Search - Backpropagation

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[Other] Backpropagation DL : 0: 累神經系統 Backpropagation.
Update : 2008-10-13 Size : 626.55kb Publisher : TAE
[Other resource] backpropagation DL : 0: Neural Network backpropagation
Update : 2008-10-13 Size : 66.94kb Publisher : joy
[AI-NN-PR] BPN DL : 0: Backpropagation Network Time-Series Forecasting 源码, 经典的BPN人工神经网络例子源码-Backpropagation Network Time-Series Forecasting source, the classic example of artificial neural network BPN source
Update : 2025-02-17 Size : 33kb Publisher :
[AI-NN-PR] Backpropagation Network with Bias Terms and Moment DL : 0: 附加了动量项BP神经网络-Additional items of Momentum BP neural network
Update : 2025-02-17 Size : 30kb Publisher : 高山
[AI-NN-PR] 自适应(Adaptive)神经网络源程序 DL : 0: 自适应(Adaptive)神经网络源程序 The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.~..~ There are 11 blocks that implement basically these 5 kinds of neural networks: 1) Adaptive Linear Network (ADALINE) 2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm A simulink example regarding the approximation of a scalar nonlinear function of 4 variables -Adaptive (Adaptive) The neural network source adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms .~..~ There are 11 blocks that implement basically these five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) 102206 with Multilayer Layer Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Dynamic Networks with the Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables
Update : 2025-02-17 Size : 196kb Publisher : 周志连
[AI-NN-PR] Adaptive DL : 0: The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms.~..~ There are 11 blocks that implement basically these 5 kinds of neural networks: 1) Adaptive Linear Network (ADALINE) 2) Multilayer Layer Perceptron with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) RBF and Piecewise Linear Networks with Dynamic Cell Structure (DCS) algorithm A simulink example regarding the approximation of a scalar nonlinear function of 4 variables is included-The adaptive Neural Network Library is a collection of blocks that implement several Adaptive Neural Networks featuring different adaptation algorithms .~..~ There are 11 blocks that implement basically these five kinds of neural networks : a) Adaptive Linear Network (ADALINE) 2) Multilayer Layer 102206 with Extended Backpropagation algorithm (EBPA) 3) Radial Basis Functions (RBF) Networks, 4) RBF Networks with Extended Minimal Resource Allocating algorithm (EMRAN) 5) and RBF Networks with Piecewise Linear Dynamic Cell Structure (DCS) algorithm A Simulink example regarding the approximation of a scalar nonlinear function of four variables is included
Update : 2025-02-17 Size : 194kb Publisher : 叶建槐
[CSharp] pittnet DL : 0: Backpropagation / ART1 / Kohonen / Radial Basis-Backpropagation/ART1/Kohonen/Radial B Fellowships
Update : 2025-02-17 Size : 143kb Publisher : jack
[AI-NN-PR] apbSrc.tar DL : 0: Single-layer neural networks can be trained using various learning algorithms. The best-known algorithms are the Adaline, Perceptron and Backpropagation algorithms for supervised learning. The first two are specific to single-layer neural networks while the third can be generalized to multi-layer perceptrons.-Single-layer neural networks can be train ed using various learning algorithms. The best- known algorithms are the Adaline. 102206 and Backpropagation algorithms fo r supervised learning. The first two are specific ic to single-layer neural networks while the th ird can be generalized to multi-layer perceptr ons.
Update : 2025-02-17 Size : 799kb Publisher : 陈伟
[AI-NN-PR] BackPropagation DL : 0: 以类库的形式实现了BP神经网络算法。可从该类基础上派生新类，实现各类应用-In order to realize a form of class libraries BP neural network algorithm. Can be derived from the basis of such new categories, the realization of a wide range of applications
Update : 2025-02-17 Size : 5kb Publisher : 唐述
[AI-NN-PR] NeuralNetworkOCR DL : 0: bp, backpropagation 神经网络简单认字错误开发实例。-bp, backpropagation neural network to develop a simple example of character recognition errors.
