Network Models (network + models)

Distribution by Scientific Domains

Kinds of Network Models

  • artificial neural network models
  • neural network models

  • Selected Abstracts

    Dynamic On-Line Reoptimization Control of a Batch MMA Polymerization Reactor Using Hybrid Neural Network Models

    Y. Tian
    Abstract A hybrid neural network model based on-line reoptimization control strategy is developed for a batch polymerization reactor. To address the difficulties in batch polymerization reactor modeling, the hybrid neural network model contains a simplified mechanistic model covering material balance assuming perfect temperature control, and recurrent neural networks modeling the residuals of the simplified mechanistic model due to imperfect temperature control. This hybrid neural network model is used to calculate the optimal control policy. A difficulty in the optimal control of batch polymerization reactors is that the optimization effort can be seriously hampered by unknown disturbances such as reactive impurities and reactor fouling. With the presence of an unknown amount of reactive impurities, the off-line calculated optimal control profile will be no longer optimal. To address this issue, a strategy combining on-line reactive impurity estimation and on-line reoptimization is proposed in this paper. The amount of reactive impurities is estimated on-line during the early stage of a batch by using a neural network based inverse model. Based on the estimated amount of reactive impurities, on-line reoptimization is then applied to calculate the optimal reactor temperature profile for the remaining time period of the batch reactor operation. This approach is illustrated on the optimization control of a simulated batch methyl methacrylate polymerization process. [source]

    Short-term travel speed prediction models in car navigation systems

    Seungjae Lee
    The objective of this study is the development of the short-term prediction models to predict average spot speeds of the subject location in the short-term periods of 5, 10 and 15 minutes respectively. In this study, field data were used to see the comparison of the predictability of Regression Analysis, ARIMA, Kalman Filtering and Neural Network models. These field data were collected from image processing detectors at the urban expressway for 17 hours including both peak and non-peak hours. Most of the results were reliable, but the results of models using Kalman Filtering and Neural Networks are more accurate and realistic than those of the others. [source]

    Predicting phenotypic effects of gene perturbations in C. elegans using an integrated network model

    BIOESSAYS, Issue 8 2008
    Karsten Borgwardt
    Predicting the phenotype of an organism from its genotype is a central question in genetics. Most importantly, we would like to find out if the perturbation of a single gene may be the cause of a disease. However, our current ability to predict the phenotypic effects of perturbations of individual genes is limited. Network models of genes are one tool for tackling this problem. In a recent study, (Lee et al.) it has been shown that network models covering the majority of genes of an organism can be used for accurately predicting phenotypic effects of gene perturbations in multicellular organisms. BioEssays 30:707,710, 2008. © 2008 Wiley Periodicals, Inc. [source]

    Network models for capillary porous media: application to drying technology

    T. Metzger Jun.-Prof.
    Abstract Network models offer an efficient pore-scale approach to investigate transport in partially saturated porous materials and are particularly suited to study capillarity. Drying is a prime model application since it involves a range of physical effects: capillary pumping, viscous liquid flow, phase transition, vapor diffusion, heat transfer, but also cracks and shrinkage. This review article gives an introduction to this modern technique addressing required model input, sketching important elements of the computational algorithm and commenting on the nature of simulation results. For the case of drying, it is illustrated how network models can help analyze the influence of pore structure on process kinetics and gain a deeper understanding of the role of individual transport phenomena. Finally, a combination of pore network model and discrete element method is presented, extending the application range to mechanical effects caused by capillary forces. [source]

    Model Reduction in Emulsion Polymerization Using Hybrid First Principles/Artificial Neural Networks Models, 2,

    Gurutze Arzamendi
    Abstract Summary: A "series" hybrid model based on material balances and artificial neural networks to predict the evolution of weight average molecular weight, , in semicontinuous emulsion polymerization with long chain branching kinetics is presented. The core of the model is composed by two artificial neural networks (ANNs) that calculate polymerization rate, Rp, and instantaneous weight-average molecular weight, from reactor process variables. The subsequent integration of the material balances allowed to obtain the time evolution of conversion and , along the polymerization process. The accuracy of the proposed model under a wide range of conditions was assessed. The low computer-time load makes the hybrid model suitable for optimization strategies. Effect of the monomer feed rate on . [source]

    Neural Network Earnings per Share Forecasting Models: A Comparative Analysis of Alternative Methods

