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ANN Models (ann + models)
Selected AbstractsTwo 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] Applications of artificial neural networks to RF and microwave measurementsINTERNATIONAL JOURNAL OF RF AND MICROWAVE COMPUTER-AIDED ENGINEERING, Issue 1 2002Jeffrey A. Jargon Abstract This article describes how artificial neural networks (ANNs) can be used to benefit a number of RF and microwave measurement areas including vector network analysis (VNA). We apply ANNs to model a variety of on-wafer and coaxial VNA calibrations, including open-short-load-thru (OSLT) and line-reflect-match (LRM), and assess the accuracy of the calibrations using these ANN-modeled standards. We find that the ANN models compare favorably to benchmark calibrations throughout the frequencies they were trained for. We summarize other current applications of ANNs, including the determination of permittivities of liquids from the reflection coefficient measurements of an open-ended coaxial probe and the determination of moisture content of wheat from free-space transmission coefficient measurements. We also discuss some potential applications of ANN models related to power measurements, material characterization, and the comparison of nonlinear vector network analyzers. © 2002 John Wiley & Sons, Inc. Int J RF and Microwave CAE 12: 3,24, 2002. [source] Hydrological disturbance benefits a native fish at the expense of an exotic fishJOURNAL OF APPLIED ECOLOGY, Issue 5 2006F. LEPRIEUR Summary 1Some native fish in New Zealand do not coexist with introduced salmonids. Previous studies of disjunct distributions of exotic brown trout Salmo trutta and native galaxiids demonstrated native extirpation except where major waterfalls prevented upstream migration of trout. In the Manuherikia River system, we predicted that water abstraction might be a further factor controlling the spatial distribution of both the invader and a native fish. 2We applied multiple discriminant function analyses to test for differences in environmental conditions (catchment and instream scales) at sites with roundhead galaxias Galaxias anomalus and brown trout in sympatry and allopatry. We then used a supervised artificial neural network (ANN) to predict the presence,absence of G. anomalus and brown trout (135 sites). The quantification of contributions of environmental variables to ANN models allowed us to identify factors controlling their spatial distribution. 3Brown trout can reach most locations in the Manuherikia catchment, and often occur upstream of G. anomalus. Their largely disjunct distributions in this river are mediated by water abstraction for irrigation, together with pool habitat availability and valley slope. Trout are more susceptible than the native fish to stresses associated with low flows, and seem to be prevented from eliminating galaxiid populations from sites in low gradient streams where there is a high level of water abstraction. 4Synthesis and applications. In contrast to many reports in the literature, our results show that hydrological disturbance associated with human activities benefits a native fish at the expense of an exotic in the Manuherikia River, New Zealand. Water abstraction is also known to have negative impacts on native galaxiids, therefore we recommend restoring natural low flows to maintain sustainable habitats for native galaxiids, implementing artificial barriers in selected tributaries to limit trout predation on native fish, and removing trout upstream. [source] NEURAL NETWORK MODELING OF END-OVER-END THERMAL PROCESSING OF PARTICULATES IN VISCOUS FLUIDSJOURNAL OF FOOD PROCESS ENGINEERING, Issue 2010YANG MENG ABSTRACT Modeling of the heat transfer process in thermal processing is important for the process design and control. Artificial neural networks (ANNs) have been used in recent years in heat transfer modeling as a potential alternative to conventional dimensionless correlation approach and shown to be even better performers. In this study, ANN models were developed for apparent heat transfer coefficients associated with canned particulates in high viscous Newtonian and non-Newtonian fluids during end-over-end thermal processing in a pilot-scale rotary retort. A portion of experimental data obtained for the associated heat transfer coefficients were used for training while the rest were used for testing. The principal configuration parameters were the combination of learning rules and transfer functions, number of hidden layers, number of neurons in each hidden layer and number of learning runs. For the Newtonian fluids, the optimal conditions were two hidden layers, five neurons in each hidden layer, the delta learning rule, a sine transfer function and 40,000 learning runs, while for the non-Newtonian fluids, the optimal conditions were one hidden layer, six neurons in each hidden layer, the delta learning rule, a hyperbolic tangent transfer function and 50,000 learning runs. The prediction accuracies for the ANN models were much better compared with those from the dimensionless correlations. The trained network was found to predict responses with a mean relative error of 2.9,3.9% for the Newtonian fluids and 4.7,5.9% for the non-Newtonian fluids, which were 27,62% lower than those associated with the dimensionless correlations. Algebraic solutions