Hybrid Intelligent Systems (Studies in Fuzziness and Soft Computing)

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The objective of this edited volume is to offer a general view at the recent conceptual developments of Soft Computing (SC) regarded as a general methodology supporting the design of hybrid systems along with their diversified applications to modeling, simulation and control of non-linear dynamical systems. As of now, SC methodologies embrace neural networks, fuzzy logic, genetic algorithms and chaos theory. Each of these methodologies exhibits well delineated advantages and disadvantages. Interestingly, they have been found useful in solving a broad range of problems. However, many real-world complex problems require a prudent, carefully orchestrated integration of several of these methodologies to fully achieve the required efficiency, accuracy, and interpretability of the solutions. In this edited volume, an overview of SC methodologies, and their applications to modeling, simulation and control, will be given in an introductory paper by the Editors. Then, detailed methods for integrating the different SC methodologies in solving real-world problems will be given in the papers by the other authors in the book. The edited volume will cover a wide spectrum of applications including areas such as: robotic dynamic systems, non-linear plants, manufacturing systems, and time series prediction.