    DECISION SCIENCES, Issue 2 2004
    Wei Zhang
    ABSTRACT In this paper, we present a comparative analysis of the forecasting accuracy of univariate and multivariate linear models that incorporate fundamental accounting variables (i.e., inventory, accounts receivable, and so on) with the forecast accuracy of neural network models. Unique to this study is the focus of our comparison on the multivariate models to examine whether the neural network models incorporating the fundamental accounting variables can generate more accurate forecasts of future earnings than the models assuming a linear combination of these same variables. We investigate four types of models: univariate-linear, multivariate-linear, univariate-neural network, and multivariate-neural network using a sample of 283 firms spanning 41 industries. This study shows that the application of the neural network approach incorporating fundamental accounting variables results in forecasts that are more accurate than linear forecasting models. The results also reveal limitations of the forecasting capacity of investors in the security market when compared to neural network models. [source]

    An immunity network with provision for diverse recognition in T-cell receptors

    Takayuki Yamaguchi
    Abstract Recently, biological superior information processing ability has been researched to be able to be useful for a computer system. In particular, neural networks for the brain and nervous system have been researched. Immunity network models based on the biological immune response network also have been studied, but most of the researchers consider only a basic part of the interaction between B cells and T cells. Recent research has reported that the function of T cell receptors (TCR) is important in the practical immune response network. Therefore, we propose a new immunity network model which, unlike the traditional immunity networks, has the same function as that of a TCR. We applied the proposed immunity network model and the traditional immunity network model to the pattern recognition system. And we compared the performance of the proposed immunity network model with the traditional immunity network model, and showed the usability of the proposed immunity network model. © 2009 Wiley Periodicals, Inc. Electron Comm Jpn, 92(10): 41,48, 2009; Published online in Wiley InterScience ( DOI 10.1002/ecj.10040 [source]

    Prediction of biodegradation from the atom-type electrotopological state indices

    Jarmo Huuskonen
    Abstract A group contribution method based on atom-type electrotopological state indices for predicting the biodegradation of a diverse set of 241 organic chemicals is presented. Multiple linear regression and artificial neural networks were used to build the models using a training set of 172 compounds, for which the approximate time for ultimate biodegradation was estimated from the results of a survey of an expert panel. Derived models were validated by using a leave-25%-out method and against two test sets of 12 and 57 chemicals not included in the training set. The squared correlation coefficient (r2) for a linear model with 15 structural parameters was 0.76 for the training set and 0.68 for the test set of 12 molecules. The model predicted correctly the biodegradation of 48 chemicals in the test set of 57 molecules, for which biodegradability was presented as rapid or slow. The use of artificial neural networks gave better prediction for both test sets when the same set of parameters was tested as inputs in neural network simulations. The predictions of rapidly biodegradable chemicals were more accurate than the predictions of slowly bio-degradable chemicals for both the regression and neural network models. [source]

    Stepwise geographical traceability of virgin olive oils by chemical profiles using artificial neural network models

    Diego 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]

    Neural network models to predict cation exchange capacity in arid regions of Iran

    M. Amini
    Summary Design and analysis of land-use management scenarios requires detailed soil data. When such data are needed on a large scale, pedotransfer functions (PTFs) could be used to estimate different soil properties. Because existing regression-based PTFs for estimating cation exchange capacity (CEC) do not, in general, apply well to arid areas, this study was conducted (i) to evaluate the existing models and (ii) to develop neural network-based PTFs for predicting CEC in Aridisols of Isfahan in central Iran. As most researches have found a significant correlation between CEC and soil organic matter content (OM) and clay content, we also used these two variables for modelling of CEC. We tested several published PTFs and developed two neural network algorithms using multilayer perceptron and general regression neural networks based on a set of 170 soil samples. The data set was divided into two subsets for calibration and testing of the models. In general, the neural network-based models provided more reliable predictions than the regression-based PTFs. [source]