were included, which could be used to predict the heat transfer coefficients without requiring an ANN. PRACTICAL APPLICATIONS The artificial neural network (ANN) model is a network of computational elements that was originally developed to mimic the function of the human brain. ANN models do not require the prior knowledge of the relationship between the input and output variables because they can discover the relationship through successive training. Moreover, ANN models can predict several output variables at the same time, which is difficult in general regression methods. ANN concepts have been successfully used in food processing for prediction, quality control and pattern recognition. ANN models have been used in recent years for heat transfer modeling as a potential alternative to conventional dimensionless correlation approach and shown to be even better performers. In this study, ANN models were successfully developed for the heat transfer parameters associated with canned particulate high viscous Newtonian and non-Newtonian fluids during an end-over-end rotation thermal processing. Optimized configuration parameters were obtained by choosing appropriate combinations of learning rule, transfer function, learning runs, hidden layers and number of neurons. The trained network was found to predict parameter responses with mean relative errors considerably lower than from dimensionless correlations. [source] DYNAMIC MODELING OF RETORT PROCESSING USING NEURAL NETWORKSJOURNAL OF FOOD PROCESSING AND PRESERVATION, Issue 2 2002C. R. CHEN ABSTRACT Two neural network approaches , a moving-window and hybrid neural network , which combine neural network with polynomial regression models, were used for modeling F(t) and Qv(t) dynamic functions under constant retort temperature processing. The dynamic functions involved six variables: retort temperature (116,132C), thermal diffusivity (1.5,2.3 × 10,7m2/s), can radius (40,61 mm), can height (40,61 mm), and quality kinetic parameters z (15,39C) and D (150,250 min). A computer simulation designed for process calculations of food thermal processing systems was used to provide the fundamental data for training and generalization of ANN models. Training data and testing data were constructed by both second order central composite design and orthogonal array, respectively. The optimal configurations of ANN models were obtained by varying the number of hidden layers, number of neurons in hidden layer and learning runs, and a combination of learning rules and transfer function. Results demonstrated that both neural network models well described the F(t) and Qv(t) dynamic functions, but moving-window network had better modeling performance than the hybrid ANN models. By comparison of the configuration parameters, moving-window ANN models required more neurons in the hidden layer and more learning runs for training than the hybrid ANN models. [source] Host,parasitoid population density prediction using artificial neural networks: diamondback moth and its natural enemiesAGRICULTURAL AND FOREST ENTOMOLOGY, Issue 3 2010Henri E. Z. Tonnang 1An integrated pest management (IPM) system incorporating the introduction and field release of Diadegma semiclausum (Hellén), a parasitoid of diamondback moth (DBM) Plutella xylostella (L.), comprising the worst insect pest of the cabbage family, has been developed in Kenya to replace the pesticides-only approach. 2Mathematical modelling using differential equations has been used in theoretical studies of host,parasitoid systems. Although, this method helps in gaining an understanding of the system's dynamics, it is generally less accurate when used for prediction. The artificial neural network (ANN) approach was therefore chosen to aid prediction. 3The ANN methodology was applied to predict the population density of the DBM and D. semiclausum, its larval parasitoid. Two data sets, each from different release areas in the Kenya highlands, and both collected during a 3-year period after the release of the parasitoid, were used in the present study. Two ANN models were developed using these data. 4The ANN approach gave satisfactory results for DBM and for D. semiclausum. Sensitivity analysis suggested that pest populations may be naturally controlled by rainfall. 5The ANN provides a powerful tool for predicting host,parasitoid population densities and made few assumptions on the field data. The approach allowed the use of data collected at any appropriate scale of the system, bypassing the assumptions and uncertainties that could have occurred when parameters are imported from other systems. The methodology can be explored with respect to the development of tools for monitoring and forecasting the population densities of a pest and its natural enemies. In addition, the model can be used to evaluate the relative effectiveness of the natural enemies and to investigate augmentative biological control strategies. [source] Unemployment variation over the business cycles: a comparison of forecasting modelsJOURNAL OF FORECASTING, Issue 7 2004Saeed Moshiri Abstract Asymmetry has been well documented in the business cycle literature. The asymmetric business cycle suggests that major macroeconomic series, such as a country's unemployment rate, are non-linear and, therefore, the use of linear models to explain their behaviour and forecast their