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A Dual Fuzzy Neuro Controller Using Genetic Algorithm in Civil Aviation Intelligent Landing System

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A kind of dual fuzzy neuro control algorithm (DFNC) for civil aviation aircraft intelligent landing system is developed in this paper. The DFNC algorithm uses Genetic Algorithm (GA) as the optimization technique and chooses best control performance of approaching and landing to be the optimization object. Real-time recurrent learning (RTRL) is applied to train the RNN that uses gradient-descent of the error function with respect to the weights to perform the weights updates. Convergence analysis of system error is provided. The control scheme utilizes five crossover methods of GAs to search optimal control parameters. Simulations show that the proposed intelligent controller has better performance than the conventional controller.
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Document Type: Research Article

Publication date: March 1, 2012

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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