Artificial Neural Networks for Modelling and Control of by Johan A.K. Suykens, Joos P.L. Vandewalle, B.L. de Moor

By Johan A.K. Suykens, Joos P.L. Vandewalle, B.L. de Moor

Artificial neural networks own a number of homes that cause them to really appealing for purposes to modelling and keep an eye on of complicated non-linear platforms. between those homes are their common approximation skill, their parallel community constitution and the provision of on- and off-line studying equipment for the interconnection weights. in spite of the fact that, dynamic versions that comprise neural community architectures can be hugely non-linear and tough to examine consequently. Artificial Neural Networks for Modelling andControl of Non-Linear Systems investigates the topic from a approach theoretical standpoint. but the mathematical concept that's required from the reader is restricted to matrix calculus, uncomplicated research, differential equations and simple linear method concept. No initial wisdom of neural networks is explicitly required.
The e-book provides either classical and novel community architectures and studying algorithms for modelling and keep watch over. issues comprise non-linear method id, neural optimum regulate, top-down version established neural keep an eye on layout and balance research of neural keep an eye on structures. an incredible contribution of this e-book is to introduce NLqTheory as an extension in the direction of glossy regulate concept, so one can learn and synthesize non-linear structures that comprise linear including static non-linear operators that fulfill a region situation: neural kingdom area keep an eye on platforms are an instance. additionally, it seems that NLq Theory is unifying with recognize to many difficulties coming up in neural networks, structures and keep watch over. Examples convey that complicated non-linear structures may be modelled and regulated inside of NLq conception, together with gaining knowledge of chaos.
The didactic style of this e-book makes it appropriate to be used as a textual content for a direction on Neural Networks. additionally, researchers and architects will locate many vital new strategies, particularly NLq Theory, that experience purposes up to speed idea, process thought, circuit concept and Time sequence Analysis.

Show description

Read Online or Download Artificial Neural Networks for Modelling and Control of Non-Linear Systems PDF

Similar microprocessors & system design books

Embedded Systems Architecture: A Comprehensive Guide for Engineers and Programmers

This entire textbook offers a wide and in-depth evaluate of embedded platforms structure for engineering scholars and embedded structures pros. The booklet is well-suited for undergraduate embedded structures classes in electronics/electrical engineering and engineering expertise (EET) departments in universities and faculties, and for company education of staff.

Control and Scheduling Codesign: Flexible Resource Management in Real-Time Control Systems (Advanced Topics in Science and Technology in China)

With emphasis on versatile source administration in networked and embedded real-time regulate structures working in dynamic environments with uncertainty, keep an eye on and Scheduling Codesign is dedicated to the mixing of regulate with computing and communique. It covers the authors' fresh and unique examine effects inside a unified framework of suggestions scheduling.

Logic Synthesis for Compositional Microprogram Control Units

The keep watch over unit is without doubt one of the most vital components of any electronic process. typically, keep an eye on devices have an abnormal constitution, which makes the processing in their common sense circuits layout very subtle. One real way to optimise such features because the measurement or functionality of keep an eye on devices is to conform their buildings to the actual homes of interpreted keep an eye on algorithms.

Extra info for Artificial Neural Networks for Modelling and Control of Non-Linear Systems

Sample text

If and only if there is only a smaIl probability that they differ significantly and fand gare Jl-equivalent if Pp. (f, g) = O. 4 [Horllik, 1989]. -dense in M r . o This means that regardless of the dimension r of the input space and for any squashing function '1/), a feedforward neural network with one hidden layer can approximate any continuous function arbitrarily weIl in the Pp. metric. In the proof of Hornik's Theorems a central role is played by the Stone-Weierstrass theorem. 26 Chapter 2 Artificial neural networks In addition to the previous Theorems, more refined Theorems were formulated by Hornik (1991).

Radial basis function networks on the other hand possess besides their universal approximation ability also a best representation property. The theory of RBF networks is closely related to the theory of splines. A suboptimallearning algorithm consists of first placing the centers of the Gaussians and secondly applying linear regression in order to find the output weights. Chapter 3 Nonlinear system identification using neural networks In this Chapter we treat the problem of nonlinear system identification using neural networks.

L. 29) = 1, ... , nh and s 1, ... , n x and v; the r-th row of the interconwith indices r nection matrix V. T. 31 ) Ek Wk A-l\11Tk + OI 48 Chapter 3 Nonlinear system identification Here 0" is a positive scalar and the step size St is determined by line search along the direction 'fJ at each iteration. This algorithm is known to converge at least to a local minimum. 4 on nonlinear optimization. Besides off-line (batch) algorithms also recursive methods are available for on-line identification. 32) fk where Bk is the estimate of B at time k and 'Yk is the gain at time k.

Download PDF sample

Rated 4.60 of 5 – based on 26 votes