Learning fuzzy control by evolutionary and advantage reinforcements

Chowdhury, M.M. and Li, Y. (1998) Learning fuzzy control by evolutionary and advantage reinforcements. International Journal of Intelligent Systems, 13(10-11), pp. 949-974. (doi:10.1002/(SICI)1098-111X(199810/11)13:10/11<949::AID-INT5>3.0.CO;2-Z)

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Publisher's URL: http://dx.doi.org/10.1002/(SICI)1098-111X(199810/11)13:10/11<949::AID-INT5>3.0.CO;2-Z


In this paper, evolutionary and dynamic programming-based reinforcement learning techniques are combined to form an unsupervised learning scheme for designing autonomous optimal fuzzy logic control systems. A ‘‘messy genetic algorithm’’ and an ‘‘advantage learning’’ scheme are first compared as reinforcement learning paradigms. The messy genetic algorithm enables flexible coding of a fuzzy structure for global optimization, resulting in a coarsely optimized feedforward-type neurofuzzy structure. Local pruning and fine tuning of the neurofuzzy system is then achieved effectively by advantage learning by directly interacting with the environment without the use of a supervisor. The methodology is illustrated and tested in detail through application to two nonlinear control systems.

Item Type:Articles
Keywords:fuzzy control, neural networks, genetic algorithms, evolutionary computation.
Glasgow Author(s) Enlighten ID:Li, Professor Yun
Authors: Chowdhury, M.M., and Li, Y.
Subjects:T Technology > T Technology (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
Research Group:Intelligent Systems
Journal Name:International Journal of Intelligent Systems

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