Fault Diagnosis Method for Analog Electronic Circuits Based on Immune Memory Network Theory

A method of analog circuit fault diagnosis based on immune memory network theory and k nearest neighbor algorithm is proposed.First,immune memory network is used to search the best memory antibody in fault space.In order to equally distribute the memory antibodies in fault space,the memory antibodies in immune memory network are chosen according to concentration.The mechanism of clone and hyper-variation are used to maintain the diversity of antibody,and methods including stimulating and suppressing antibody by concentration and expectation are applied to avoiding immaturity convergence.Second,an improved threshold KNN(k nearest neighbor) algorithm is used to classify the antigen based on the set of best memory antibody in fault space.At last,the band-pass filter is taken as an example,both of data from real circuit and data from software simulation are provided as testing samples to evaluate the diagnosis performance.The experimental results prove that the proposed method for analog circuit fault diagnosis an increase the diagnosis precision.