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100301s2009 xxk| s |||| 0|eng d |
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|a 9781848825482
|9 978-1-84882-548-2
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|a 10.1007/978-1-84882-548-2
|2 doi
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|a TJ212-225
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|a TJFM
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|a TEC004000
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|a 629.8
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|a Yu, Wen.
|e editor.
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|a Recent Advances in Intelligent Control Systems
|c edited by Wen Yu.
|h [electronic resource] /
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| 264 |
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|a London :
|b Springer London,
|c 2009.
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| 300 |
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|b online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
|2 rdacarrier
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|a text file
|b PDF
|2 rda
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|a Part I: Fuzzy Control -- Fuzzy Control of Large Civil Structures Subjected to Natural Hazards -- Approaches to Robust H-infinity Controller Synthesis of Nonlinear Discrete-time-delay Systems via Takagi-Sugeno Fuzzy Models -- H-infinity Fuzzy Control for Systems with Repeated Scalar Nonlinearities -- Stable Adaptive Compensation with Fuzzy Cerebellar Model Articulation for Overhead Cranes -- Part II: Neural Control -- Estimation and Control of Nonlinear Discrete-time Systems -- Neural Networks Based Probability Density Function Control for Stochastic Sytems -- Hybrid Differential Neural Network Identifier for Partially Uncertain Hybrid Systems -- Real-time Motion Planning of Kinematically Redundant Manipulators Using Recurrent Neural Networks -- Adaptive Neural Control of Uncertain Multi-variable Nonlinear Systems with Saturation and Dead-zone -- Part III: Fuzzy Neural Control.-An Online Self-constructing Fuzzy Neural Network with Restrictive Growth -- Nonlinear System Control Using Functional-link-based Neuro-fuzzy Networks -- An Adaptive Neuro-fuzzy Controller for Robot Navigation -- Part IV: Intelligent Control -- Flow Control of Real-time Multimedia Applications in Best-effort Networks -- Online Synchronous Policy Iteration Method for Optimal Control.
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|a 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. Presenting state-of-the-art research, this collection will be of significant benefit to researchers in automatic control, automation, computer science (especially artificial intelligence) and mechatronics while graduate students and practicing control engineers working with intelligent systems will find it a good source of study material.
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| 650 |
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|a Engineering.
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| 650 |
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|a Artificial intelligence.
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| 650 |
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|a Control engineering systems.
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| 650 |
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|a Industrial engineering.
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1 |
4 |
|a Engineering.
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| 650 |
2 |
4 |
|a Control.
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| 650 |
2 |
4 |
|a Automotive Engineering.
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| 650 |
2 |
4 |
|a Industrial and Production Engineering.
|
| 650 |
2 |
4 |
|a Artificial Intelligence (incl. Robotics).
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| 650 |
2 |
4 |
|a Control , Robotics, Mechatronics.
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| 710 |
2 |
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|a SpringerLink (Online service)
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| 773 |
0 |
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|t Springer eBooks
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| 776 |
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8 |
|i Printed edition:
|z 9781848825475
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| 856 |
4 |
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|u https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-84882-548-2
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| 912 |
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|a ZDB-2-ENG
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| 950 |
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|a Engineering (Springer-11647)
|