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Nonlinear System Identification

From Classical Approaches to Neural Networks and Fuzzy Models

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2,5(2)Évaluer

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  • 804pages
  • 29 heures de lecture

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Focusing on nonlinear system identification, this book equips engineers and scientists with essential principles and practical techniques for tackling real-world problems. It emphasizes intuitive understanding over formal proofs, using minimal mathematics and abundant illustrations. With a foundation in matrix algebra, signals and systems, and statistics, it also serves as an introduction to linear system identification and optimization methods. The evolution of the field, enhanced by neural networks and fuzzy models, enables broader applicability across diverse systems, despite their unique characteristics.

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Nonlinear System Identification, Oliver Nelles

Langue
Année de publication
2010
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2,5
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