Update : 2025-02-17 Size : 78kb Publisher : ky lee
[Other] Backpropagation DL : 0: 累神經系統 Backpropagation.-Tired nervous system Backpropagation.
Update : 2025-02-17 Size : 627kb Publisher :
[AI-NN-PR] backpropagation DL : 0: Neural Network backpropagation
Update : 2025-02-17 Size : 1.98mb Publisher : joy
[AI-NN-PR] ann_demo DL : 0: Backpropagation Artificial Neural Network in C
Update : 2025-02-17 Size : 96kb Publisher : 杨诚
[Speech/Voice recognition/combine] smc_speech DL : 0: 《Neural Networks for Text-to-Speech Phoneme Recognition》This paper presents two different artificial neural network approaches for phoneme recognition for text-to-speech applications: Staged Backpropagation Neural Networks and Self-Organizing Maps.- Neural Networks for Text-to-Speech Phoneme Recognition This paper presents two different artificial neural network approaches for phoneme recognition for text-to-speech applications: Staged Backpropagation Neural Networks and Self-Organizing Maps.
Update : 2025-02-17 Size : 706kb Publisher : 付诗
[AI-NN-PR] lightweightbackpropagationneuralnetwork DL : 0: * Lightweight backpropagation neural network. * This a lightweight library implementating a neural network for use * in C and C++ programs. It is intended for use in applications that * just happen to need a simply neural network and do not want to use * needlessly complex neural network libraries. It features multilayer * feedforward perceptron neural networks, sigmoidal activation function * with bias, backpropagation training with settable learning rate and * momentum, and backpropagation training in batches. -* Lightweight backpropagation neural network.* This a lightweight library implementating a neural network for use* in C and C++ Programs. It is intended for use in applications that* just happen to need a simply neural network and do not want to use* needlessly complex neural network libraries. It features multilayer* feedforward perceptron neural networks, sigmoidal activation function* with bias, backpropagation training with settable learning rate and* momentum, and backpropagation training in batches.
Update : 2025-02-17 Size : 29kb Publisher : 正熹
[Mathimatics-Numerical algorithms] batbp DL : 0: Batch version of the back-propagation algorithm. ％ Given a set of corresponding input-output pairs and an initial network ％ [W1,W2,critvec,iter]=batbp(NetDef,W1,W2,PHI,Y,trparms) trains the ％ network with backpropagation. ％％ The activation functions must be either linear or tanh. The network ％ architecture is defined by the matrix NetDef consisting of two ％ rows. The first row specifies the hidden layer while the second ％ specifies the output layer. ％-Batch version of the back-propagation algorithm. Given a set of corresponding input-output pairs and an initial network [W1, W2, critvec, iter] = batbp (NetDef, W1, W2, PHI, Y, trparms) trains the network with backpropagation. The activation functions must be either linear or tanh. The network architecture is defined by the matrix NetDef consisting of two rows. The first row specifies the hidden layer while the second specifies the output layer.
Update : 2025-02-17 Size : 2kb Publisher : 张镇
[CSharp] Neural_Dot_Net_2002 DL : 0: Backpropagation Neural network implementation in C# .NET
Update : 2025-02-17 Size : 788kb Publisher : wanayngawai
[Software Engineering] backpropagation DL : 0: backpropagation mlp neural network
Update : 2025-02-17 Size : 1kb Publisher : niksu
[Education soft system] Backpropagation DL : 0: Make a program to train a Backpropagation neural network which simulates the behavior of an XOR gate.
Update : 2025-02-17 Size : 486kb Publisher : Juan Carlos
[matlab] backpropagation DL : 0: A MLP code with backpropagation training algorithm designed for aproximation of functions problems.
Update : 2025-02-17 Size : 1kb Publisher : Paulo

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