Diffuse Algorithms for Neural and Neuro-Fuzzy Networks With Applications in Control Engineering and Signal Processing
This title presents new approaches to training neural and neuro-fuzzy networks. The 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 behaviour on a...
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| Main Author | |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Chantilly
Elsevier Science & Technology
2017
Butterworth-Heinemann |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 0128126094 9780128126097 |
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| Abstract | This title presents new approaches to training neural and neuro-fuzzy networks. The 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 behaviour on a finite interval. 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. 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. |
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| AbstractList | This title presents new approaches to training neural and neuro-fuzzy networks. The 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 behaviour on a finite interval. 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. 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. |
| Author | Skorohod, Boris. A |
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| Snippet | This title presents new approaches to training neural and neuro-fuzzy networks. The book is divided into six chapters. Chapter 1 consists of plants models... |
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| SubjectTerms | Algorithms Automatic control Fuzzy systems Neural networks (Computer science) Signal processing |
| Subtitle | With Applications in Control Engineering and Signal Processing |
| TableOfContents | Front Cover -- Diffuse Algorithms for Neural and Neuro-Fuzzy Networks -- Copyright Page -- Contents -- List of Figures -- List of Tables -- Preface -- 1 Introduction -- 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 Plant Models With Time Delays -- 1.1.6 Systems With Partly Unknown Dynamics -- 1.1.7 Recurrent Neural Network -- 1.1.8 Neurocontrol -- 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 Finite Time Interval -- 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 -- 4 Diffuse Neural and Neuro-Fuzzy Networks Training Algorithms -- 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 -- 4.4.1 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 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 -- 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 Networks -- Glossary -- Notations -- Abbreviations -- References -- Index -- Back Cover |
| Title | Diffuse Algorithms for Neural and Neuro-Fuzzy Networks |
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