Multiple Model Approaches To Nonlinear Modelling And Control
This work presents approaches to modelling and control problems arising from conditions of ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide range of methods being combined to provide multiple model solutions. Many component methods are described, as well as discussion of the strategies available for building a successful multiple model approach.
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Multiple Model Approaches To Nonlinear Modelling And Control
This work presents approaches to modelling and control problems arising from conditions of ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide range of methods being combined to provide multiple model solutions. Many component methods are described, as well as discussion of the strategies available for building a successful multiple model approach.
82.99 In Stock
Multiple Model Approaches To Nonlinear Modelling And Control

Multiple Model Approaches To Nonlinear Modelling And Control

Multiple Model Approaches To Nonlinear Modelling And Control

Multiple Model Approaches To Nonlinear Modelling And Control

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Overview

This work presents approaches to modelling and control problems arising from conditions of ever increasing nonlinearity and complexity. It prescribes an approach that covers a wide range of methods being combined to provide multiple model solutions. Many component methods are described, as well as discussion of the strategies available for building a successful multiple model approach.

Product Details

ISBN-13: 9781000162769
Publisher: CRC Press
Publication date: 11/25/2020
Series: Series in Systems and Control
Sold by: Barnes & Noble
Format: eBook
Pages: 360
File size: 106 MB
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About the Author

R Murray-Smith (Edited by) , T. Johansen (Edited by)

Table of Contents

1. Basic Principles: The Operating Regime Approach 2. Modelling: Fuzzy Set Methods for Local Modelling Identification 3. Modelling of Electrically Stimulated Muscle 4. Process Modelling Using a Functional State Approach 5. Markov Mixtures of Experts 6. Active Learning With Mixture Models 7. Local Learning in Local Model Networks 8. Side Effects of Normalising Basic Functions 9. Control: Heterogeneous Control Laws 10. Local Laguerre Models 11. Multiple Model Adaptive Control 12. H Control Using Multiple Linear Models 13. Synthesis of Fuzzy Control Systems Based on Linear Takagi-Sugeno Fuzzy Models
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