backpropagation algortihm for fisher data
標(biāo)簽： backpropagation algortihm fisher data
上傳時(shí)間： 2013-12-23
上傳用戶：weixiao99
the attached file contains backpropagation code using visual basic
標(biāo)簽： backpropagation attached contains visual
上傳時(shí)間： 2013-11-28
上傳用戶：cainaifa
Source code Recognize character with backpropagation
標(biāo)簽： backpropagation Recognize character Source
上傳時(shí)間： 2013-12-25
上傳用戶：wfl_yy
該文檔為BP(backpropagation)反向傳播神經(jīng)網(wǎng)絡(luò)介紹及公式推導(dǎo)詳述資料，講解的還不錯(cuò)，感興趣的可以下載看看…………………………
標(biāo)簽： 神經(jīng)網(wǎng)絡(luò)
上傳時(shí)間： 2021-11-01
上傳用戶：默默
自適應(yīng)(Adaptive)神經(jīng)網(wǎng)絡(luò)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
標(biāo)簽： collection implement Adaptive adaptive
上傳時(shí)間： 2015-04-09
上傳用戶：ywqaxiwang
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
標(biāo)簽： Neural collection implement Adaptive
上傳時(shí)間： 2013-12-23
上傳用戶：teddysha
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.
標(biāo)簽： Single-layer algorithms best-known networks
上傳時(shí)間： 2015-06-17
上傳用戶：趙云興
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. ％
標(biāo)簽： back-propagation corresponding input-output algorithm
上傳時(shí)間： 2016-12-27
上傳用戶：exxxds
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