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| 001 | | | vtls000022446 |
| 003 | | | IISER-K |
| 005 | | | 20140514173300.0 |
| 006 | | | m e d |
| 007 | | | cr bn |||m|||a |
| 008 | | | 120213s2002 paua ob 001 0 eng d |
| 020 | | | \a 9780898717563 (electronic bk.) |
| 020 | | | \z 9780898715057 (print) |
| 020 | | | \z 0898715059 (print) |
| 039 | | 9 | \a 201405141733 \b VLOAD \y 201202131229 \z Siladitya |
| 082 | 0 | 4 | \a 629.8/36 \2 21 |
| 100 | 1 | | \a Lewis, Frank L. |
| 245 | 1 | 0 | \a Neuro-fuzzy control of industrial systems with actuator nonlinearities \h [electronic resource] / \c F.L. Lewis, J. Campos, R. Selmic. |
| 260 | | | \a Philadelphia, Pa. : \b Society for Industrial and Applied Mathematics (SIAM, 3600 Market Street, Floor 6, Philadelphia, PA 19104), \c 2002. |
| 300 | | | \a 1 electronic text (xiv, 244 p.) : \b ill., digital file. |
| 490 | 1 | | \a Frontiers in applied mathematics ; \v 24 |
| 504 | | | \a Includes bibliographical references (p. 233-240) and index. |
| 505 | 0 | | \a Background on Neural Networks and Fuzzy Logic Systems -- Background on Dynamical Systems and Industrial Actuators -- Neurocontrol of Systems with Friction -- Neural and Fuzzy Control of Systems with Deadzones -- Neural Control of Systems with Backlash -- Fuzzy Logic Control of Vehicle Active Suspension -- Neurocontrol Using the Adaptive Critic Architecture -- Neurocontrol of Telerobotic Systems with Time Delays -- Implementation of Neural Network Control Systems -- Appendix A: C Code for Neural Network Friction Controller -- Appendix B: C Code for Continuous-Time Neural Network Deadzone Controller -- Appendix C: C Code for Discrete-Time Neural Network Backlash Controller -- Appendix D: Versatile Real-Time Executive Code for Implementation of Neural Network Backstepping Controller on ATB1000 Tank Gun Barrel. |
| 506 | | | \a Restricted to subscribers or individual electronic text purchasers. |
| 520 | 3 | | \a Neural networks and fuzzy systems are model free control design approaches that represent an advantage over classical control when dealing with complicated nonlinear actuator dynamics. Neuro-Fuzzy Control of Industrial Systems with Actuator Nonlinearities brings neural networks and fuzzy logic together with dynamical control systems. Each chapter presents powerful control approaches for the design of intelligent controllers to compensate for actuator nonlinearities such as time delay, friction, deadzone, and backlash that can be found in all industrial motion systems, plus a thorough development, rigorous stability proofs, and simulation examples for each design. In the final chapter, the authors develop a framework to implement intelligent control schemes on actual systems. |
| 520 | 8 | | \a Rigorous stability proofs are further verified by computer simulations, and appendices contain the computer code needed to build intelligent controllers for real-time applications. Neural networks capture the parallel processing and learning capabilities of biological nervous systems, and fuzzy logic captures the decision-making capabilities of human linguistics and cognitive systems. |
| 530 | | | \a Also available in print version. |
| 538 | | | \a Mode of access: World Wide Web. |
| 538 | | | \a System requirements: Adobe Acrobat Reader. |
| 588 | | | \a Description based of title page of print version. |
| 650 | | 0 | \a Adaptive control systems. |
| 650 | | 0 | \a Fuzzy systems. |
| 650 | | 0 | \a Nonlinear theories. |
| 653 | | | \a Industrial systems |
| 653 | | | \a Adaptive control systems |
| 653 | | | \a Fuzzy systems |
| 653 | | | \a Nonlinear theories |
| 700 | 1 | | \a Campos, J. |
| 700 | 1 | | \a Selmic, R. |
| 710 | 2 | | \a Society for Industrial and Applied Mathematics. |
| 776 | 0 | 8 | \i Print version: \z 0898715059 \z 9780898715057 \w (DLC) 2002017737 |
| 830 | | 0 | \a Frontiers in applied mathematics ; \v 24. |
| 856 | 4 | 0 | \3 SIAM \u http://epubs.siam.org/ebooks/siam/frontiers_in_applied_mathematics/fr24 \y SIAM |
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