future values may not be appropriate. Many researchers have focused on providing evidence for the non-linearity in the unemployment series. Only recently have there been some developments in applying non-linear models to estimate and forecast unemployment rates. A major concern of non-linear modelling is the model specification problem; it is very hard to test all possible non-linear specifications, and to select the most appropriate specification for a particular model. Artificial neural network (ANN) models provide a solution to the difficulty of forecasting unemployment over the asymmetric business cycle. ANN models are non-linear, do not rely upon the classical regression assumptions, are capable of learning the structure of all kinds of patterns in a data set with a specified degree of accuracy, and can then use this structure to forecast future values of the data. In this paper, we apply two ANN models, a back-propagation model and a generalized regression neural network model to estimate and forecast post-war aggregate unemployment rates in the USA, Canada, UK, France and Japan. We compare the out-of-sample forecast results obtained by the ANN models with those obtained by several linear and non-linear times series models currently used in the literature. It is shown that the artificial neural network models are able to forecast the unemployment series as well as, and in some cases better than, the other univariate econometrics time series models in our test. Copyright © 2004 John Wiley & Sons, Ltd. [source] Choosing among competing econometric forecasts: Regression-based forecast combination using model selectionJOURNAL OF FORECASTING, Issue 6 2001Norman R. Swanson Abstract Forecast combination based on a model selection approach is discussed and evaluated. In addition, a combination approach based on ex ante predictive ability is outlined. The model selection approach which we examine is based on the use of Schwarz (SIC) or the Akaike (AIC) Information Criteria. Monte Carlo experiments based on combination forecasts constructed using possibly (misspecified) models suggest that the SIC offers a potentially useful combination approach, and that further investigation is warranted. For example, combination forecasts from a simple averaging approach MSE-dominate SIC combination forecasts less than 25% of the time in most cases, while other ,standard' combination approaches fare even worse. Alternative combination approaches are also compared by conducting forecasting experiments using nine US macroeconomic variables. In particular, artificial neural networks (ANN), linear models, and professional forecasts are used to form real-time forecasts of the variables, and it is shown via a series of experiments that SIC, t -statistic, and averaging combination approaches dominate various other combination approaches. An additional finding is that while ANN models may not MSE-dominate simpler linear models, combinations of forecasts from these two models outperform either individual forecast, for a subset of the economic variables examined. Copyright © 2001 John Wiley & Sons, Ltd. [source] COMPARISON OF PROCESS-BASED AND ARTIFICIAL NEURAL NETWORK APPROACHES FOR STREAMFLOW MODELING IN AN AGRICULTURAL WATERSHED,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 3 2006Puneet Srivastava ABSTRACT: The performance of the Soil and Water Assessment Tool (SWAT) and artificial neural network (ANN) models in simulating hydrologic response was assessed in an agricultural watershed in southeastern Pennsylvania. All of the performance evaluation measures including Nash-Sutcliffe coefficient of efficiency (E) and coefficient of determination (R2) suggest that the ANN monthly predictions were closer to the observed flows than the monthly predictions from the SWAT model. More specifically, monthly streamflow E and R2 were 0.54 and 0.57, respectively, for the SWAT model calibration period, and 0.71 and 0.75, respectively, for the ANN model training period. For the validation period, these values were ,0.17 and 0.34 for the SWAT and 0.43 and 0.45 for the ANN model. SWAT model performance was affected by snowmelt events during winter months and by the model's inability to adequately simulate base flows. Even though this and other studies using ANN models suggest that these models provide a viable alternative approach for hydrologic and water quality modeling, ANN models in their current form are not spatially distributed watershed modeling systems. However, considering the promising performance of the simple ANN model, this study suggests that the ANN approach warrants further development to explicitly address the spatial distribution of hydrologic/water quality processes within watersheds. [source] APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING,JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION, Issue 2 2006Moon Seong Rang ABSTRACT: The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall-runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km2 watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth-order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea. [source] AN ADAPTIVE LEARNING FRAMEWORK FOR FORECASTING SEASONAL WATER ALLOCATIONS IN IRRIGATED CATCHMENTSNATURAL RESOURCE MODELING, Issue 3 2010SHAHBAZ KHAN Abstract This paper describes an adaptive learning framework for forecasting