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One of the biggest challenges of any control paradigm is being able to handle large complex systems under unforeseen uncertainties. A system is called complex here if its dimension (order) is too high and its model (if available) is non-linear and tightly interconnected and information on the system is uncertain such that classical techniques cannot easily handle the problem. Soft computing, a collection of fuzzy logic, neuro-computing, genetic algorithms and genetic programming have proven to be a powerful tool for adding autonomy and semi-autonomony to many complex systems. For such systems the size of soft computing control architecture will be nearly infinite. Examples of complex systems are power networks, space robotic colonies, air traffic control systems, an integrated manufacturing plant, satellite arrays, etc. In this paper, a rule base reduction approach is suggested to manage large inference engines. Notions of rule hierarchy and sensor data fusion are introduced and combined to achieve desirable goals. New paradigms using soft computing approaches, such as multi-objective genetic algorithms, are utilized to design autonomous controllers for a number of application areas. These applications are satellite array formations, robotic agents, wireless cellular systems, water purification systems, etc. The ACE Center's VI-PŽ model for coordinated research teams will also be discussed briefly. Videotape of the experimental implementation will be shown.
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Tom
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59--70
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Bibliogr. 12 poz.,
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- Autonomous Contr. Engineering Center, University of New Mexico, Albuquerque, NM, United States
Bibliografia
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Bibliografia
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bwmeta1.element.baztech-article-BPW4-0002-0022