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Item Details
Title: HIGH-LEVEL FEEDBACK CONTROL WITH NEURAL NETWORKS
By: Young Ho Kim, Frank L. Lewis
Format: Hardback

List price: £85.00


We currently do not stock this item, please contact the publisher directly for further information.

ISBN 10: 9810233760
ISBN 13: 9789810233761
Publisher: WORLD SCIENTIFIC PUBLISHING CO PTE LTD
Pub. date: 29 September, 1998
Series: World Scientific Series In Robotics And Intelligent Systems 21
Pages: 228
Description: This work seeks to bridge the gap between feedback control and artificial intelligence. It provides design techniques for "high-level" neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels.
Synopsis: Complex industrial or robotic systems with uncertainty and disturbances are difficult to control. As system uncertainty or performance requirements increase, it becomes necessary to augment traditional feedback controllers with additional feedback loops that effectively "add intelligence" to the system. Some theories of artificial intelligence (AI) are now showing how complex machine systems should mimic human cognitive and biological processes to improve their capabilities for dealing with uncertainty.This book bridges the gap between feedback control and AI. It provides design techniques for "high-level" neural-network feedback-control topologies that contain servo-level feedback-control loops as well as AI decision and training at the higher levels. Several advanced feedback topologies containing neural networks are presented, including "dynamic output feedback", "reinforcement learning" and "optimal design", as well as a "fuzzy-logic reinforcement" controller. The control topologies are intuitive, yet are derived using sound mathematical principles where proofs of stability are given so that closed-loop performance can be relied upon in using these control systems. Computer-simulation examples are given to illustrate the performance.
Publication: Singapore
Imprint: World Scientific Publishing Co Pte Ltd
Returns: Returnable
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COOPERATIVE CONTROL OF MULTI-AGENT SYSTEMS (HB)
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DETERMINISTIC LEARNING THEORY FOR IDENTIFICATION, RECOGNITION, AND CONTROL (HB)
INTEGRAL AND INVERSE REINFORCEMENT LEARNING FOR OPTIMAL CONTROL SYSTEMS AND GAMES (HB)
INTELLIGENT DIAGNOSIS AND PROGNOSIS OF INDUSTRIAL NETWORKED SYSTEMS
INTELLIGENT DIAGNOSIS AND PROGNOSIS OF INDUSTRIAL NETWORKED SYSTEMS (HB)
INTELLIGENT DIAGNOSIS AND PROGNOSIS OF INDUSTRIAL NETWORKED SYSTEMS (PB)
INTELLIGENT FAULT DIAGNOSIS AND PROGNOSIS FOR ENGINEERING SYSTEMS
INTELLIGENT FAULT DIAGNOSIS AND PROGNOSIS FOR ENGINEERING SYSTEMS (HB)
MANUFACTURING SYSTEMS CONTROL DESIGN (HB)
MANUFACTURING SYSTEMS CONTROL DESIGN (PB)
NEURAL NETWORK CONTROL OF ROBOTS AND NONLINEAR SYSTEMS (HB)
NEURO-FUZZY CONTROL OF INDUSTRIAL SYSTEMS WITH ACTUATOR NONLINEARITIES (HB)
NONLINEAR H2/H-INFINITY CONSTRAINED FEEDBACK CONTROL (HB)
NONLINEAR H2/H-INFINITY CONSTRAINED FEEDBACK CONTROL (PB)
OPTIMAL ADAPTIVE CONTROL AND DIFFERENTIAL GAMES BY REINFORCEMENT LEARNING PRINCIPLES
OPTIMAL ADAPTIVE CONTROL AND DIFFERENTIAL GAMES BY REINFORCEMENT LEARNING PRINCIPLES (HB)
OPTIMAL AND ROBUST ESTIMATION
OPTIMAL AND ROBUST ESTIMATION (HB)
OPTIMAL CONTROL
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REINFORCEMENT LEARNING AND APPROXIMATE DYNAMIC PROGRAMMING FOR FEEDBACK CONTROL
REINFORCEMENT LEARNING AND APPROXIMATE DYNAMIC PROGRAMMING FOR FEEDBACK CONTROL
REINFORCEMENT LEARNING AND APPROXIMATE DYNAMIC PROGRAMMING FOR FEEDBACK CONTROL
REINFORCEMENT LEARNING AND APPROXIMATE DYNAMIC PROGRAMMING FOR FEEDBACK CONTROL
REINFORCEMENT LEARNING AND APPROXIMATE DYNAMIC PROGRAMMING FOR FEEDBACK CONTROL (HB)
ROBOT MANIPULATOR CONTROL
ROBOT MANIPULATOR CONTROL
ROBOT MANIPULATOR CONTROL
ROBOT MANIPULATOR CONTROL
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