【摘要】 为解决BP模糊神经网络(FNN)在绝缘内部缺陷的局部放电识别中存在收敛速度慢、陷于局部极小值及常规的遗传算法(GA)处理多输入、多输出网络时易出现“早熟”现象等问题,采用了通过共享函数技术和拥挤技术来维持种群多样性的小生境遗传算法(NGA)识别局部放电。以NGA训练的模糊网络模型,在收敛速度、寻优能力和辨别效果方面均优于BP、GA算法下的模型,符合缺陷识别的工程需要。更多还原

【Abstract】 There are several problems when using back propagation (BP) fuzzy neutral network(FNN) to identify the partial discharge of insulation, such as low speed in convergence, being immersed in local smallest value, and so on. Using the conventional Genetic Algorithm (GA) to disposal multi- input and multi- output network may possibly lead to "premature" phenomena. This composition settles the above problems by using the Niche Genetic Algorithm (NGA) which is based on sharing function technique and crowding technique. The FNN constituted by NGA, whose convergence rate, optimizing capability and distinguish effect are superior to those of GA and BP, is accorded with project need of discriminating.更多还原