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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.
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.
Chapter 5 considers 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. 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 practice
- Offers 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
Researchers and graduate students in control, signal processing, machine learning
1.1 Separable models of plants and training problems associated with them
1.1.1 Separable least squares method
1.1.2 Perceptron with one hidden layer
1.1.3 Radial basis neural network
1.1.4 Neuro-fuzzy network
1.1.5 Plants models with time delays
1.1.6 Systems with partly unknown dynamics
1.1.7 Recurrent neural network
1.2 The recursive least squares algorithm with diffuse and soft initializations
1.3 Diffuse initialization of the Kalman filter
2 Diffuse algorithms for estimating parameters of linear regression
2.1 Problem statement
2.2 Soft and diffuse initializations
2.3 Examples of application
2.3.1 Identification of nonlinear dynamic plants
2.3.2 Supervisory control
2.3.3 Estimation with a sliding window
3 Statistical analysis of fluctuations of least squares algorithm on final time interval
3.1 Problem statement
3.2 Properties of normalized root mean square estimation error
3.3 Fluctuations in soft initialization
3.4 Fluctuations in diffuse initialization
4 Diffuse neural and neuro-fuzzy networks training algorithms
4.1 Problem statement
4.2 Training with use of soft and diffuse initializations
4.3 Training in absence of a priori information about parameters of output layer
4.4 Convergence of diffuse training algorithms
4.4.1 Finite training set
4.4.2 Infinite training set
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
4.8.1 Identification of nonlinear static plants
4.8.2 Identification of nonlinear dynamic plants
4.8.3 Example of classification task
5 Diffuse Kalman filter
5.1 Problem statement
5.2 Estimation in the absence or incomplete a priori information about initial conditions
5.3 Estimation with diffuse initialization
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
6 Applications of diffuse algorithms
6.1 Identification of the mobile robot dynamics
6.2 Modeling of hysteretic deformation by neural networks
6.3 Harmonics tracking of electric power network
- No. of pages:
- © Butterworth-Heinemann 2017
- 15th February 2017
- Paperback ISBN:
- eBook ISBN:
Professor Skorohod was born in Dnepropetrovsk, Soviet Union in 1951. He received M.S. and Ph.D in electrical engineering from Sevastopol State University, Sevastopol, in 1973 and 1980, respectively.
From 1980 to 1985, he was a Senior Researcher with the Department of Technical Cybernetics, Sevastopol State University, from 1985 to 1991, he was the Head of the Laboratory, Sevastopol State University and since 1992, he has been professor in the Department of Informatics and control in Technical systems, Sevastopol State University, Sevastopol, Russia. His interests include control systems, intelligent algorithms, neural networks and fuzzy logic.
Informatics and Control in Technical Systems Department, Sevastopol State University, Russia
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