فایل کامل و عالی حل تمرین کامل متلب matlab شبکه عصبی مارتین هاگان martin hagan

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 فایل کامل و عالی حل تمرین کامل متلب matlab شبکه عصبی مارتین هاگان martin hagan دارای ۱۲۹ صفحه می باشد و دارای تنظیمات کامل در MATLAB می باشد و آماده پرینت یا چاپ است

فایل Matlab (مطلب یا همان متلب) فایل کامل و عالی حل تمرین کامل متلب matlab شبکه عصبی مارتین هاگان martin hagan  کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه  و مراکز دولتی می باشد.

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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.