% Neural Network Design Demonstrations.
% Copyright (c) 1994 by PWS Publishing Company.
%
% General
%   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.