Filename
Size
Date
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