end-season water allocations using climate forecasts, historic allocation data, and results of other detailed hydrological models. The adaptive learning framework is based on artificial neural network (ANN) method, which can be trained using past data to predict future water allocations. Using this technique, it was possible to develop forecast models for end-irrigation-season water allocations from allocation data available from 1891 to 2005 based on the allocation level at the start of the irrigation season. The model forecasting skill was further improved by the incorporation of a set of correlating clusters of sea surface temperature (SST) and the Southern oscillation index (SOI) data. A key feature of the model is to include a risk factor for the end-season water allocations based on the start of the season water allocation. The interactive ANN model works in a risk-management context by providing probability of availability of water for allocation for the prediction month using historic data and/or with the incorporation of SST/SOI information from the previous months. All four developed ANN models (historic data only, SST incorporated, SOI incorporated, SST-SOI incorporated) demonstrated ANN capability of forecasting end-of-season water allocation provided sufficient data on historic allocation are available. SOI incorporated ANN model was the most promising forecasting tool that showed good performance during the field testing of the model. [source] Weather-based prediction of anthracnose severity using artificial neural network modelsPLANT PATHOLOGY, Issue 4 2004S. Chakraborty Data were collected and analysed from seven field sites in Australia, Brazil and Colombia on weather conditions and the severity of anthracnose disease of the tropical pasture legume Stylosanthes scabra caused by Colletotrichum gloeosporioides. Disease severity and weather data were analysed using artificial neural network (ANN) models developed using data from some or all field sites in Australia and/or South America to predict severity at other sites. Three series of models were developed using different weather summaries. Of these, ANN models with weather for the day of disease assessment and the previous 24 h period had the highest prediction success, and models trained on data from all sites within one continent correctly predicted disease severity in the other continent on more than 75% of days; the overall prediction error was 21·9% for the Australian and 22·1% for the South American model. Of the six cross-continent ANN models trained on pooled data for five sites from two continents to predict severity for the remaining sixth site, the model developed without data from Planaltina in Brazil was the most accurate, with >85% prediction success, and the model without Carimagua in Colombia was the least accurate, with only 54% success. In common with multiple regression models, moisture-related variables such as rain, leaf surface wetness and variables that influence moisture availability such as radiation and wind on the day of disease severity assessment or the day before assessment were the most important weather variables in all ANN models. A set of weights from the ANN models was used to calculate the overall risk of anthracnose for the various sites. Sites with high and low anthracnose risk are present in both continents, and weather conditions at centres of diversity in Brazil and Colombia do not appear to be more conducive than conditions in Australia to serious anthracnose development. [source] An artificial neural network for five different assay systems of prostate-specific antigen in prostate cancer diagnosticsBJU INTERNATIONAL, Issue 7 2008Carsten Stephan OBJECTIVE To compare separate prostate-specific antigen (PSA) assay-specific artificial neural networks (ANN) for discrimination between patients with prostate cancer (PCa) and no evidence of malignancy (NEM). PATIENTS AND METHODS In 780 patients (455 with PCa, 325 with NEM) we measured total PSA (tPSA) and free PSA (fPSA) with five different assays: from Abbott (AxSYM), Beckman Coulter (Access), DPC (Immulite 2000), and Roche (Elecsys 2010) and with tPSA and complexed PSA (cPSA) assays from Bayer (ADVIA Centaur). ANN models were developed with five input factors: tPSA, percentage free/total PSA (%fPSA), age, prostate volume and digital rectal examination status for each assay separately to examine two tPSA ranges of 0,10 and 10,27 ng/mL. RESULTS Compared with the median tPSA concentrations (range from 4.9 [Bayer] to 6.11 ng/mL [DPC]) and especially the median %fPSA values (range from 11.2 [DPC] to 17.4%[Abbott], for tPSA 0,10 ng/mL), the areas under the receiver operating characteristic curves (AUC) for all calculated ANN models did not significantly differ from each other. The AUC were: 0.894 (Abbott), 0.89 (Bayer), 0.895 (Beckman), 0.882 (DPC) and 0.892 (Roche). At 95% sensitivity the specificities were without significant differences, whereas the individual absolute ANN outputs differed markedly. CONCLUSIONS Despite only slight differences, PSA assay-specific ANN models should be used to optimize the ANN outcome to reduce the number of unnecessary prostate biopsies. We further developed the ANN named ,ProstataClass' to provide clinicians with an easy to use tool in making their decision about follow-up testing. [source] | ||||||||||||||||