Artificial Immune Algorithm-based Analog Circuit Fault Diagnosis

The theory of analog circuit fault diagnosis is very important and significant,and now it has become a hot research topic.The rapid progress in morden electronic and computer technology promotes the advent of system-on-chip and mixed-signal integrated circuits,which present higher and newer circuit test request.Due to the impact of tolerance components,manys traditional analog circuit fault diagnosis methods are not very efficiency.Meanwhile,they need to calculate a large number of nonlinear equations,so the workload is excessive and the results are not satisfactory.Mordern intelligent technologies provide a effective way for circuit fault diagnosis.Artificial immune system has the features which are suitable for analog circuit fault diagnosis.It does not need to establish the precise mathematic model. In the fault diagnosis it has a good prospect because of Its self-organization, self-learning and memory capacities.In order to solve the problem how to access the circuit fault feature, the the article studies the fault feature methods,including effective sampling points extraction and wavelet analysis. The article researches the artificial immune system deeply,sums up the general framework of the immune algorithm and discusses the design of the specific immune algorithm.The paper investigates the analog circuit fault diagnosis method based on clonal selection algorithm and artificial immune network.Clone selection algorithm has a number of shortcomings such as slow convergence,easily caught in the local minimum value. The paper uses adaptive mutation,crossover and Niche to overcome the shortcomings.The method takes each cloned population of each antibody as the Niche sub-population and implements crossover and adaptive mutation on the Niche sub-population respectively.The simulation shows that the method can increase the diagnosis speed and diagnostic accuracy.The learning algorithm of artificial immune network uses the adaptive learning mechanism such as promotion and inhibtion of the immune network and clone selection,to generate an memory antibody population which can identify the antigen. The algorithm provides the methods to reduce and cluster the fault samples, further Improved adaptive mutation is applied to analog circuit fault diagnosis. The diagnostic results show that the algorithm has a good ability to reduce the diagnostic samples and a high rate of fault diagnosis,and proves the effectiveness and feasibility of the algorithm.