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Download Computationally Intelligent Hybrid Systems: The Fusion of by Seppo J. Ovaska PDF

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By Seppo J. Ovaska

Content material:
Chapter 1 creation to Fusion of sentimental Computing and tough Computing (pages 5–30): Seppo J. Ovaska
Chapter 2 common version for Large?Scale Plant software (pages 35–55): Akimoto Kamiya
Chapter three Adaptive Flight keep an eye on: gentle Computing with challenging Constraints (pages 61–88): Richard E. Saeks
Chapter four Sensorless keep watch over of Switched Reluctance cars (pages 93–124): Adrian David Cheok
Chapter five Estimation of Uncertainty Bounds for Linear and Nonlinear strong regulate (pages 129–164): Gregory D. Buckner
Chapter 6 oblique On?Line software put on tracking (pages 169–198): Bernhard Sick
Chapter 7 Predictive Filtering equipment for strength platforms purposes (pages 203–240): Seppo J. Ovaska
Chapter eight Intrusion Detection for computing device protection (pages 245–272): Sung?Bae Cho and Sang?Jun Han
Chapter nine Emotion producing approach on Human–Computer Interfaces (pages 277–312): Kazuya Mera and Takumi Ichimura
Chapter 10 advent to medical facts Mining: Direct Kernel tools and purposes (pages 317–362): Mark J. Embrechts, Boleslaw Szymanski and Karsten Sternickel
Chapter eleven world-wide-web utilization Mining (pages 367–396): Ajith Abraham

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Additional resources for Computationally Intelligent Hybrid Systems: The Fusion of Soft Computing and Hard Computing

Example text

Our environment, society, economy, transportation, power grid, telephone network, and Internet are all representative large-scale systems. Such complex systems can be further divided into multiple subsystems [2]. Chapter 2 of the present book, "General Model for Large-Scale Plant Application," is authored by Akimoto Kamiya. It considers a vital subclass of largescale systems—that is, large-scale plants. The word "plant" is defined in The Random House Dictionary of the English Language as the complete equipment or apparatus for a particular mechanical process or operation.

551-554. 29. S. Omatu, T. Iwasa, and M. Yoshioka, "Skill-Based PID Control by Using Neural Networks," Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, San Diego, CA, Oct. 1998, pp. 1972-1977. 30. Y. -Y. Chow, "Analysis of Training Neural Compensation Model for System Dynamics Modeling," Proceedings of the International Joint Conference on Neural Networks, Washington, DC, July 2001, pp. 1250-1255. 31. R. Fletcher and M. J. D. Powell, "A Rapidly Convergent Descent Method for Minimization," Computer Journal 6, 163-168 (1963).

Rule-based approach Tolerance for imprecision, partial truth, and uncertainty Capability to approximate any continuous function or system Possibility to utilize human intuition NN . . . Black box approach Massively parallel distributed structure Supervised or unsupervised learning from data Generalization ability Capability to approximate any continuous function or system real-world applications, because the conceptual structure of HC is overly precise in relation to the imprecision of the world around us, and there is a growing demand for computationally intelligent hybrid systems.

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