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Neural Systems for Control

1997-02-24 Computers
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Release : 1997-02-24
Genre : Computers
Kind : eBook
Book Rating : 391/5 ( reviews)

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Book Synopsis Neural Systems for Control by : Omid Omidvar

Download or read book Neural Systems for Control written by Omid Omidvar. This book was released on 1997-02-24. Available in PDF, EPUB and Kindle. Book excerpt: Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern control theory. The book covers such important new developments in control systems such as intelligent sensors in semiconductor wafer manufacturing; the relation between muscles and cerebral neurons in speech recognition; online compensation of reconfigurable control for spacecraft aircraft and other systems; applications to rolling mills, robotics and process control; the usage of past output data to identify nonlinear systems by neural networks; neural approximate optimal control; model-free nonlinear control; and neural control based on a regulation of physiological investigation/blood pressure control. All researchers and students dealing with control systems will find the fascinating Neural Systems for Control of immense interest and assistance. Focuses on research in natural and artifical neural systems directly applicable to contol or making use of modern control theory Represents the most up-to-date developments in this rapidly growing application area of neural networks Takes a new and novel approach to system identification and synthesis

Neural Networks for Control

1995 Computers
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Release : 1995
Genre : Computers
Kind : eBook
Book Rating : 617/5 ( reviews)

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Book Synopsis Neural Networks for Control by : W. Thomas Miller

Download or read book Neural Networks for Control written by W. Thomas Miller. This book was released on 1995. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks for Control brings together examples of all the most important paradigms for the application of neural networks to robotics and control. Primarily concerned with engineering problems and approaches to their solution through neurocomputing systems, the book is divided into three sections: general principles, motion control, and applications domains (with evaluations of the possible applications by experts in the applications areas.) Special emphasis is placed on designs based on optimization or reinforcement, which will become increasingly important as researchers address more complex engineering challenges or real biological-control problems.A Bradford Book. Neural Network Modeling and Connectionism series

Adaptive Control with Recurrent High-order Neural Networks

2012-12-06 Computers
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Release : 2012-12-06
Genre : Computers
Kind : eBook
Book Rating : 853/5 ( reviews)

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Book Synopsis Adaptive Control with Recurrent High-order Neural Networks by : George A. Rovithakis

Download or read book Adaptive Control with Recurrent High-order Neural Networks written by George A. Rovithakis. This book was released on 2012-12-06. Available in PDF, EPUB and Kindle. Book excerpt: The series Advances in Industrial Control aims to report and encourage technology transfer in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. New theory, new controllers, actuators, sensors, new industrial processes, computer methods, new applications, new philosophies ... , new challenges. Much of this development work resides in industrial reports, feasibility study papers and the reports of advanced collaborative projects. The series offers an opportunity for researchers to present an extended exposition of such new work in all aspects of industrial control for wider and rapid dissemination. Neural networks is one of those areas where an initial burst of enthusiasm and optimism leads to an explosion of papers in the journals and many presentations at conferences but it is only in the last decade that significant theoretical work on stability, convergence and robustness for the use of neural networks in control systems has been tackled. George Rovithakis and Manolis Christodoulou have been interested in these theoretical problems and in the practical aspects of neural network applications to industrial problems. This very welcome addition to the Advances in Industrial Control series provides a succinct report of their research. The neural network model at the core of their work is the Recurrent High Order Neural Network (RHONN) and a complete theoretical and simulation development is presented. Different readers will find different aspects of the development of interest. The last chapter of the monograph discusses the problem of manufacturing or production process scheduling.

Neural Systems for Control

1997 Computers
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Release : 1997
Genre : Computers
Kind : eBook
Book Rating : 303/5 ( reviews)

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Book Synopsis Neural Systems for Control by : Omid Omidvar

Download or read book Neural Systems for Control written by Omid Omidvar. This book was released on 1997. Available in PDF, EPUB and Kindle. Book excerpt: "Neutral Systems for Control" represents the most up-to-date developments in the rapidly growing application area of neural networks and focuses on research in natural and artificial neural systems directly applicable to control or making use of modern control theory. All researchers and students dealing with control systems will find the book of immense interest and assistance.

Differential Neural Networks for Robust Nonlinear Control

2001 Computers
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Release : 2001
Genre : Computers
Kind : eBook
Book Rating : 242/5 ( reviews)

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Book Synopsis Differential Neural Networks for Robust Nonlinear Control by : Alexander S. Poznyak

Download or read book Differential Neural Networks for Robust Nonlinear Control written by Alexander S. Poznyak. This book was released on 2001. Available in PDF, EPUB and Kindle. Book excerpt: This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.).

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