This book and sofwtare package provide a complement to the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural networks. Neural network functions discussed include multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalized regression neural networks, learning quantizer networks, and self-organizing feature maps. In an easy-to-use, Windows-based environment it offers a wide range of data analytic tools which are not usually found together: these include genetic algorithms, probabilistic networks, as well as a number of related techniques that support these - notably, fractal dimension analysis, coherence analysis, and mutual information analysis. The text presents a number of worked examples and case studies using Simulnet, the software package which comes with the book. Readers are assumed to have a basic understanding of computers and elementary mathematics. With this background, a reader will find themselves quickly conducting sophisticated hands-on analyses of data sets.

This book and software package complements the traditional data analysis tools already widely available. It presents an introduction to the analysis of data using neural network functions such as multilayer feed-forward networks using error back propagation, genetic algorithm-neural network hybrids, generalised regression neural networks, learning quantizer networks, and self-organising feature maps. In an easy-to-use, Windows-based environment it offers a wide range of data analytic tools which are not usually found together: genetic algorithms, probabilistic networks, as well as a number of related techniques that support these. Readers are assumed to have a basic understanding of computers and elementary mathematics, allowing them to quickly conduct sophisticated hands-on analyses of data sets.