Recent advances in intelligent control systems /

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Bibliographic Details
Imprint:Dordrecht ; New York : Springer, c2009.
Description:1 online resource (xviii, 376 p.) : ill. (some col.)
Language:English
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/8889658
Hidden Bibliographic Details
Other authors / contributors:Yu, Wen, robotics engineer.
ISBN:9781848825482
184882548X
9781848825475 (hbk)
1848825471 (hbk)
Notes:Includes bibliographical references and index.
Description based on print version record.
Summary:Intelligent control -- control based on fuzzy logic and neural networks -- has changed the face of industrial control engineering whether in terms of autonomous spacecraft operation, exploratory robots or increasing the profitability of mineral-processing or steel-rolling plants. Recent Advances in Intelligent Control Systems gathers contributions from workers around the world and presents them in four categories according to the style of control employed: fuzzy control; neural control; fuzzy neural control; and intelligent control. The contributions illustrate the interdisciplinary antecedents of intelligent control and contrast its results with those of more traditional control methods. A variety of design examples, drawn primarily from robotics and mechatronics but also representing process and production engineering, large civil structures, network flows, and others, provide instances of the successful application of computational intelligence for control.
Other form:Print version: Recent advances in intelligent control systems. Dordrecht ; New York : Springer, c2009 9781848825475 1848825471

MARC

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505 0 |a Fuzzy Control of Large Civil Structures Subjected to Natural Hazards; Approaches to Robust H8 Controller Synthesis of Nonlinear Discrete-time-delay Systems via Takagi-Sugeno Fuzzy Models; H8 Fuzzy Control for Systems with Repeated Scalar Nonlinearities; Stable Adaptive Compensation with Fuzzy Cerebellar Model Articulation Controller for Overhead Cranes; Estimation and Control of Nonlinear Discrete-time Systems; Neural Networks Based Probability Density Function Control for Stochastic Systems; Hybrid Differential Neural Network Identifier for Partially Uncertain Hybrid Systems. 
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