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by Joos P.L. Vandewalle,B.L. de Moor,Johan A.K. Suykens
Download Artificial Neural Networks for Modelling and Control of Non-Linear Systems fb2
Computer Science
  • Author:
    Joos P.L. Vandewalle,B.L. de Moor,Johan A.K. Suykens
  • ISBN:
    0792396782
  • ISBN13:
    978-0792396789
  • Genre:
  • Publisher:
    Springer; 1996 edition (December 31, 1995)
  • Pages:
    235 pages
  • Subcategory:
    Computer Science
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Johan A. K. Suykens, Joos P. L. Vandewalle, B. de Moor. Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems

Johan A. Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result.

Topics include non-linear system identification, neural optimal control, top-down model based . Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos.

Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems.

Nonlinear system identification using neural networks . From linear to nonlinear dynamical models .  . Preface The topic of this book is the use of artificial neural networks for modelling and control purposes. Parametrization by ANNs. 1 Input/output models. 2 Neural state space models :. 3 Identifiability . The relatively young field of neural control, which started approximately ten years aga with Barto's broomstick balancing experiments, has undergone quite a revolution in recent years. Many methods emerged including optimal control, direct and indirect adaptive control, reinforcement learning, predictive contral etc.

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However, dynamic mo Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems Full description.

In this book we discussed the use of artificial neural networks for modelling and control of nonlinear systems in a systemtheoretical context. The function approximator incorporated in FEL can also be implemented as a B-spline neural network (BSN) instead of an MLP or CMAC. This type of FEL control has been named Learning.

Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems

Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems.

Suykens, Johan A. ISBN: 9780792396789. Personal Author: Suykens, Johan A. Publication Information: Boston : Kluwer Academic Publishers, 1996. Data usage warning: You will receive one text message for each title you selected. Standard text messaging rates apply. Text it to me. Text it to me, and go to next item.

Artificial neural networks possess several properties that make them particularly attractive for applications to modelling and control of complex non-linear systems. Among these properties are their universal approximation ability, their parallel network structure and the availability of on- and off-line learning methods for the interconnection weights. However, dynamic models that contain neural network architectures might be highly non-linear and difficult to analyse as a result. Artificial Neural Networks for Modelling and Control of Non-Linear Systems investigates the subject from a system theoretical point of view. However the mathematical theory that is required from the reader is limited to matrix calculus, basic analysis, differential equations and basic linear system theory. No preliminary knowledge of neural networks is explicitly required. The book presents both classical and novel network architectures and learning algorithms for modelling and control. Topics include non-linear system identification, neural optimal control, top-down model based neural control design and stability analysis of neural control systems. A major contribution of this book is to introduce NLq Theory as an extension towards modern control theory, in order to analyze and synthesize non-linear systems that contain linear together with static non-linear operators that satisfy a sector condition: neural state space control systems are an example. Moreover, it turns out that NLq Theory is unifying with respect to many problems arising in neural networks, systems and control. Examples show that complex non-linear systems can be modelled and controlled within NLq theory, including mastering chaos. The didactic flavor of this book makes it suitable for use as a text for a course on Neural Networks. In addition, researchers and designers will find many important new techniques, in particular NLq Theory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.