应用RBF网络和D-S证据推理的模拟电路诊断

摘要

提出了一种基于径向基函数网络与证据推理的模拟电路融合诊断方法,以解决模拟电路诊断中由于故障信息缺乏所致的诊断准确性问题,并提高其训练速度.采集多类电路信息,对应于每类特征参量构造一个径向基函数网络,由这多个彼此独立的径向基函数网络来完成故障的初级诊断.再用初级诊断中各子网络的输出结果构造证据体,通过证据融合推理分析,得出最终的故障定位结果.模拟实验结果表明,所提方法对于电路的硬故障与元件参数偏移较小的软故障诊断均有效,其充分挖掘了多类测试信号中的故障信息,提高了诊断结果的准确率.%In order to solve the possible problems in neural-network based analog fault diagnosis including lack of fault information, slow training speed and difficult converge, a novel data-fusion based fault diagnosis approach for analog circuits is presented by using radial basis function (RBF) networks and D-S evidential reasoning. The manifold transducer information and symptoms were utilized in diagnosis. The map from symptom space to fault pattern space was constructed by the separate RBF network for each kind of symptom information. The output results of every RBF network were then aggregated using the D-S evidential reasoning algorithm. Fault location was accomplished based on the synthesis decision regulation. The experimental results show that the proposed approach can effectively combine the evidences to produce a more accurate diagnosis and has the capability to diagnose catastrophic and parametric faults of analog circuits with tolerance.