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Nonlinear System Identification

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Cover of 'Nonlinear System Identification'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction
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    Chapter 2 Introduction to Optimization
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    Chapter 3 Linear Optimization
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    Chapter 4 Nonlinear Local Optimization
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    Chapter 5 Nonlinear Global Optimization
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    Chapter 6 Unsupervised Learning Techniques
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    Chapter 7 Model Complexity Optimization
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    Chapter 8 Summary of Part I
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    Chapter 9 Introduction to Static Models
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    Chapter 10 Linear, Polynomial, and Look-Up Table Models
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    Chapter 11 Neural Networks
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    Chapter 12 Fuzzy and Neuro-Fuzzy Models
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    Chapter 13 Local Linear Neuro-Fuzzy Models: Fundamentals
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    Chapter 14 Local Linear Neuro-Fuzzy Models: Advanced Aspects
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    Chapter 15 Input Selection for Local Model Approaches
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    Chapter 16 Gaussian Process Models (GPMs)
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    Chapter 17 Summary of Part II
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    Chapter 18 Linear Dynamic System Identification
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    Chapter 19 Nonlinear Dynamic System Identification
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    Chapter 20 Classical Polynomial Approaches
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    Chapter 21 Dynamic Neural and Fuzzy Models
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    Chapter 22 Dynamic Local Linear Neuro-Fuzzy Models
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    Chapter 23 Neural Networks with Internal Dynamics
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    Chapter 24 Applications of Static Models
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    Chapter 25 Applications of Dynamic Models
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    Chapter 26 Design of Experiments
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    Chapter 27 Input Selection Applications
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    Chapter 28 Applications of Advanced Methods
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    Chapter 29 LMN Toolbox
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Title
Nonlinear System Identification
Published by
Springer International Publishing, September 2020
DOI 10.1007/978-3-030-47439-3
ISBNs
978-3-03-047438-6, 978-3-03-047439-3
Authors

Nelles, Oliver

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 21%
Student > Bachelor 6 12%
Student > Master 5 10%
Student > Doctoral Student 2 4%
Unspecified 2 4%
Other 7 13%
Unknown 19 37%
Readers by discipline Count As %
Engineering 19 37%
Computer Science 5 10%
Energy 2 4%
Business, Management and Accounting 1 2%
Earth and Planetary Sciences 1 2%
Other 3 6%
Unknown 21 40%