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)

Abstract

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.
• Status: Published
• Refereed: Yes
• 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
• ISSN: 0884-8173