nonlinear neural network models
Recurrent Neural Network Identification: Comparative Study on Nonlinear Process
... ABSTRACT— Neural networks (NNs) have been successfully applied to solve a variety of application problems including nonlinear modelling and ...recurrent neural networks. The most powerful types of ...
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Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models
... STAR-GARCH models allow STAR type nonlinearity in the conditional mean with GARCH type heteroscedasticity in the conditional variance, where, the GARCH process is a single regime ...STAR-GARCH models and ...
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Comparison of the Accuracy of Nonlinear Models and Artificial Neural Network in the Performance Prediction of Ross 308 Broiler Chickens
... Growth can be defined as a biological process resulting from changes in the body weight of an animal and has an economic significance in livestock breeding. Some researchers are interested in analyzing the relationship ...
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Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange
... structural models, despite their relative success in forecasting the variables, have not produced desired results, according to researcher, because these methods generally rely on information obtained from ...
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Comparative study of static and dynamic neural network models for nonlinear time series forecasting
... structural models, despite their relative success in forecasting the variables, have not produced desired results, according to researcher, because these methods generally rely on information obtained from ...
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Comparative Study of Various Neural Network Models for Software Quality Estimation
... quality models. These quality models are often based on statistical relationships between measure of quality and measure of software ...and nonlinear which resulting in limiting the accuracy of the ...
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Browse Title Index
... of models, the Artificial Neural Network Model (ANN) was identified to be able to address the problems of the Hedonic Model, such as nonlinearity and multicollinearity (Tabales, Ocerin, & ...
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Prediction of Petroleum Price Using Back Propagation Artificial Neural Network Based on Chaotic Self-Adaptive Particle Swarm Algorithm
... and nonlinear factors, how to predict the price effectively is the focus of the present ...artificial neural network model based on particle swarm optimization algorithm combined with chaos theory ...
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A comparative study for back propagation neural network and nonlinear regression models for predicting dengue outbreak
... This study aims to design a Neural Network Model (NNM) and Nonlinear Regression Model (NLRM) using different architectures and parameters incorporating time series,[r] ...
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Performance Analyses of Recurrent Neural Network Models Exploited for Online Time-Varying Nonlinear Optimization
... In implementations of an RNN model, we usually assume that it is free of all kinds of noises or external errors [9]. However, there always exist some realization errors in hard- ware implementations or disturbances in ...
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Height-diameter relationship for Pinus koraiensis in Mengjiagang Forest Farm of Northeast China using nonlinear regressions and artificial neural network models
... six nonlinear regression models (Table 3), we found that the coefficient estimates varied among density ...Among nonlinear models, the Power function showed a statistical significance in all ...
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Nonlinear Models of Neural and Genetic Network Dynamics: Natural Transformations of Łukasiewicz Logic LM-Algebras in a Łukasiewicz-Topos as Representations of Neural Network Development and Neoplastic Transformations
... genetic network, without considering the 'clustering' of genes as a necessary condition for all biological ...the neural nets might be considered to be dynamically analogous to the corresponding genetic ...
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Verification of an Evolutionary-based Wavelet Neural Network Model for Nonlinear Function Approximation
... the network, respectively, and N is the total number of ...the models the wavelet neural network has better performance in nonlinear function modeling compared with the feed forward ...
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AN EFFICIENT SUPER PEER SELECTION ALGORITHM FOR PEER TO PEER (P2P) LIVE STREAMING NETWORK
... the Neural Network times series in MATLAB toolbox, number of neurons and number of delay considered the important variables to generate the nonlinear model for NARX and NAR models after ...
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Bias correction of ANN based statistically downscaled precipitation data for the Chaliyar river basin
... Circulation Models (GCM) are the only reliable source for future climate scenarios, but they perform well only at coarse ...hydrologic models applied at river basin ...advanced nonlinear bias ...
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Gyroscope Random Drift Modeling, using Neural Networks, Fuzzy Neural and Traditional Time- series Methods
... and nonlinear methods. Based on the theories of Neural- Network, Fuzzy logic and Time series methods, actual test data about random drift of a DTG has been processed, and the random drift is ...
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Convolutional Neural Network Language Models
... addition of convolutional layers clearly improves it even further. Concretely, we observe a solid 11-26% reduction of perplexity compared to the feed-forward network after using MLP Convolution, depending on the ...
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Structural combination of neural network models
... A preliminary analysis suggested that an NN architecture with 16 inputs (corresponding to all lags considered) and 2 neurons would be the best choice. Such architecture is used here with the CB and GA procedures ...
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A Review: Evaluating the Parametric Optimization of Electrical Discharge Machining (EDM) by Using & Comparing Artificial Neural Network (ANN) and Genetic Algorithm (GA)
... establish models that accurately correlate the process variables and performance of EDM ...existing models and hence experimental investigations are always ...artificial neural network (ANN) ...
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Combining DFT and QSAR result for predicting the biological activity of the phenylsuccinimide derivatives
... In this work, we have relied on the same data-base studied by Takayama et al. (1983) for N-phenylsuccinimides (fig. 1) using several statistical tools: Principal Components Analysis (PCA), Multiple Linear Regression ...
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