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Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models [Hardcover]

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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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Item Specifications...

Pages   785
Est. Packaging Dimensions:   Length: 9.3" Width: 6.4" Height: 1.6"
Weight:   2.8 lbs.
Binding  Hardcover
Release Date   Dec 12, 2000
Publisher   Springer
ISBN  3540673695  
ISBN13  9783540673699  

Availability  73 units.
Availability accurate as of May 24, 2017 12:46.
Usually ships within one to two business days from La Vergne, TN.
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More About Oliver Nelles

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1Books > Special Features > New & Used Textbooks > Engineering > Electrical & Electronics
2Books > Special Features > New & Used Textbooks > Sciences > Mathematics
3Books > Subjects > Computers & Internet > Certification Central > General
4Books > Subjects > Computers & Internet > Computer Science > Artificial Intelligence > Neural Networks
5Books > Subjects > Professional & Technical > Engineering > Electrical & Electronics > Electricity Principles
6Books > Subjects > Professional & Technical > Engineering > Electrical & Optical > General
7Books > Subjects > Professional & Technical > Engineering > General
8Books > Subjects > Professional & Technical > Engineering > Industrial, Manufacturing & Operational Syste > Industrial Technology
9Books > Subjects > Professional & Technical > Professional Science > Mathematics > Applied > Linear Programming
10Books > Subjects > Science > General
11Books > Subjects > Science > Mathematics > Applied > Linear Programming

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!

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.


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