    Dimensioning of data networks: a flow-level perspective

    Pasi Lassila
    Traditional network dimensioning formulations have applied the Erlang model where the connections reserve capacity in the network. Until recently, tractable stochastic network models where the connections share the capacity in the network did not exist. The latter are becoming increasingly important as they can be applied to characterise file transfers in current data networks (e.g. IP networks). In particular, they can be utilised for dimensioning of networks with respect to the file transfer performance. To this end, we consider a model where the traffic consists of elastic flows (i.e. file transfers). Flows arrive randomly and share the network resources resulting in stochastically varying transmission rates for flows. Our contribution is to develop efficient methods for capacity planning to meet the performance requirements expressed in terms of the average transmission rate of flows on a given route, i.e. the per-flow throughput. These methods are validated using ns2 simulations. We discuss also the effects of access rate limitations and how to combine the elastic traffic requirements with those of real-time traffic. Finally, we outline how the methods can be applied in wireless mesh networks. Our results enable a simple characterisation of the order-of-magnitude of the required capacities, which can be utilised as a first step in practical network planning and dimensioning. Copyright © 2008 John Wiley & Sons, Ltd. [source]

    Modified mixture of experts employing eigenvector methods and Lyapunov exponents for analysis of electroencephalogram signals

    EXPERT SYSTEMS, Issue 4 2009
    Elif Derya ÜbeyliArticle first published online: 2 SEP 200
    Abstract: The use of diverse features in detecting variability of electroencephalogram (EEG) signals is presented. The classification accuracies of the modified mixture of experts (MME), which was trained on diverse features, were obtained. Eigenvector methods (Pisarenko, multiple signal classification , MUSIC, and minimum-norm) were selected to generate the power spectral density estimates. The features from the power spectral density estimates and Lyapunov exponents of the EEG signals were computed and statistical features were calculated to depict their distribution. The statistical features, which were used for obtaining the diverse features of the EEG signals, were then input into the implemented neural network models for training and testing purposes. The present study demonstrated that the MME trained on the diverse features achieved high accuracy rates (total classification accuracy of the MME is 98.33%). [source]

    Neural network ensembles: combining multiple models for enhanced performance using a multistage approach

    EXPERT SYSTEMS, Issue 5 2004
    Shuang Yang
    Abstract: Neural network ensembles (sometimes referred to as committees or classifier ensembles) are effective techniques to improve the generalization of a neural network system. Combining a set of neural network classifiers whose error distributions are diverse can generate better results than any single classifier. In this paper, some methods for creating ensembles are reviewed, including the following approaches: methods of selecting diverse training data from the original source data set, constructing different neural network models, selecting ensemble nets from ensemble candidates and combining ensemble members' results. In addition, new results on ensemble combination methods are reported. [source]

    Short-term electric power load forecasting using feedforward neural networks

    EXPERT SYSTEMS, Issue 3 2004
    Heidar A. Malki
    Abstract: This paper presents the results of a study on short-term electric power load forecasting based on feedforward neural networks. The study investigates the design components that are critical in power load forecasting, which include the selection of the inputs and outputs from the data, the formation of the training and the testing sets, and the performance of the neural network models trained to forecast power load for the next hour and the next day. The experiments are used to identify the combination of the most significant parameters that can be used to form the inputs of the neural networks in order to reduce the prediction error. The prediction error is also reduced by predicting the difference between the power load of the next hour (day) and that of the present hour (day). This is a promising alternative to the commonly used approach of predicting the actual power load. The potential of the proposed method is revealed by its comparison with two existing approaches that utilize neural networks for electric power load forecasting. [source]

    Grid cells: The position code, neural network models of activity, and the problem of learning

    HIPPOCAMPUS, Issue 12 2008
    Peter E. Welinder
    Abstract We review progress on the modeling and theoretical fronts in the quest to unravel the computational properties of the grid cell code and to explain the mechanisms underlying grid cell dynamics. The goals of the review are to outline a coherent framework for understanding the dynamics of grid cells and their representation of space; to critically present and draw contrasts between recurrent network models of grid cells based on continuous attractor dynamics and independent-neuron models based on temporal interference; and to suggest open questions for experiment and theory. © 2008 Wiley-Liss, Inc. [source]

    Computational Modeling of Statistical Learning: Effects of Transitional Probability Versus Frequency and Links to Word Learning

    INFANCY, Issue 5 2010
    Daniel Mirman
    Statistical learning mechanisms play an important role in theories of language acquisition and processing. Recurrent neural network models have provided important insights into how these mechanisms might operate. We examined whether such networks capture two key findings in human statistical learning. In Simulation 1, a simple recurrent network (SRN) performed much like human learners: it was sensitive to both transitional probability and frequency, with frequency dominating early in learning and probability emerging as the dominant cue later in learning. In Simulation 2, an SRN captured links between statistical segmentation and word learning in infants and adults, and suggested that these links arise because phonological representations are more distinctive for syllables with higher transitional probability. Beyond simply simulating general phenomena, these models provide new insights into underlying mechanisms and generate novel behavioral predictions. [source]

