Fuzzy Logic and Intelligent Systems

1. Front Matter

   Pages i-xiv

   PDF

2. Improvement of Fuzzy Control Methods

   • M. Mizumoto

   Pages 1-16

3. Neural Networks and Fuzzy Basis Functions for Functional Approximation

   • Liang Jin, Madan M. Gupta, Peter N. Nikiforuk

   Pages 17-67

4. Ordering Fuzzy Real Quantities

   • Robert Lowen

   Pages 69-83

5. Fuzzy Logic Controllers for Aircraft Flight Control

   • Jia Luo, Edward Lan

   Pages 85-124

6. A Classical Controller: A Special Case of The Fuzzy Logic Controller

   • Thomas Brehm, Kuldip Rattan

   Pages 125-155

7. Real Time Fuzzy Logic Controller for Balancing a Beam-and-Ball System

   • Nowell Godfrey, Hua Li, Yuandong Ji, William Marcy

   Pages 157-185

8. Design of Fuzzy Controllers Based on Frequency And Transient Characteristics

   • Kazuo Tanaka

   Pages 187-212

9. Fuzzy Inference Integrating 3D Measuring System with Adaptive Sensing Strategy

   • Koji Shimojima, Toshio Fukuda, Fumihito Arai, Hideo Matsuura

   Pages 213-243

10. Robot Hand-Eye Coordination Based on Fuzzy Logic

    • Sukir Kumaresan, Hua Li, Xing-Min Li

    Pages 245-269

11. Using FPGA Technique for Design and Implementation of a fuzzy Inference System

    • Donald Hung

    Pages 271-288

12. An Empirical Analysis of One Type of Direct Adaptive Fuzzy Control

    • Hugues Bersini, Vittorio Gorrini

    Pages 289-309

13. Automatic Optimal Design of Fuzzy Systems Based on Universal Approximation and Evolutionary Programming

    • Mattias Nyberg, Yoh-Han Pao

    Pages 311-366

14. Intelligent Control Using Dynamic Neural Networks with Robotic Applications

    • Liang Jin, Madan M. Gupta, Peter N. Nikiforuk

    Pages 367-408

15. Camcorder Operation Judgement Using a Neural Computing Approach

    • Kitahiro Kaneda, Paul Wang

    Pages 409-421

16. Model Reduction and Control of Multistage Flash (MSF) Desalinization Plants

    • Srinivas Ramamurthy, Jayanta Pal, Ak Sinha, Darwish Al Gobaisi, Ganti Rao

    Pages 423-445

17. Back Matter

    Pages 447-450

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One of the attractions of fuzzy logic is its utility in solving many real engineering problems. As many have realised, the major obstacles in building a real intelligent machine involve dealing with random disturbances, processing large amounts of imprecise data, interacting with a dynamically changing environment, and coping with uncertainty. Neural-fuzzy techniques help one to solve many of these problems.
Fuzzy Logic and Intelligent Systems reflects the most recent developments in neural networks and fuzzy logic, and their application in intelligent systems. In addition, the balance between theoretical work and applications makes the book suitable for both researchers and engineers, as well as for graduate students.

• Analysis
• Racter
• evolution
• fuzzy
• fuzzy control
• fuzzy logic
• fuzzy system
• intelligent systems
• linear optimization
• neural network
• programming
• robot
• sensing
• uncertainty

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