Dynamic and Static Properties of Neural Networks with Feedback
Author : Avner Priel
Publisher :
Page : 358 pages
File Size : 30,99 MB
Release : 1999
Category : Dynamic programming
ISBN :
Author : Avner Priel
Publisher :
Page : 358 pages
File Size : 30,99 MB
Release : 1999
Category : Dynamic programming
ISBN :
Author : Madan Gupta
Publisher : John Wiley & Sons
Page : 752 pages
File Size : 14,14 MB
Release : 2004-04-05
Category : Computers
ISBN : 0471460923
Neuronale Netze haben sich in vielen Bereichen der Informatik und künstlichen Intelligenz, der Robotik, Prozeßsteuerung und Entscheidungsfindung bewährt. Um solche Netze für immer komplexere Aufgaben entwickeln zu können, benötigen Sie solide Kenntnisse der Theorie statischer und dynamischer neuronaler Netze. Aneignen können Sie sie sich mit diesem Lehrbuch! Alle theoretischen Konzepte sind in anschaulicher Weise mit praktischen Anwendungen verknüpft. Am Ende jedes Kapitels können Sie Ihren Wissensstand anhand von Übungsaufgaben überprüfen.
Author : Andrea Crisanti
Publisher :
Page : 232 pages
File Size : 42,90 MB
Release : 1988
Category :
ISBN :
Author : Freddy Rafael Garces
Publisher : Springer Science & Business Media
Page : 180 pages
File Size : 33,84 MB
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 1447100654
Using relevant mathematical proofs and case studies illustrating design and application issues, this book demonstrates this powerful technique in the light of research on neural networks, which allow the identification of nonlinear models without the complicated and costly development of models based on physical laws.
Author : Dawei Dong
Publisher :
Page : 216 pages
File Size : 28,34 MB
Release : 1991
Category : Electronic dissertations
ISBN :
Author : Johan A.K. Suykens
Publisher : Springer Science & Business Media
Page : 242 pages
File Size : 40,48 MB
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 1475724934
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 emTheory, that have applications in control theory, system theory, circuit theory and Time Series Analysis.
Author : Tong Heng Lee
Publisher : World Scientific
Page : 400 pages
File Size : 39,92 MB
Release : 1998
Category :
ISBN : 9789810234522
Introduction; Mathematical background; Dynamic modelling of robots; Structured network modelling of robots; Adaptive neural network control of robots; Neural network model reference adaptive control; Flexible joint robots; task space and force control; Bibliography; Computer simulation; Simulation software in C.
Author : Krzysztof Patan
Publisher : Springer
Page : 209 pages
File Size : 30,20 MB
Release : 2019-03-16
Category : Technology & Engineering
ISBN : 303011869X
Robust and Fault-Tolerant Control proposes novel automatic control strategies for nonlinear systems developed by means of artificial neural networks and pays special attention to robust and fault-tolerant approaches. The book discusses robustness and fault tolerance in the context of model predictive control, fault accommodation and reconfiguration, and iterative learning control strategies. Expanding on its theoretical deliberations the monograph includes many case studies demonstrating how the proposed approaches work in practice. The most important features of the book include: a comprehensive review of neural network architectures with possible applications in system modelling and control; a concise introduction to robust and fault-tolerant control; step-by-step presentation of the control approaches proposed; an abundance of case studies illustrating the important steps in designing robust and fault-tolerant control; and a large number of figures and tables facilitating the performance analysis of the control approaches described. The material presented in this book will be useful for researchers and engineers who wish to avoid spending excessive time in searching neural-network-based control solutions. It is written for electrical, computer science and automatic control engineers interested in control theory and their applications. This monograph will also interest postgraduate students engaged in self-study of nonlinear robust and fault-tolerant control.
Author : Serguey Kashchenko
Publisher : Springer
Page : 260 pages
File Size : 14,18 MB
Release : 2015-10-06
Category : Mathematics
ISBN : 3319198661
This monograph examines in detail models of neural systems described by delay-differential equations. Each element of the medium (neuron) is an oscillator that generates, in standalone mode, short impulses also known as spikes. The book discusses models of synaptic interaction between neurons, which lead to complex oscillatory modes in the system. In addition, it presents a solution to the problem of choosing the parameters of interaction in order to obtain attractors with predetermined structure. These attractors are represented as images encoded in the form of autowaves (wave memory). The target audience primarily comprises researchers and experts in the field, but it will also be beneficial for graduate students.
Author :
Publisher : IOS Press
Page : 344 pages
File Size : 46,82 MB
Release : 2003
Category : Electronic books
ISBN : 9781586033033