C++ Neural Networks and Fuzzy Logic: Preface

Neural Networks for Optimization Problems

It is possible to construct a neural network to find the values of the variables that correspond to an optimum

value of the objective function of a problem. For example, the neural networks that use the Widrow−Hoff

learning rule find the minimum value of the error function using the least mean squared error. Neural

networks such as the feedforward backpropagation network use the steepest descent method for this purpose

and find a local minimum of the error, if not the global minimum. On the other hand, the Boltzmann machine

or the Cauchy machine uses statistical methods and probabilities and achieves success in finding the global

minimum of an error function. So we have an idea of how to go about using a neural network to find an

optimum value of a function. The question remains as to how the constraints of an optimization problem

should be treated in a neural network operation. A good example in answer to this question is the traveling

salesperson problem. Let’s discuss this example next.

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C++ Neural Networks and Fuzzy Logic:Preface

Neural Networks for Optimization Problems

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