    Optimizing object classification under ambiguity/ignorance: application to the credit rating problem

    Malcolm J. Beynon
    A nascent technique for object classification is employed to exposit the classification of US banks to their financial strength ratings, presented by the Moody's Investors Services. The classification technique primarily utilized, called CaRBS (classification and ranking belief simplex), allows for the presence of ignorance to be inherent. The modern constrained optimization method, trigonometric differential evolution (TDE), is adopted to configure a CaRBS system. Two different objective functions are considered with TDE to measure the level of optimization achieved, which utilize differently the need to reduce ambiguity and/or ignorance inherently during the optimization process. The appropriateness of the CaRBS system to analyse incomplete data is also highlighted, with no requirement to impute any missing values or remove objects with missing values inherent. Comparative results are also presented using the well-known multivariate discriminant analysis and neural network models. The findings in this study identify a novel dimension to the issue of object classification optimization, with the discernment between the concomitant notions of ambiguity and ignorance. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Off-site monitoring systems for predicting bank underperformance: a comparison of neural networks, discriminant analysis, and professional human judgment

    Philip Swicegood
    This study compares the ability of discriminant analysis, neural networks, and professional human judgment methodologies in predicting commercial bank underperformance. Experience from the banking crisis of the 1980s and early 1990s suggest that improved prediction models are needed for helping prevent bank failures and promoting economic stability. Our research seeks to address this issue by exploring new prediction model techniques and comparing them to existing approaches. When comparing the predictive ability of all three models, the neural network model shows slightly better predictive ability than that of the regulators. Both the neural network model and regulators significantly outperform the benchmark discriminant analysis model's accuracy. These findings suggest that neural networks show promise as an off-site surveillance methodology. Factoring in the relative costs of the different types of misclassifications from each model also indicates that neural network models are better predictors, particularly when weighting Type I errors more heavily. Further research with neural networks in this field should yield workable models that greatly enhance the ability of regulators and bankers to identify and address weaknesses in banks before they approach failure. Copyright © 2001 John Wiley & Sons, Ltd. [source]

    Clustering with artificial neural networks and traditional techniques

    G. Tambouratzis
    In this article, two clustering techniques based on neural networks are introduced. The two neural network models are the Harmony theory network (HTN) and the self-organizing logic neural network (SOLNN), both of which are characterized by parallel processing, a distributed architecture, and a large number of nodes. After describing their clustering characteristics and potential, a comparison to classical statistical techniques is performed. This comparison allows the creation of a correspondence between each neural network clustering technique and particular metrics as used by the corresponding statistical methods, which reflect the affinity of the clustered patterns. In particular, the HTN is found to perform the clustering task with an accuracy similar to the best statistical methods, while it is further capable of proposing an optimal number of groups into which the patterns may be clustered. On the other hand, the SOLNN combines a high clustering accuracy with the ability to cluster higher-dimensional patterns without a considerable increase in the processing time. © 2003 Wiley Periodicals, Inc. [source]

    Modeling power and intermodulation behavior of microwave transistors with unified small-signal/large-signal neural network models

    F. Giannini
    Abstract This article presents a detailed procedure to learn a nonlinear model and its derivatives to as many orders as desired with multilayer perceptron (MLP) neural networks. A modular neural network modeling a nonlinear function and its derivatives is introduced. The method has been used for the extraction of the large-signal model of a power MESFET device, modeling the nonlinear relationship of drain-source current Ids as well as gate and drain charge Qg and Qd with respect to intrinsic voltages Vgs and Vds over the whole operational bias region. The neural models have been implemented into a user-defined nonlinear model of a commercial microwave simulator to predict output power performance as well as intermodulation distortion. The accuracy of the device model is verified by harmonic load-pull measurements. This neural network approach has demonstrated to predict nonlinear behavior with enough accuracy even if based only on first-order derivative information. © 2003 Wiley Periodicals, Inc. Int J RF and Microwave CAE 13: 276,284, 2003. [source]

