Neural Networks: Implementations and Applications

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering area

Using genetic algorithms with grammar encoding to generate neural networks

Kitano's approach to neural network design is extended in the sense that not just the neural network structure, but also the values of the weights are coded in the chromosome. Experimental results are presented demonstrating the capability of the technique in the solution of a standard test problem

Neural Network Applications

Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering area

Integrating Evolutionary Computation with Neural Networks

There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique

Automatic General of a Neural Network Architecture Using Evolutionary Computation

This paper reports the application of evolutionary computation in the automatic generation of a neural network architecture. It is a usual practice to use trial and error to find a suitable neural network architecture. This is not only time consuming but may not generate an optimal solution for a given problem. The use of evolutionary computation is a step towards automation in architecture generation. In this paper a brief introduction to the field is given as well as an implementation of automatic neural network generation using genetic programmin