This article presents a new method for learning and tuning a fuzzy logic controller automatically. A reinforcement learning and a genetic algorithm are used in conjunction with a multilayer neural network model of a fuzzy logic controller, which can automatically generate the fuzzy control rules and
A real-time approach to array control based on a learned genetic algorithm
β Scribed by Salvatore Caorsi; Massimo Donelli; Andrea Lommi; Andrea Massa
- Publisher
- John Wiley and Sons
- Year
- 2003
- Tongue
- English
- Weight
- 409 KB
- Volume
- 36
- Category
- Article
- ISSN
- 0895-2477
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β¦ Synopsis
Abstract
In the framework of wireless communications, it is mandatory to guarantee the reliability of the receiver by assuring good quality at the endβuser, in order to successfully decode transmitted signals. To this end, a method for the design of adaptive antenna arrays is described in this paper. The proposed procedure, based on a customized genetic algorithm, allows realβtime control of the receiver's performance by acting on the discretized phase coefficients of the array elements. As a result, at the receiver, multiple and alternating interferences are considerably reduced with respect to the desired signal. In order to validate the proposed strategy, selected numerical results, concerning various environment conditions, are presented and deeply discussed. Β© 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 36: 235β238, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10731
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