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人工智能/神经网络 Single-layer neural networks can be trained using various learning algorithms. The best-known algori
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 pe ...
电子书籍 小波神经网络好文章！A method for fault detection is proposed using a trained neural network as the nominal mo
小波神经网络好文章！A method for fault detection is proposed using a trained neural network as the nominal
model of the system to be monitored. Partial physical knowledge, if available, can be combined
with the nominal model to perform fault isolation.
matlab例程 neural network trained with unscented kalman filter
neural network trained with unscented kalman filter
matlab例程 neural network trained with extended kalman filter
neural network trained with extended kalman filter
人工智能/神经网络 neural network utility is a Neural Networks library for the C++ Programmer. It is entirely object o
neural network utility is a Neural Networks library for the
C++ Programmer. It is entirely object oriented and focuses
on reducing tedious and confusing problems of programming neural networks.
By this I mean that network layers are easily defined. An
entire multi-layer network can be created in a f ...
matlab例程 This function calculates Akaike s final prediction error ％ estimate of the average generalization e
This function calculates Akaike s final prediction error
％ estimate of the average generalization error.
％
％ [FPE,deff,varest,H] = fpe(NetDef,W1,W2,PHI,Y,trparms) produces the
％ final prediction error estimate (fpe), the effective number of
％ weights in the network if the network has been train ...
人工智能/神经网络 ％ Train a two layer neural network with the Levenberg-Marquardt ％ method. ％ ％ If desired, it is p
％ Train a two layer neural network with the Levenberg-Marquardt
％ method.
％
％ If desired, it is possible to use regularization by
％ weight decay. Also pruned (ie. not fully connected) networks can
％ be trained.
％
％ Given a set of corresponding input-output pairs and an initial
％ network,
％ ...
人工智能/神经网络 This function calculates Akaike s final prediction error ％ estimate of the average generalization e
This function calculates Akaike s final prediction error
％ estimate of the average generalization error for network
％ models generated by NNARX, NNOE, NNARMAX1+2, or their recursive
％ counterparts.
％
％ [FPE,deff,varest,H] = nnfpe(method,NetDef,W1,W2,U,Y,NN,trparms,skip,Chat)
％ produces the fin ...
matlab例程 This function applies the Optimal Brain Surgeon (OBS) strategy for ％ pruning neural network models
This function applies the Optimal Brain Surgeon (OBS) strategy for
％ pruning neural network models of dynamic systems. That is networks
％ trained by NNARX, NNOE, NNARMAX1, NNARMAX2, or their recursive
％ counterparts.
matlab例程 Train a two layer neural network with a recursive prediction error ％ algorithm ("recursive Gauss-Ne
Train a two layer neural network with a recursive prediction error
％ algorithm ("recursive Gauss-Newton"). Also pruned (i.e., not fully
％ connected) networks can be trained.
％
％ The activation functions can either be linear or tanh. The network
％ architecture is defined by the matrix NetDef , w ...