A Combination Of Fuzzy Theory And Genetic-Neural Network Algorithm
Nowadays, the BP network algorithm has achieved a great success and many nonlinear problems can be solved well. However, standard BP network algorithm has some Shortcomings. Such as local minimum, low convergence and oscillation effects etc. GA has a strong macro-search capability. It has some advantages. Such as simple and universal, robust, parallel computing features, so use it to complete the pre-search can overcome the shortcomings of BP. Fuzzy system is good at express peoples experiential knowledge. It can deal with vague information. It can solve the intelligent questions better. Fuzzy clustering methods have been used widely in pattern recognition. Combine fuzzy systems with genetic-neural Network Algorithm not only make the algorithm more efficient, but also to address the intelligent questions better. It has become a hot research.
fuzzy sytems genetic algorithm neural network algorithm genetic-neural network algorithm
Tang Xiaoyi Guo Qingping Wu Peng Song Huijuan
Department of Computer Science and technology, Wuhan university of Technology Wuhan, Hubei, China, 430063
国际会议
香港
英文
639-642
2010-08-12（万方平台首次上网日期，不代表论文的发表时间）