    Parallel Algorithms for Dynamic Shortest Path Problems

    Ismail Chabini
    The development of intelligent transportation systems (ITS) and the resulting need for the solution of a variety of dynamic traffic network models and management problems require faster-than-real-time computation of shortest path problems in dynamic networks. Recently, a sequential algorithm was developed to compute shortest paths in discrete time dynamic networks from all nodes and all departure times to one destination node. The algorithm is known as algorithm DOT and has an optimal worst-case running-time complexity. This implies that no algorithm with a better worst-case computational complexity can be discovered. Consequently, in order to derive algorithms to solve all-to-one shortest path problems in dynamic networks, one would need to explore avenues other than the design of sequential solution algorithms only. The use of commercially-available high-performance computing platforms to develop parallel implementations of sequential algorithms is an example of such avenue. This paper reports on the design, implementation, and computational testing of parallel dynamic shortest path algorithms. We develop two shared-memory and two message-passing dynamic shortest path algorithm implementations, which are derived from algorithm DOT using the following parallelization strategies: decomposition by destination and decomposition by transportation network topology. The algorithms are coded using two types of parallel computing environments: a message-passing environment based on the parallel virtual machine (PVM) library and a multi-threading environment based on the SUN Microsystems Multi-Threads (MT) library. We also develop a time-based parallel version of algorithm DOT for the case of minimum time paths in FIFO networks, and a theoretical parallelization of algorithm DOT on an ,ideal' theoretical parallel machine. Performances of the implementations are analyzed and evaluated using large transportation networks, and two types of parallel computing platforms: a distributed network of Unix workstations and a SUN shared-memory machine containing eight processors. Satisfactory speed-ups in the running time of sequential algorithms are achieved, in particular for shared-memory machines. Numerical results indicate that shared-memory computers constitute the most appropriate type of parallel computing platforms for the computation of dynamic shortest paths for real-time ITS applications. [source]


    C. 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]

    A Review on Residence Time Distribution (RTD) in Food Extruders and Study on the Potential of Neural Networks in RTD Modeling

    G. Ganjyal
    ABSTRACT: Residence time distribution and mean residence time depend on process variables, namely feed rate, screw speed, feed moisture content, barrel temperature, die temperature and die diameter. Flow in an extruder has been modeled by simulating residence time distribution, assuming the extruder to be a series of continuous-stirred-tank or plug-flow reactors. Others have developed relationships for mean residence time as functions of process variables. Better models can be developed using neural networks. As an example, data from the literature were used to model mean residence time as a function of process variables using statistical regression and neural networks. Neural network models performed better than regression models. [source]

    Building neural network models for time series: a statistical approach

    Marcelo C. Medeiros
    Abstract This paper is concerned with modelling time series by single hidden layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using simple existing techniques. The problem of selecting the number of hidden units is solved by sequentially applying Lagrange multiplier type tests, with the aim of avoiding the estimation of unidentified models. Misspecification tests are derived for evaluating an estimated neural network model. All the tests are entirely based on auxiliary regressions and are easily implemented. A small-sample simulation experiment is carried out to show how the proposed modelling strategy works and how the misspecification tests behave in small samples. Two applications to real time series, one univariate and the other multivariate, are considered as well. Sets of one-step-ahead forecasts are constructed and forecast accuracy is compared with that of other nonlinear models applied to the same series. Copyright © 2006 John Wiley & Sons, Ltd. [source]

    Unemployment variation over the business cycles: a comparison of forecasting models

    Saeed 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]

    Predicting LDC debt rescheduling: performance evaluation of OLS, logit, and neural network models

    Douglas K. Barney
    Abstract Empirical studies in the area of sovereign debt have used statistical models singularly to predict the probability of debt rescheduling. Unfortunately, researchers have made few efforts to test the reliability of these model predictions or to identify a superior prediction model among competing models. This paper tested neural network, OLS, and logit models' predictive abilities regarding debt rescheduling of less developed countries (LDC). All models predicted well out-of-sample. The results demonstrated a consistent performance of all models, indicating that researchers and practitioners can rely on neural networks or on the traditional statistical models to give useful predictions. Copyright © 2001 John Wiley & Sons, Ltd. [source]


