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

Rating:
Summary: Covers optimization methods
Review: 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.

Rating:
Summary: Very good book
Review: 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.

Rating:
Summary: A excellent comprehensive overview
Review: 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!