| 000 | 05240nam a22006015i 4500 | ||
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| 001 | 978-3-030-47439-3 | ||
| 003 | DE-He213 | ||
| 005 | 20211012175144.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 200909s2020 sz | s |||| 0|eng d | ||
| 020 |
_a9783030474393 _9978-3-030-47439-3 |
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| 024 | 7 |
_a10.1007/978-3-030-47439-3 _2doi |
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| 050 | 4 | _aQC174.7-175.36 | |
| 072 | 7 |
_aPBWR _2bicssc |
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_aSCI012000 _2bisacsh |
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_aPBWR _2thema |
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_aPHDT _2thema |
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| 082 | 0 | 4 |
_a621 _223 |
| 100 | 1 |
_aNelles, Oliver. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aNonlinear System Identification _h[electronic resource] : _bFrom Classical Approaches to Neural Networks, Fuzzy Models, and Gaussian Processes / _cby Oliver Nelles. |
| 250 | _a2nd ed. 2020. | ||
| 264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2020. |
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| 300 |
_aXXVIII, 1225 p. 670 illus., 179 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aIntroduction -- 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. | |
| 520 | _aThis 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. . | ||
| 650 | 0 | _aStatistical physics. | |
| 650 | 0 | _aControl engineering. | |
| 650 | 0 | _aRobotics. | |
| 650 | 0 | _aMechatronics. | |
| 650 | 0 | _aComputational complexity. | |
| 650 | 0 | _aCalculus of variations. | |
| 650 | 0 | _aComputer simulation. | |
| 650 | 1 | 4 |
_aApplications of Nonlinear Dynamics and Chaos Theory. _0https://scigraph.springernature.com/ontologies/product-market-codes/P33020 |
| 650 | 2 | 4 |
_aControl and Systems Theory. _0https://scigraph.springernature.com/ontologies/product-market-codes/T19010 |
| 650 | 2 | 4 |
_aControl, Robotics, Mechatronics. _0https://scigraph.springernature.com/ontologies/product-market-codes/T19000 |
| 650 | 2 | 4 |
_aComplexity. _0https://scigraph.springernature.com/ontologies/product-market-codes/T11022 |
| 650 | 2 | 4 |
_aCalculus of Variations and Optimal Control; Optimization. _0https://scigraph.springernature.com/ontologies/product-market-codes/M26016 |
| 650 | 2 | 4 |
_aSimulation and Modeling. _0https://scigraph.springernature.com/ontologies/product-market-codes/I19000 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer Nature eBook | |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030474386 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030474409 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030474416 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-47439-3 |
| 912 | _aZDB-2-PHA | ||
| 912 | _aZDB-2-SXP | ||
| 950 | _aPhysics and Astronomy (SpringerNature-11651) | ||
| 950 | _aPhysics and Astronomy (R0) (SpringerNature-43715) | ||
| 999 |
_c551262 _d551197 |
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