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.For very narrow PDFa#39;s, the network approaches a nearest neighbor classifier. Optimum ... Category membership is dummy-coded as 0 for the noisy sine function exemplars, and 1 for the noisy cosine function exemplars. A total of 10 suchanbsp;...
|Title||:||Neural Network Data Analysis Using SimulnetTM|
|Author||:||Edward J. Rzempoluck|
|Publisher||:||Springer Science & Business Media - 2012-12-06|