| Description |
1 online resource (xvii, 785 pages) |
| Contents |
1. Introduction -- I. Optimization Techniques -- 2. Introduction to Optimization -- 3. Linear Optimization -- 4. Nonlinear Local Optimization -- 5. Nonlinear Global Optimization -- 6. Unsupervised Learning Techniques -- 7. Model Complexity Optimization -- II. Static Models -- 9. Introduction to Static Models -- 10. Linear, Polynomial, and Look-Up Table Models -- 11. Neural Networks -- 12. Fuzzy and Neuro-Fuzzy Models -- 13. Local Linear Neuro-Fuzzy Models: Fundamentals -- 14. Local Linear Neuro-Fuzzy Models: Advanced Aspects -- III. Dynamic Models -- 16. Linear Dynamic System Identification -- 17. Nonlinear Dynamic System Identification -- 18. Classical Polynomial Approaches -- 19. Dynamic Neural and Fuzzy Models -- 20. Dynamic Local Linear Neuro-Fuzzy Models -- 21. Neural Networks with Internal Dynamics -- IV. Applications -- 22. Applications of Static Models -- 23. Applications of Dynamic Models -- 24. Applications of Advanced Methods -- A. Vectors and Matrices -- A.1 Vector and Matrix Derivatives -- A.2 Gradient, Hessian, and Jacobian -- B. Statistics -- B.1 Deterministic and Random Variables -- B.2 Probability Density Function (pdf) -- B.3 Stochastic Processes and Ergodicity -- B.4 Expectation -- B.5 Variance -- B.6 Correlation and Covariance -- B.7 Properties of Estimators -- References |
| Summary |
The book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. Additionally, it provides the reader with the necessary background on optimization techniques making the book self-contained. The emphasis is put on modern methods based on neural networks and fuzzy systems without neglecting the classical approaches. The entire book is written from an engineering point-of-view, focusing on the intuitive understanding of the basic relationships. This is supported by many illustrative figures. Advanced mathematics is avoided. Thus, the book is suitable for last year undergraduate and graduate courses as well as research and development engineers in industries. The new edition includes exercises |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Online resource; title from PDF title page (Ebook Central, viewed November 27, 2017) |
| Subject |
Engineering.
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Computer simulation.
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Mathematical optimization.
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Physics.
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Digital computer simulation.
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System identification.
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engineering.
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simulation.
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physics.
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System identification
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Digital computer simulation
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Computer simulation
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Engineering
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Mathematical optimization
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Physics
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Neuronales Netz
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Fuzzy-Logik
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Nichtlineares dynamisches System
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Systemidentifikation
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Stochastische Optimierung
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| Form |
Electronic book
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| ISBN |
9783662043233 |
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3662043238 |
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