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% nnd - Splash screen.
% nndtoc - Table of contents.
% nnsound - Turn Neural Network Design sounds on and off.
% poslin - Positive linear tranfer function.
Chapter 2
, Neuron Model and Network Architectures
% nnd2n1 - One-input neuron demonstration.+
% nnd2n2 - Two-input neuron demonstration.+
%
% Chapter 3, An Illustrative Example
% nnd3pc - Perceptron classification demonstration.+
% nnd3hamc - Hamming classification demonstration.+
% nnd3hopc - Hopfield classification demonstration.+
Chapter 4
, Perceptron Learning Rule
% nnd4db - Decision boundaries demonstration.+
% nnd4pr - Perceptron rule demonstration.+
Chapter 5
, Signal and Weight Vector Spaces
% nnd5gs - Gram-Schmidt demonstration.
% nnd5rb - Reciprocal basis demonstration.
Chapter 6
, Linear Transformations for Neural Networks
% nnd6lt - Linear transformations demonstration.
% nnd6eg - Eigenvector game.
Chapter 7
, Supervised Hebbian Learning
% nnd7sh - Supervised Hebb demonstration.
Chapter 8
, Performance Surfaces and Optimum Points
% nnd8ts1 - Taylor series demonstration #1.
% nnd8ts2 - Taylor series demonstration #2.
% nnd8dd - Directional derivatives demonstration.
% nnd8qf - Quadratic function demonstration.
Chapter 9
, Performance Optimization
% nnd9sdq - Steepest descent for quadratic function demonstration.
% nnd9mc - Method comparison demonstration.
% nnd9nm - Newton's method demonstration.
% nnd9sd - Steepest descent demonstration.
Chapter 10
, Widrow-Hoff Learning
% nnd10nc - Adaptive noise cancellation demonstration.+
% nnd10eeg - Electroencephelogram noise cancellation demonstration.+
% nnd10lc - Linear pattern classification demonstration.+
Chapter 11
, Backpropagation
% nnd11nf - Network function demonstration.+
% nnd11bc - Backpropagation calculation demonstration.*
% nnd11fa - Function approximation demonstration.*
% nnd11gn - Generalization demonstration.*
Chapter 12
, Variations on Backpropagation
% nnd12sd1 - Steepest descent backpropagation demonstration #1.*
% nnd12sd2 - Steepest descent backpropagation demonstration #2.*
% nnd12mo - Momentum backpropagation demonstration.*
% nnd12vl - Variable learning rate backpropagation demonstration.*
% nnd12ls - Conjugate gradient line search demonstration.*
% nnd12cg - Conjugate gradient backpropagation demonstration.*
% nnd12ms - Maquardt step demonstration.*
% nnd12m - Marquardt backpropagation demonstration.*
Chapter 13,
Associative Learning
% nnd13uh - Unsupervised Hebb demonstration.+
% nnd13edr - Effects of decay rate demonstration.+
% nnd13hd - Hebb with decay demonstration.+
% nnd13gis - Graphical instar demonstration.+
% nnd13is - Instar demonstration.+
% nnd13os - Outstar demonstration.+
Chapter 14
, Competitive Networks
% nnd14cc - Competitive classification demonstration.+
% nnd14cl - Competitive learning demonstration.+
% nnd14fm1 - 1-D Feature map demonstration.*
% nnd14fm2 - 2-D Feature map demonstration.*
% nnd14lv1 - LVQ1 demonstration.*
% nnd14lv2 - LVQ2 demonstration.*
Chapter 15
, Grossberg Network
% nnd15li - Leaky integrator demonstration.
% nnd15sn - Shunting network demonstration.
% nnd15gl1 - Grossberg layer 1 demonstration.
% nnd15gl2 - Grossberg layer 2 demonstration.
% nnd15aw - Adaptive weights demonstration.
Chapter 16
, Adaptive Resonance Theory
% nnd16al1 - ART1 layer 1 demonstration.
% nnd16al2 - ART1 layer 2 demonstration.
% nnd16os - Orienting subsystem demonstration.
% nnd16a1 - ART1 algorithm demonstration.
Chapter 17
, Stability
% nnd17ds - Dynamical system demonstration.
%
% Chapter 18, Hopfield Network
% nnd18hn - Hopfield network demonstration.
%
% + Requires MININNET functions or the Neural Network Toolbox.
% * Requires the Neural Network Toolbox.
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martin hagan مارتین هاگان متلب matlab شبکه عصبی neural network حل تمرین متلب ۱۷ فصل