfactory. Additionally, the present methodology proved to be much better than the linear multiple regression analysis. From the analysis of the results it is found that condenser and evaporator temperatures have strong effects on coefficient of performance (COP) and system irreversibility. Also both subcooling and superheating affect the system performance. This effect is similar for R134a and R407c, and different for R410a. Copyright © 2005 John Wiley & Sons, Ltd. [source] Artificial neural networks applications in building energy predictions and a case study for tropical climatesINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 10 2005Melek Yalcintas Abstract This study presents artificial neural network (ANN) methods in building energy use predictions. Applications of the ANN methods in energy audits and energy savings predictions due to building retrofits are emphasized. A generalized ANN model that can be applied to any building type with minor modifications would be a very useful tool for building engineers. ANN methods offer faster learning time, simplicity in analysis and adaptability to seasonal climate variations and changes in the building's energy use when compared to other statistical and simulation models. The model herein is presented for predicting chiller plant energy use in tropical climates with small seasonal and daily variations. It was successfully created based on both climatic and chiller data. The average absolute training error for the model was 9.7% while the testing error was 10.0%. This indicates that the model can successfully predict the particular chiller energy consumption in a tropical climate. Copyright © 2005 John Wiley & Sons, Ltd. [source] Multi-objective optimization of the coal combustion performance with artificial neural networks and genetic algorithmsINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 6 2005Hao Zhou Abstract The present work introduces an approach to predict the nitrogen oxides (NOx) emissions and carbon burnout characteristics of a large capacity pulverized coal-fired boiler with an artificial neural network (ANN). The NOx emissions and carbon burnout characteristics are investigated by parametric field experiments. The effects of over-fire-air (OFA) flow rates, coal properties, boiler load, air distribution scheme and nozzle tilt are studied. An ANN is used to model the NOx emissions characteristics and the carbon burnout characteristics. A genetic algorithm (GA) is employed to perform a multi-objective search to determine the optimum solution of the ANN model, finding the optimal setpoints, which can suggest operators' correct actions to decrease NOx emissions and the carbon content in the flyash simultaneously, namely, get a good boiler combustion performance with high boiler efficiency while keeping the NOx emission concentration meet the requirement. Copyright © 2005 John Wiley & Sons, Ltd. [source] On the use of reactive power as an endogenous variable in short-term load forecastingINTERNATIONAL JOURNAL OF ENERGY RESEARCH, Issue 5 2003P. Jorge Santos Abstract In the last decades, short-term load forecasting(STLF) has been the object of particular attention in the power systems field. STLF has been applied almost exclusively to the generation sector, based on variables, which are transversal to most models. Among the most significant variables we can find load, expressed as active power (MW), as well as exogenous variables, such as weather and economy-related ones; although the latter are applied in larger forecasting horizons than STLF. In this paper, the application of STLF to the distribution sector is suggested including inductive reactive power as a forecasting endogenous variable. The inclusion of this additional variable is mainly due to the evidence that correlations between load and weather variables are tenuous, due to the mild climate of the actual case-study system and the consequent feeble penetration of electrical heating ventilation and air conditioning loads. Artificial neural networks (ANN) have been chosen as the forecasting methodology, with standard feed forward back propagation algorithm, because it is a largely used method with generally considered satisfactory results. Usually the input vector to ANN applied to load forecasting is defined in a discretionary way, mainly based on experience, on engineering judgement criteria and on concern about the ANN dimension, always taking into consideration the apparent (or actually evaluated) correlations within the available data. The approach referred in the paper includes pre-processing the data in order to influence the composition of the input vector in such a way as to reduce the margin of discretion in its definition. A relative entropy analysis has been performed to the time series of each variable. The paper also includes an illustrative case study. Copyright © 2003 John Wiley & Sons, Ltd. [source] An efficient neural network approach for nanoscale FinFET modelling and circuit simulationINTERNATIONAL JOURNAL OF NUMERICAL MODELLING: ELECTRONIC NETWORKS, DEVICES AND FIELDS, Issue 5 2009M. S. Alam Abstract The present paper demonstrates the suitability of artificial neural network (ANN) for modelling of a FinFET in nano-circuit simulation. The FinFET used in this work is designed using careful engineering of source,drain extension, which simultaneously improves maximum frequency of oscillation ,max because of lower gate to drain capacitance, and intrinsic gain AV0,=,gm/gds, due to lower output conductance gds. The framework for the ANN-based FinFET model is a common source equivalent circuit, where the dependence of intrinsic capacitances, resistances and dc drain current Id on drain,source Vds and gate,source Vgs is derived by a simple two-layered neural network architecture. All extrinsic components of the FinFET model are treated as bias independent. The model was implemented in a circuit simulator and verified by its ability to generate accurate response to excitations not used during training. The model was used to design a low-noise amplifier. At low power (Jds,10,µA/µm) improvement was observed in both third-order-intercept IIP3 (,10,dBm) and intrinsic gain AV0 (,20,dB), compared to a comparable bulk MOSFET with similar effective channel length. This is attributed to higher ratio of first-order to third-order derivative of Id with respect to gate voltage and lower gds in FinFET compared to bulk MOSFET. Copyright © 2009 John Wiley & Sons, Ltd. [source] Support vector design of the microstrip linesINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 4 2008Filiz Güne Abstract In this article, the support vector regression is adapted to the analysis and synthesis of microstrip lines on all isotropic/anisotropic dielectric materials, which is a novel technique based on the rigorous mathematical fundamentals and the most competitive technique to the popular artificial neural networks (ANN). In this design process, accuracy, computational efficiency and number of support vectors are investigated in detail and the support vector regression performance is compared with an ANN performance. It can be concluded that the ANN may be replaced by the support vector machines in the regression applications because of its higher approximation capability and much faster convergence rate with the sparse solution technique. Synthesis is achieved by utilizing the analysis black-box bidirectionally by reverse training. Furthermore, by using the adaptive step size, a much faster convergence rate is obtained in the reverse training. Besides, design of microstrip lines on the most commonly used isotropic/anisotropic dielectric materials are given as the worked examples. © 2008 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2008. [source] Signal-noise support vector model of a microwave transistorINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 4 2007Filiz Güne Abstract In this work, a support vector machines (SVM) model for the small-signal and noise behaviors of a microwave transistor is presented and compared with its artificial neural network (ANN) model. Convex optimization and generalization properties of SVM are applied to the black-box modeling of a microwave transistor. It has been shown that SVM has a high potential of accurate and efficient device modeling. This is verified by giving a worked example as compared with ANN which is another commonly used modeling technique. It can be concluded that hereafter SVM modeling is a strongly competitive approach against ANN modeling. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007. [source] | ||||||||||||||||||||||||||||||||||||||||||||||