Download C++ Neural Networks and Fuzzy Logic by Valluru B. Rao, Hayagriva Rao PDF

By Valluru B. Rao, Hayagriva Rao

The largely revised and up-to-date variation presents a logical and easy-to-follow development via C++ programming for 2 of the most well-liked applied sciences for synthetic intelligence--neural and fuzzy programming. The authors conceal idea in addition to useful examples, giving programmers an effective starting place in addition to operating examples with reusable code.

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Extra resources for C++ Neural Networks and Fuzzy Logic

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This means that changes do not occur simultaneously to outputs that are fed back as inputs, but rather occur for one vector component at a time. The true operation of the Hopfield network follows the procedure below for input vector Invec and output vector Outvec: 1. Apply an input, Invec, to the network, and initialize Outvec = Invec 2. Start with i = 1 3. Calculate Valuei = DotProduct ( Inveci, Columni of Weight matrix) 4. Calculate Outveci = f(Valuei) where f is the threshold function discussed previously 5.

If two patterns of equal length are given and are treated as vectors, their dot product is obtained by first multiplying corresponding components together and then adding these products. Two vectors are said to be orthogonal, if their dot product is 0. The mathematics involved in computations done for neural networks include matrix multiplication, transpose of a matrix, and transpose of a vector. Also see Appendix B. The inputs (which are stable, stored patterns) to be given should be orthogonal to one another.

For example, for x3 and x5: x3 = w23 x2 + w13 x1 x5 = w35 x3 + w45 x4 We will formalize the equations in Chapter 7, which details one of the training algorithms for the feed-forward network called Backpropagation. Note that you present information to this network at the leftmost nodes (layer 1) called the input layer. html (1 of 3) [21/11/02 21:56:45] C++ Neural Networks and Fuzzy Logic:Introduction to Neural Networks cases do so from the rightmost node(s), which make up the output layer. Weights are usually determined by a supervised training algorithm, where you present examples to the network and adjust weights appropriately to achieve a desired response.

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