5/23/2012

Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models Review

Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models
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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!

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Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new editionincludes exercises.

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