Diffuse Algorithms for Neural and Neuro Fuzzy Networks With Applications in Control Engineering and Signal Processing Boris 0128126108 9780128126103

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Product details:

• ISBN 10: 0128126108
• ISBN 13: 9780128126103
• Author:  Boris A. Skorohod

Diffuse Algorithms for Neural and Neuro-Fuzzy Networks: With Applications in Control Engineering and Signal Processing presents new approaches to training neural and neuro-fuzzy networks. This book is divided into six chapters. Chapter 1 consists of plants models reviews, problems statements, and known results that are relevant to the subject matter of this book. Chapter 2 considers the RLS behavior on a finite interval. The theoretical results are illustrated by examples of solving problems of identification, control, and signal processing.

Table of contents:

Chapter 1. Introduction
Abstract
1.1 Separable Models of Plants and Training Problems Associated With Them
1.2 The Recursive Least Squares Algorithm With Diffuse and Soft Initializations
1.3 Diffuse Initialization of the Kalman Filter
Chapter 2. Diffuse Algorithms for Estimating Parameters of Linear Regression
Abstract
2.1 Problem Statement
2.2 Soft and Diffuse Initializations
2.3 Examples of Application
Chapter 3. Statistical Analysis of Fluctuations of Least Squares Algorithm on Finite Time Interval
Abstract
3.1 Problem Statement
3.2 Properties of Normalized Root Mean Square Estimation Error
3.3 Fluctuations of Estimates Under Soft Initialization with Large Parameters
3.4 Fluctuations Under Diffuse Initialization
3.5 Fluctuations with Random Inputs
Chapter 4. Diffuse Neural and Neuro-Fuzzy Networks Training Algorithms
Abstract
4.1 Problem Statement
4.2 Training With the Use of Soft and Diffuse Initializations
4.3 Training in the Absence of a Priori Information About Parameters of the Output Layer
4.4 Convergence of Diffuse Training Algorithms
4.5 Iterative Versions of Diffuse Training Algorithms
4.6 Diffuse Training Algorithm of Recurrent Neural Network
4.7 Analysis of Training Algorithms With Small Noise Measurements
4.8 Examples of Application
Chapter 5. Diffuse Kalman Filter
Abstract
5.1 Problem Statement
5.2 Estimation With Diffuse Initialization
5.3 Estimation in the Absence or Incomplete a Priori Information About Initial Conditions
5.4 Systems State Recovery in a Finite Number of Steps
5.5 Filtering With the Sliding Window
5.6 Diffuse Analog of the Extended Kalman Filter
5.7 Recurrent Neural Network Training
5.8 Systems With Partly Unknown Dynamics
Chapter 6. Applications of Diffuse Algorithms
Abstract
6.1 Identification of the Mobile Robot Dynamics
6.2 Modeling of Hysteretic Deformation by Neural Networks
6.3 Harmonics Tracking of Electric Power Networks

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