Item description for Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models by Oliver Nelles...
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 editionincludes exercises.
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Est. Packaging Dimensions: Length: 9.3" Width: 6.4" Height: 1.6" Weight: 2.8 lbs.
Release Date Dec 12, 2000
ISBN 3540673695 ISBN13 9783540673699
Availability 73 units. Availability accurate as of Jan 20, 2017 05:47.
Usually ships within one to two business days from La Vergne, TN.
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Reviews - What do customers think about Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models?
A excellent comprehensive overview May 22, 2002
This book is excellent compehensive overwiev over many relevant topics that are useful in the wide spectrum of data analysis: Starting with least squares - regression and its variants it comes to nonlinear local and global optimization techniques and even advanced neurofuzzy models. This book is so precious because it explains and compares nearly all useful approaches, their advantages and disadvantages, including numerical and stastical arguments. You can understand it without being a mathematician. But you should be familiar with the following expressions: Gradient, Hessian, Inverse, Covariance Matrix, Estimator
Lots of useful details condensed into just one book! Excellent!
Covers optimization methods Apr 7, 2002
I like the excellent and extensive overview on all kinds of optimization mehtods. You get a good feeling for the differences of linear and nonlinear, local and global optimization techniques.
Very good book Feb 18, 2002
This book covers the nonlinear modeling of static and dynamic processes. It gives a good foundation on the most important neural networks and fuzzy systems with extended treatment of local linear model approaches. It is great for researchers and engineers, who are interested not only in the theoretical background but also in many practically useful tips.
I like the book very much! It is easy to understand because of many figures and explanations.