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Neural Network Control of Nonlinear Discrete-Time Systems (Automation and Control Engineering), by Jagannathan Sarangapani
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Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems.
Borrowing from Biology
Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts.
Progressive Development
After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware.
Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.
- Sales Rank: #3885904 in Books
- Brand: Brand: CRC Press
- Published on: 2006-04-24
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x 6.00" w x 1.50" l, 2.10 pounds
- Binding: Hardcover
- 624 pages
- Used Book in Good Condition
Most helpful customer reviews
1 of 1 people found the following review helpful.
Was this book proof-read at all?
By Michael Sperrazzo
I'm probably going to find a different book to learn from. The writing is good, there are many figures to explain concepts, and there are so - many - errors. Wow. There is no errata for this book that I can find, either.
I am on page 20 right now, and I've come across at least a dozen mistakes - not small errors, like a mistaken key press, but numbered equations with errors such as "y = x" being printed where "y = f(x)" was intended, random terms being forgotten in numbered equations, and missing blocks in system block diagrams. Most are obvious after doing a double take, but I don't think I want to deal with this every other page - especially when the equations and diagrams start to become more complicated.
0 of 1 people found the following review helpful.
Highly recommended
By tangent
No fluff here, this book is chock full of valuable and insightful information on the application of recurrent closed loop neural nets for estimating and controlling nonlinear time series.
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