摘要
提出一种结合动态模糊神经网络和混沌优化算法的故障诊断方法,将混沌变量引入模糊神经网络结构和参数的优化搜索.利用混沌优化的动态模糊神经网络建立变压器故障诊断模型,此模型不仅能对模糊规则而且能对输入变量的重要性做出评价,从而使得每个输入变量和模糊规则都可根据误差减少率进行修正.仿真结果表明,混沌动态模糊神经网络算法精度高、迭代步骤少、收敛快,对识别和预测变压器状态具有较高的精度和效率,并可方便有效地应用到其他领域.
An innovative method was present,that combined dynamic fuzzy neural network（DFNN） with chaos optimization for the purpose of Power transformer fault diagnosis.Chaotic variables are applied to the search of FNN structure and all parameters.Chaotic dynamic fuzzy neural network（CDFNN） model was built for power transformer fault diagnosis.This model not only make the evaluation on the importance of the fuzzy rules,but also that of input variables,so that each input variables and fuzzy rules were amended according to the error reduced rate.The experimental results show the precision of the chaotic dynamic fuzzy neural network algorithm is more higher and the iteration steps are more fewer and the speed of convergence is more quicker.It has higher accuracy and efficiency to recognize and predict the state of power transformer,and also effectively and reliably be used in other fields.
出处
《湖南科技大学学报（自然科学版）》
CAS
北大核心
2012年第1期35-39,共5页
Journal of Hunan University of Science And Technology：Natural Science Edition
基金
教育部重点科研基金项目(208098)
湖南省教育厅项目(11C0209)
湖南省科技厅项目(2011SK3207)
关键词
动态模糊神经网络
混沌优化
模糊规则
变压器
故障诊断
dynamic fuzzy neural network
chaos optimization
fuzzy rules
power transformer
fault diagnosis
