Diffuse Algorithms for Neural and Neuro-Fuzzy Networks

Diffuse Algorithms for Neural and Neuro-Fuzzy Networks presents new approaches to training of neural and neuro-fuzzy networks. This book is divided into 6 chapters. Chapter 1 consists of plants models reviews, problems statements and known results that are relevant to the subject matter of this book.In Chapter 2 is considered the RLS behavior on a finite interval. The theoretical results are illustrated by examples of solving problems of identification, control and signal processing. Properties of the bias, the matrix of second-order moments and the normalized average squared error of the RLS algorithm on a finite time interval are studied in Chapter 3. Chapter 4 deals with the problem of multilayer neural and neuro-fuzzy networks training with simultaneous estimation of the hidden and output layers parameters. The theoretical results are illustrated with the examples of pattern recognition, identification of nonlinear static and dynamic plants. In Chapter 5, the estimation problem of the state and the parameters of the discrete dynamic plants in the absence of a priori statistical information about initial conditions or its incompletion is considered. The Kalman filter and the extended Kalman filter diffuse analogues are obtained. Finally, Chapter 6 provides examples of the use of diffuse algorithms for solving problems in various engineering applications. This book is ideal for researchers and graduate students in control, signal processing, and machine learning. Presents a new approach to training which can be applied to solve the control, identification, signal processing and classification problems arising in practiceOffers an improvement from the existing learning techniques in Control, Robotics and Machine Learning.Provides examples of the use of diffuse algorithms for solving problems in various engineering applications.