    ABSTRACT In this paper, two artificial neural networks (ANNs) are applied to acquire the relationship between the mechanical properties and moisture content of cumin seed, using the data of quasi-static loading test. In establishing these relationship, the moisture content, seed size, loading rate and seed orientation were taken as the inputs of both models. The force and energy required for fracturing of cumin seed, under quasi-static loading were taken as the outputs of two models. The activation function in the output layer of models obeyed a linear output, whereas the activation function in the hidden layers were in the form of a sigmoid function. Adjusting ANN parameters such as learning rate and number of neurons and hidden layers affected the accuracy of force and energy prediction. Comparison of the predicted and experimented data showed that the ANN models used to predict the relationships of mechanical properties of cumin seed have a good learning precision and good generalization, because the root mean square errors of the predicated data by ANNs were rather low (4.6 and 7.7% for the force and energy, respectively). PRACTICAL APPLICATIONS Cumin seed is generally used as a food additive in the form of powder for imparting flavor to different food preparations and for a variety of medicinal properties. Physical properties of cumin seeds are essential for the design of equipment for handling, harvesting, aeration, drying, storing, grinding and processing. For powder preparation especially the fracture behavior of the seeds are essential. These properties are affected by numerous factors such as size, form and moisture content of the grain and deformation speed. A neural network model was developed that can be used to predict the relationships of mechanical properties. Artificial neural network models are powerful empirical models approach, which can be compared with mathematical models. [source]

    Confucian Capitalism and the Paradox of Closure and Structural Holes in East Asian Firms

    Sun-Ki Chai
    abstract A long-standing debate has taken place in the organizational sociology and social network literatures about the relative advantages of network closure versus structural holes in the generation of social capital. There is recent evidence that these advantages differ across cultures and between East Asia and the West in particular, but existing network models are unable to explain why or address cultural variation in general. This paper seeks to provide a solution by integrating a culture-embedded rational model of action into the social network model of structure, using this not only to re-examine the closure versus structural hole debate, but also to tie it to the literature on Confucian capitalism and the ,East Asian Model' of the firm. We argue that this integrated approach allows us to systematically analyse the relationship between culture and behaviour in networks and, more specifically, to explain why closure has been a more powerful source of productivity in East Asia than the West. [source]

    Patterning by genetic networks

    S. Genieys
    Abstract We consider here the morphogenesis (pattern formation) problem for some genetic network models. First, we show that any given spatio-temporal pattern can be generated by a genetic network involving a sufficiently large number of genes. Moreover, patterning process can be performed by an effective algorithm. We also show that Turing's or Meinhardt's type reaction,diffusion models can be approximated by genetic networks. These results exploit the fundamental fact that the genes form functional units and are organized in blocks. Due to this modular organization, the genes always are capable to construct any new patterns and even any time sequences of new patterns from old patterns. Computer simulations illustrate some analytical results. Copyright © 2005 John Wiley & Sons, Ltd. [source]

    Computational Network Model Prediction of Hemodynamic Alterations Due to Arteriolar Remodeling in Interval Sprint Trained Skeletal Muscle

    MICROCIRCULATION, Issue 3 2007
    Kyle W. Binder
    ABSTRACT Objectives: Exercise training is known to enhance skeletal muscle blood flow capacity, with high-intensity interval sprint training (IST) primarily affecting muscles with a high proportion of fast twitch glycolytic fibers. The objective of this study was to determine the relative contributions of new arteriole formation and lumenal arteriolar remodeling to enhanced flow capacity and the impact of these adaptations on local microvascular hemodynamics deep within the muscle. Methods: The authors studied arteriolar adaptation in the white/mixed-fiber portion of gastrocnemius muscles of IST (6 bouts of running/day; 2.5 min/bout; 60 m/min speed; 15% grade; 4.5 min rest between bouts; 5 training days/wk; 10 wks total) and sedentary (SED) control rats using whole-muscle Microfil casts. Dimensional and topological data were then used to construct a series of computational hemodynamic network models that incorporated physiological red blood cell distributions and hematocrit and diameter dependent apparent viscosities. Results: In comparison to SED controls, IST elicited a significant increase in arterioles/order in the 3A through 6A generations. Predicted IST and SED flows through the 2A generation agreed closely with in vivo measurements made in a previous study, illustrating the accuracy of the model. IST shifted the bulk of the pressure drop across the network from the 3As to the 4As and 5As, and flow capacity increased from 0.7 mL/min in SED to 1.5 mL/min in IST when a driving pressure of 80 mmHg was applied. Conclusions: The primary adaptation to IST is an increase in arterioles in the 3A through 6A generations, which, in turn, creates an approximate doubling of flow capacity and a deeper penetration of high pressure into the arteriolar network. [source]