کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10323019 660888 2005 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
GA-TSKfnn: Parameters tuning of fuzzy neural network using genetic algorithms
چکیده انگلیسی
Fuzzy logic allows mapping of an input space to an output space. The mechanism for doing this is through a set of IF-THEN statements, commonly known as fuzzy rules. In order for a fuzzy rule to perform well, the fuzzy sets must be carefully designed. A major problem plaguing the effective use of this approach is the difficulty of automatically and accurately constructing the membership functions. Genetic Algorithms (GAs) is a technique that emulates biological evolutionary theories to solve complex optimization problems. Genetic Algorithms provide an alternative to our traditional optimization techniques by using directed random searches to derive a set of optimal solutions in complex landscapes. GAs literally searches towards the two end of the search space in order to determine the optimum solutions. Populations of candidate solutions are evaluated to determine the best solution. In this paper, a hybrid system combining a Fuzzy Inference System and Genetic Algorithms-a Genetic Algorithms based Takagi-Sugeno-Kang Fuzzy Neural Network (GA-TSKfnn) is proposed to tune the parameters in the Takagi-Sugeno-Kang fuzzy neural network. The aim is to reduce unnecessary steps in the parameters sets before they can be fed into the network. Modifications are made to various layers of the network to enhance the performance. The proposed GA-TSKfnn is able to achieve higher classification rate when compared against traditional neuro-fuzzy classifiers.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 29, Issue 4, November 2005, Pages 769-781
نویسندگان
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