example3.html

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		<meta http-equiv="Description" content="This work is a java implementation of feed forward neural nets. Using this package, you can easily build and train multilayer, generalized feed forward, modular feed forward nets with any number of layers and any number of units.">
		<title>Example 3 - FEED FORWARD NEURAL NETWORKS - A JAVA IMPLEMENTATION 
        v2.0</title>
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					<b><font size="4"><a name="top"></a>FEED FORWARD NEURAL 
                    NETWORK<span lang="tr">S</span> - A JAVA IMPLEMENTATION v2.0 </font></b>
                    <br><font size="5"><b>Example </b></font><span lang="tr">
                    <font size="5"><b>3</b></font></span></td>
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					This is an example of an untypical usage of the
                    <span lang="tr">package</span>. We don't use pattern<span lang="tr">
                    </span>sets and error calculation methods, we use 
                    incremental training with patterns created on the fly.<br>
                    <br>
                    Here we create a very simple multilayer perceptron and train 
                    it for a simple function.<br>
                    <br>
                    - Create a multilayer perceptron with three layers: one 
                    input layer with two units; one hidden layer with three 
                    neurons, using tanh function; one output layer with one 
                    neuron using linear function.<br>
                    - Create random input values, calculate target by the 
                    formula y = sin ( x1 + x2 ) and train the net with these 
                    values without using a pattern set.<br>
                    - Test it.</td>
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