| 书目名称 | Nonlinear System Identification | | 副标题 | From Classical Appro | | 编辑 | Oliver Nelles | | 视频video | | | 概述 | Self-contained, no other literature needed.Offers a user-oriented, comprehensive overview of fundamental principles to advanced methods.Provides explanations and terminology from an engineering perspe | | 图书封面 |  | | 描述 | .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 | | 出版日期 | Textbook 2020Latest edition | | 关键词 | Fuzzy and neuro-fuzzy models; Linear optimization; Nonlinear local optimization; Linear and nonlinear d | | 版次 | 2 | | doi | https://doi.org/10.1007/978-3-030-47439-3 | | isbn_softcover | 978-3-030-47441-6 | | isbn_ebook | 978-3-030-47439-3 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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发表于 2025-3-21 16:35:05
