C++ Neural Networks and Fuzzy Logic: Preface

The Train/Test/Redesign Loop

Much of the process of determining the best parameters for a given application is trial and error. You need to

spend a great deal of time evaluating different options to find the best fit for your problem. You may literally

create hundreds if not thousands of networks either manually or automatically to search for the best solution.

Many commercial neural network programs use genetic algorithms to help to automatically arrive at an

optimum network. A genetic algorithm makes up possible solutions to a problem from a set of starting genes.

Analogous to biological evolution, the algorithm combines genetic solutions with a predefined set of operators

to create new generations of solutions, who survive or perish depending on their ability to solve the problem.

The key benefit of genetic algorithms (GA) is the ability to traverse an enormous search space for a possibly

optimum solution. You would program a GA to search for the number of hidden layers and other network

parameters, and gradually evolve a neural network solution. Some vendors use a GA only to assign a starting

set of weights to the network, instead of randomizing the weights to start you off near a good solution.

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C++ Neural Networks and Fuzzy Logic:Preface

Design a Network Architecture

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