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Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook (Advanced Textbooks in Control and Signal Processin
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A comprehensive introduction to the most popular class of neural network, the multilayer perceptron, showing how it can be used for system identification and control. The book provides readers with a sufficient theoretical background to understand the characteristics of different methods, and to be aware of the pit-falls so as to make the correct decisions in all situations. This is a very application-oriented text that gives detailed and pragmatic recommendations to guide users through the plethora of methods suggested in the literature. Furthermore, it introduces sound working procedures that can lead to efficient neural network solutions. Invaluable to the practitioner and as a textbook in courses with a significant hands-on component.
- Sales Rank: #3059969 in Books
- Brand: Brand: Springer
- Published on: 2013-11-22
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .60" w x 6.10" l, .82 pounds
- Binding: Paperback
- 246 pages
- Used Book in Good Condition
Most helpful customer reviews
1 of 2 people found the following review helpful.
Toolbox for Neural Net System Identification
By Travel_Fan
The book is straight forward and useful. However, I would like to see more examples using MATLAB.
6 of 7 people found the following review helpful.
it works!
By Chester GSD
This is an excellent book. It contains theory and essential exaples for system identification and control based on ANN approach. The authors have also a WEB site where additional information and the toolboxes (MATLAB format) can be found.
The programs work an they are easy to understand and run.
I first tried the toolboxes (the user manuals are included with the toolboxes) and then ordered the book. It is worth having both. I highly recommend it, specially if you are a newbie in ANN but need a fast start.
Cheers!
1 of 1 people found the following review helpful.
all about the perceptron for control systems
By W Boudville
Much of the book might already be familiar to a reader involved with neural networks. The authors give a recap of decades of progress into using multilayer networks in a feedback mode. Key sections of the text discuss the weighting of the nodes and how you can realistically compute these weights.
But the emphasis differs from most existing texts on neural networks. Here, the authors explain how you might control a dynamical system that could exhibit pronounced nonlinearities. The book is pitched towards someone from control systems theory. The latter has been highly developed, to handle both linear and nonlinear systems. However, if you consult standard texts on control systems, neural networks rarely (if ever) garner a mention. This book tries to correct that deficiency.
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