Neuro-Fuzzy Clustering of Distorted Data Using Cat Swarm Optimization

Nowadays computational intelligence technologies often and quite successfully are used in solving complex problems that as a rule do not have an analytical solution. Today these technologies and especially artificial neural networks (ANN) are widely used to solve various problems of signal processing optimization optimal and adaptive control pattern recognition identification time- series prediction etc. At the same time the described approaches to data recovery are workable only in cases when the initial data are set a priori and the “object-property” table or time series has a fixed number of observations i.e. do not change during processing. This book is devoted to the development and study of methods for dynamic data mining containing missing and distorted observations. The main feature of data mining methods is to establish the presence and nature of hidden patterns in data whereas traditional methods mainly deal with parametric evaluation of already established patterns.