<?xml version="1.0" encoding="utf-8"?><!-- $Revision: 1.6.2.2 $  $Date: 2004/02/06 00:25:33 $ --><demos>   <name>Neural Network</name>   <type>toolbox</type>   <icon>$toolbox/matlab/icons/matlabicon.gif</icon>   <description><![CDATA[<p>The Neural Network Toolbox includes many kinds of powerful networksfor solving problems including:</p><ul>   <li> function approximation, modeling,</li>   <li> signal processing and prediction</li>   <li> classification, and clustering.</li></ul><p>These tools are an essential part of many applications, includingengineering, finance, medicine, and artificial intelligence.</p>]]></description>   <demosection>      <label>Neurons</label>      <demoitem>         <label>Simple neuron and transfer functions</label>         <callback>nnd2n1</callback>      </demoitem>      <demoitem>         <label>Neuron with vector input</label>         <callback>nnd2n2</callback>      </demoitem>   </demosection>   <demosection>      <label>Perceptrons</label>      <demoitem>         <label>Decision Boundaries</label>         <callback>nnd4db</callback>      </demoitem>      <demoitem>         <label>Perceptron learning rule</label>         <callback>nnd4pr</callback>      </demoitem>      <demoitem>         <label>Classification with a 2-input perceptron</label>         <file>html/demop1.html</file>         <callback>playshow demop1</callback>      </demoitem>      <demoitem>         <label>Outlier input vectors</label>         <file>html/demop4.html</file>         <callback>playshow demop4</callback>      </demoitem>      <demoitem>         <label>Normalized perceptron rule</label>         <file>html/demop5.html</file>         <callback>playshow demop5</callback>      </demoitem>      <demoitem>         <label>Linearly non-separable vectors</label>         <file>html/demop6.html</file>         <callback>playshow demop6</callback>      </demoitem>   </demosection>   <demosection>      <label>Linear Networks</label>      <demoitem>         <label>Pattern association showing error surface</label>         <file>html/demolin1.html</file>         <callback>playshow demolin1</callback>      </demoitem>      <demoitem>         <label>Training a linear neuron</label>         <file>html/demolin2.html</file>         <callback>playshow demolin2</callback>      </demoitem>      <demoitem>         <label>Linear classification system</label>         <callback>nnd10lc</callback>      </demoitem>      <demoitem>         <label>Adaptive noise cancellation</label>         <file>html/demolin8.html</file>         <callback>playshow demolin8</callback>      </demoitem>      <demoitem>         <label>Adaptive noise cancellation in airplane</label>         <callback>nnd10nc</callback>      </demoitem>      <demoitem>         <label>Linear fit of nonlinear problem</label>         <file>html/demolin4.html</file>         <callback>playshow demolin4</callback>      </demoitem>      <demoitem>         <label>Underdetermined problem</label>         <file>html/demolin5.html</file>         <callback>playshow demolin5</callback>      </demoitem>      <demoitem>         <label>Linearly dependent problem</label>         <file>html/demolin6.html</file>         <callback>playshow demolin6</callback>      </demoitem>      <demoitem>         <label>Too large a learning rate</label>         <file>html/demolin7.html</file>         <callback>playshow demolin7</callback>      </demoitem>   </demosection>   <demosection>      <label>Backpropagation</label>      <demoitem>         <label>Generalization</label>         <callback>nnd11gn</callback>      </demoitem>      <demoitem>         <label>Steepest descent backpropagation</label>         <callback>nnd12sd1</callback>      </demoitem>      <demoitem>         <label>Momentum backpropagation</label>         <callback>nnd12mo</callback>      </demoitem>      <demoitem>         <label>Variable learning rate backpropagation</label>         <callback>nnd12vl</callback>      </demoitem>      <demoitem>         <label>Conjugate gradient backpropagation</label>         <callback>nnd12cg</callback>      </demoitem>      <demoitem>         <label>Marquardt backpropagation</label>         <callback>nnd12m</callback>      </demoitem>   </demosection>   <demosection>      <label>Radial Basis Networks</label>      <demoitem>         <label>Radial basis approximation</label>         <file>html/demorb1.html</file>         <callback>playshow demorb1</callback>      </demoitem>      <demoitem>         <label>Radial basis underlapping neurons</label>         <file>html/demorb3.html</file>         <callback>playshow demorb3</callback>      </demoitem>      <demoitem>         <label>Radial basis overlapping neurons</label>         <file>html/demorb4.html</file>         <callback>playshow demorb4</callback>      </demoitem>      <demoitem>         <label>GRNN function approximation</label>         <file>html/demogrn1.html</file>         <callback>playshow demogrn1</callback>      </demoitem>      <demoitem>         <label>PNN classification</label>         <file>html/demopnn1.html</file>         <callback>playshow demopnn1</callback>      </demoitem>   </demosection>   <demosection>      <label>Self-organizing Networks</label>      <demoitem>         <label>Competitive learning</label>         <file>html/democ1.html</file>         <callback>playshow democ1</callback>      </demoitem>      <demoitem>         <label>One-dimensional self-organizing map</label>         <file>html/demosm1.html</file>         <callback>playshow demosm1</callback>      </demoitem>      <demoitem>         <label>Two-dimensional self-organizing map</label>         <file>html/demosm2.html</file>         <callback>playshow demosm2</callback>      </demoitem>   </demosection>   <demosection>      <label>LVQ Networks</label>      <demoitem>         <label>Learning vector quantization</label>         <file>html/demolvq1.html</file>         <callback>playshow demolvq1</callback>      </demoitem>   </demosection>   <demosection>      <label>Hopfield Networks</label>      <demoitem>         <label>Hopfield two neuron design</label>         <file>html/demohop1.html</file>         <callback>playshow demohop1</callback>      </demoitem>      <demoitem>         <label>Hopfield unstable equilibria</label>         <file>html/demohop2.html</file>         <callback>playshow demohop2</callback>      </demoitem>      <demoitem>         <label>Hopfield three neuron design</label>         <file>html/demohop3.html</file>         <callback>playshow demohop3</callback>      </demoitem>      <demoitem>         <label>Hopfield spurious stable points</label>         <file>html/demohop4.html</file>         <callback>playshow demohop4</callback>      </demoitem>   </demosection>   <demosection>      <label>Application Examples</label>      <demoitem>         <label>Linear design (command-line)</label>         <callback>applin1</callback>      </demoitem>      <demoitem>         <label>Adaptive linear prediction (command-line)</label>         <callback>applin2</callback>      </demoitem>      <demoitem>         <label>Elman amplitude detection (command-line)</label>         <callback>appelm1</callback>      </demoitem>      <demoitem>         <label>Character recognition (command-line)</label>         <callback>appcr1</callback>      </demoitem>   </demosection>   <demosection>      <label>Control Systems</label>      <demoitem>         <label>Predictive control of a tank reactor (sim)</label>         <callback>predcstr</callback>         <dependency>Simulink</dependency>      </demoitem>      <demoitem>         <label>NARMA-L2 control of a magnet levitation system (sim)</label>         <callback>narmamaglev</callback>         <dependency>Simulink</dependency>      </demoitem>      <demoitem>         <label>Reference control of a robot arm (sim)</label>         <callback>mrefrobotarm</callback>         <dependency>Simulink</dependency>      </demoitem>   </demosection>   <demosection>      <label>Other Demos</label>      <demoitem>         <label>Other Neural Network Design textbook demos</label>         <callback>nnd</callback>      </demoitem>   </demosection></demos>