The transfer function in the neural network
Proposed Neural Network with FFT Transfer Function to Estimate Henon Dynamical Map
... artificial neural network (Ann) to estimate two-dimensional Henon dynamical map by selecting an appropriate network, transfer function and node ...proposed network side by side ...
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Estimation of Tinkerbell Dynamical Map by Using Neural Network with FFT as Transfer Function
... as transfer function in artificial neural network gives excellent results and good accuracy with and without noise in compare with traditional transfer functions (see Tables (1) – (9) ) ...
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Proposed Neural Network with FFT Transfer Function to Estimate Loranz Dynamical Map
... artificial neural network (Ann) to estimate three dimensional Loranz dynamical map by selecting an appropriate network, transfer function and node weights to get Loranz dynamical map ...
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Estimation of Noisy Logistic Dynamical Map by Using Neural Network with FFT Transfer Function
... artificial neural network (Ann) to estimate one dimensional noisy Logistic dynamical map by selecting an appropriate network, transfer function and node weights to get noisy Logistic ...
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Neural Network Estimation of Some Noisy Asymmetric Dynamical Maps with Use FFT as Transfer Function
... artificial neural network (Ann) to estimate one dimensional noisy Asymmetric dynamical map by selecting an appropriate network, transfer function and node weights to get noisy ...
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Head-Related Transfer Function Selection Using Neural Networks
... 3. Neural network for HRTF selection The selection of HRTF datasets based on the sum of the squared errors, as presented in ...a neural network can learn the best mapping be- tween the users’ ...
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Application of a radial basis function neural network for diagnosis of diabetes mellitus
... Artificial neural network, logistic regression, multilayer perceptron, radial basis function, supervised ...(MLP) network models are the popular network architectures used in most of ...
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Neural network radiative transfer for imaging spectroscopy
... tive transfer models ...nonparametric function approxima- tion with neural networks can replicate radiative transfer cal- culations and generate accurate radiance spectra at multiple ...
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Radial basis function neural network for software engineering measures A survey
... of Neural Network, and contains a unique hidden ...linear transfer process at the output ...The Network topology with a single layer of trainable weights radically decreases the time of ...
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The impact of VMAX activation function in particle swarm optimization neural network
... Artificial Neural Networks (ANN) with Particle Swarm Optimization ...a network topology, inputs, and transfer functions for a network before ...Any network parameter may be thrown into ...
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The impact of VMAX activation function in particle swarm optimization neural network
... Artificial Neural Networks (ANN) with Particle Swarm Optimization ...a network topology, inputs, and transfer functions for a network before ...Any network parameter may be thrown into ...
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PROBABILISTIC POTENTIAL FUNCTION NEURAL NETWORK CLASSIFIER
... potential function neural network classifier algorithm to deal with classes which are multi-modally distributed and formed from sets of disjoint pattern clusters is proposed in the ...other ...
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Deep Learning in an Adaptive Function Neural Network
... Artificial neural network learning is typically accomplished via adaptation between ...the network can learn to respond differentially to classes of incoming ...internal neural mechanism is ...
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Convolutional neural network transfer for automated glaucoma identification
... Convolutional Neural Networks (CNNs) are powerful tools for solving image classification tasks as they are able to learn highly discriminative features from raw pixel ...
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Wavelet Neural Network with Random Wavelet Function Parameters
... Wavelet Neural Networks (WNN) is a bottleneck which impacts on the accuracy of the final WNN ...the network; meanwhile, the wavelet function parameters are randomly assigned and kept fixed during the ...
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Design of Hybrid Fuzzy Neural Network for Function Approximation
... Keywords: Cardinality, Classifier, Function Approximation, Fuzzy Neural System, Mutual Subsethood 1. Introduction The conventional approaches to system modeling that are based on mathematical tools (i.e. ...
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Learning Methods of Radial Basis Function Neural Network
... Basis Function Neural Network （ RBF ） is widely used now; it can solve the following problems: the small sample, nonlinear, dimension and local ...
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An Improved Wavelet Neural Network For Classification And Function Approximation
... the network architecture, learning algorithm, parameter initialization, and also on the dataset ...activation function that is well-suited for all the cases does not ...activation function, this ...
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Transfer learning of deep neural network representations for fMRI decoding
... deep neural networks and measured neural responses, another active area of research focuses more on examining how far these methods can be applied to brain imaging to improve existing statistical ...deep ...
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Prediction of Daily Network Traffic based on Radial Basis Function Neural Network
... daily network traffic prediction is also a process of analyzing and determining the quota of bandwidth in a network in the future, in which a technical analysis approach usage data ...
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