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文件名称:ANN

  • 所属分类：
  • matlab例程
  • 资源属性：
  • [Matlab] [源码]
  • 上传时间：
  • 2012-11-26
  • 文件大小：
  • 26kb
  • 下载次数：
  • 0次
  • 提 供 者：
  • a***
  • 相关连接：
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ann matlab neural network
(系统自动生成,下载前可以参看下载内容)

下载文件列表

文件名大小更新时间
ANN\Adaptive Filters 10
...\....................\Adaptive Filter Example.m
...\....................\Adaptive Filter Example1.m
...\....................\Adaptive Noise Cancellation.m
...\Application Example
...\...................\alphabet 1.m
...\...................\alphabet 2.m
...\...................\Elman 2.m
...\...................\Elman networks 1.m
...\...................\Linear Filter.m
...\Backpropagation 5
...\..................\Automated Regularization (trainbr).m
...\..................\Batch Gradient Descent (traingd).m
...\..................\Batch Gradient Descent with Momentum (traingdm.m
...\..................\feedfor.m
...\..................\Fletcher-Reeves Update (traincgf).m
...\..................\Levenberg-Marquardt (trainlm).m
...\..................\Modified Performance Function.m
...\..................\One Step Secant Algorithm (trainoss).m
...\..................\Polak-Ribi俽e Update (traincgp).m
...\..................\Powell-Beale Restarts (traincgb).m
...\..................\Quasi-Newton Algorithms (trainbgf).m
...\..................\Resilient Backpropagation (trainrp).m
...\..................\Sample Training Session.m
...\..................\Scaled Conjugate Gradient (trainscg).m
...\..................\Variable Learning Rate (traingda traingdx).m
...\Linear Filters 4
...\.................\Creating a Linear Neuron (newlin).m
...\.................\Linear Classification (train).m
...\.................\Linear System Design (newlind).m
...\.................\net 5.m
...\.................\newlin1.m
...\.................\Tapped Delay Line.m
...\.................\Too Large a Learning Rate.m
...\Neuron Model 2
...\...............\Batch Training With Dynamic Networks.m
...\...............\Batch Training with Static Networks.asv
...\...............\Batch Training with Static Networks.m
...\...............\Example.m
...\...............\Incremental Training with Dynamic Networks.m
...\...............\Incremental Training with Static N EXA.asv
...\...............\Incremental Training with Static N EXA.m
...\...............\Incremental Training with Static Networks 2.m
...\...............\Incremental Training with Static Networks 3.m
...\...............\Simulation With Concurrent Inputs in a Dynamic Network.m
...\...............\Simulation With Concurrent Inputs in a Static Network.m
...\...............\Simulation With Sequential Inputs in a Dynamic Network.m
...\Perceptrons 3
...\..............\a.m
...\..............\Normalized Perceptron Rule.m
...\..............\Outliers and the Normalized Perceptron Rule.m
...\..............\perceptron 2.m
...\..............\perceptron 3.asv
...\..............\perceptron 3.m
...\..............\perceptron 4.asv
...\..............\perceptron 4.m
...\..............\perceptron limitation.m
...\..............\perseptron 1.m
...\..............\simulat perceptron.m
...\Radial Basis Networks 7
...\........................\Design (newpnn).m
...\........................\GRNN Function Approximation.m
...\........................\PNN Classification.m
...\Recurrent 9
...\............\Creating an Elman Network (newelm).m
...\............\Design (newhop).m
...\............\Example.m
...\............\Hopfield Three Neuron Design.m
...\Self-Organizing 8
...\..................\Competitive Learning.m
...\..................\Creating a Self Organizing MAP Neural Network.m
...\..................\Creating an LVQ Network (newlvq).m
...\..................\One-Dimensional Self-organizing Map.m
...\..................\self 0.m
...\..................\self 1.m
...\..................\som.m
...\..................\Two-Dimensional Self-organizing Map.m

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