Nonlinear System Identification (Record no. 551262)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 05240nam a22006015i 4500 |
| 001 - | |
| control field | 978-3-030-47439-3 |
| 003 - | |
| control field | DE-He213 |
| 005 - | |
| control field | 20211012175144.0 |
| 007 - | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 200909s2020 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9783030474393 |
| -- | 978-3-030-47439-3 |
| 024 7# - | |
| -- | 10.1007/978-3-030-47439-3 |
| -- | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| -- | QC174.7-175.36 |
| 072 #7 - | |
| -- | PBWR |
| -- | bicssc |
| -- | SCI012000 |
| -- | bisacsh |
| -- | PBWR |
| -- | thema |
| -- | PHDT |
| -- | thema |
| 082 04 - | |
| Classification number | 621 |
| -- | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Nelles, Oliver. |
| 245 10 - TITLE STATEMENT | |
| Title | Nonlinear System Identification |
| Remainder of title | From Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes / |
| Statement of responsibility, etc | by Oliver Nelles. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 2nd ed. 2020. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | XXVIII, 1225 p. 670 illus., 179 illus. in color. |
| Other physical details | online resource. |
| 505 0# - | |
| Formatted contents note | Introduction -- Part One Optimization -- Introduction to Optimization -- Linear Optimization -- Nonlinear Local Optimization -- Nonlinear Global Optimization -- Unsupervised Learning Techniques -- Model Complexity Optimization -- Summary of Part 1 -- Part Two Static Models -- Introduction to Static Models -- Linear, Polynomial, and Look-Up Table Models -- Neural Networks -- Fuzzy and Neuro-Fuzzy Models -- Local Linear Neuro-Fuzzy Models: Fundamentals -- Local Linear Neuro-Fuzzy Models: Advanced Aspects -- Input Selection for Local Model Approaches -- Gaussian Process Models (GPMs) -- Summary of Part Two -- Part Three Dynamic Models -- Linear Dynamic System Identification -- Nonlinear Dynamic System Identification -- Classical Polynomial Approaches.-Dynamic Neural and Fuzzy Models -- Dynamic Local Linear Neuro-Fuzzy Models -- Neural Networks with Internal Dynamics -- Part Five Applications -- Applications of Static Models -- Applications of Dynamic Models -- Desing of Experiments -- Input Selection Applications -- Applications of Advanced Methods -- LMN Toolbox -- Vectors and Matrices -- Statistics -- Reference -- Index. |
| 650 #0 - | |
| Topical term or geographic name as entry element | Statistical physics. |
| Topical term or geographic name as entry element | Control engineering. |
| Topical term or geographic name as entry element | Robotics. |
| Topical term or geographic name as entry element | Mechatronics. |
| Topical term or geographic name as entry element | Computational complexity. |
| Topical term or geographic name as entry element | Calculus of variations. |
| Topical term or geographic name as entry element | Computer simulation. |
| Topical term or geographic name as entry element | Applications of Nonlinear Dynamics and Chaos Theory. |
| Topical term or geographic name as entry element | Control and Systems Theory. |
| Topical term or geographic name as entry element | Control, Robotics, Mechatronics. |
| Topical term or geographic name as entry element | Complexity. |
| Topical term or geographic name as entry element | Calculus of Variations and Optimal Control; Optimization. |
| Topical term or geographic name as entry element | Simulation and Modeling. |
| 710 2# - | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 856 40 - | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-030-47439-3 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| -- | author. |
| -- | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 245 10 - TITLE STATEMENT | |
| -- | [electronic resource] : |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2020. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 520 ## - | |
| -- | This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice. Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications. In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems. . |
| -- | https://scigraph.springernature.com/ontologies/product-market-codes/P33020 |
| -- | https://scigraph.springernature.com/ontologies/product-market-codes/T19010 |
| -- | https://scigraph.springernature.com/ontologies/product-market-codes/T19000 |
| -- | https://scigraph.springernature.com/ontologies/product-market-codes/T11022 |
| -- | https://scigraph.springernature.com/ontologies/product-market-codes/M26016 |
| -- | https://scigraph.springernature.com/ontologies/product-market-codes/I19000 |
| 773 0# - | |
| -- | Springer Nature eBook |
| 776 08 - | |
| -- | Printed edition: |
| -- | 9783030474386 |
| -- | Printed edition: |
| -- | 9783030474409 |
| -- | Printed edition: |
| -- | 9783030474416 |
| 912 ## - | |
| -- | ZDB-2-PHA |
| -- | ZDB-2-SXP |
| 950 ## - | |
| -- | Physics and Astronomy (SpringerNature-11651) |
| -- | Physics and Astronomy (R0) (SpringerNature-43715) |
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