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By DARPA Neural Network Study (U.S.)

ISBN-10: 0916159175

ISBN-13: 9780916159177

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2 Networks for Classification The previous section describes networks that attempt to make projections of the future. But understanding trends and what impacts those trends might have is only one of several types of applications. The second class of applications is classification. A network that can classify could be used in the medical industry to process both lab results and doctor-recorded patience symptoms to determine the most likely disease. Other applications can separate the "tire kicker" inquiries from the requests for information from real buyers.

There is only one feedforward path from input layer to output layer. 2. The uni-directional counter-propagation network has three layers. , a fourth layer is sometimes added. The main layers include an input buffer layer, a self-organizing Kohonen layer, and an output layer which uses the Delta Rule to modify its incoming connection weights. Sometimes this layer is called a Grossberg Outstar layer. 2. An Example Counter-propagation Network. 44 The size of the input layer depends upon how many separable parameters define the problem.

Hopfield Law: It is similar to Hebb's rule with the exception that it specifies the magnitude of the strengthening or weakening. " The Delta Rule: This rule is a further variation of Hebb's Rule. It is one of the most commonly used. This rule is based on the simple idea of continuously modifying the strengths of the input connections to reduce the difference (the delta) between the desired output value and the actual output of a processing element. This rule changes the synaptic weights in the way that minimizes the mean squared error of the network.

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Artificial neural networks technology. DACS report by DARPA Neural Network Study (U.S.)


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