摘要
针对模糊神经网络中的隶属函数构造和推理规则建立两个难点,提出一种改进的遗传算法完成了隶属函数的自动生成和模糊规则的自动提取。其中采用的动态高斯变异算子,确保了进化初期有效地搜索解空间,进化后期则具有局部精确搜索的性能,提高了收敛速度,得到了精简稳定的模糊神经网络模型,并将其应用到客车车型的自动识别中,结果显示了该方法的有效性。
In view of the two problems of membership function construction and reasoning rules establishment in fuzzy neural network, proposes an improved genetic algorithms to complete membership function automatically generation and the fuzzy rules automatically extraction. The use of dynamic Gaussian variation operator ensures effective search of problem space in early evolution and local search accurately in the later, improves the convergence rate and abtains streamlined and stable fuzzy neural network model. Its application to the automatic recognition of passenger cars shows the effectiveness of the method.
出处
《湖南工业大学学报》
2010年第2期39-42,共4页
Journal of Hunan University of Technology
基金
湖南省教育厅科研基金资助项目(07C507)
关键词
模糊神经网络
隶属度函数
遗传算法
车型识别
fuzzy neural network
membership function
genetic algorithm
vehicle type recognition
