Analog circuit intermittent fault diagnosis method based on multi-granularity cascade forest

The invention provides an analog circuit intermittent fault diagnosis method based on a multi-granularity cascade forest. In order to solve the problem that the traditional fault diagnosis method is not suitable for large-scale complex integrated circuits at the present stage, and the tntermittent fault data is small, the characteristic that a multi-granularity cascade forest utilization algorithmis adopted to well express small data is adopted to classify different types of fault data so as to diagnose the intermittent faults of an analog circuit. According to the method, a deep neural network layer-by-layer cascade structure is adopted; the depth of a cascade layer and the number of random forests and complete random forests in each layer can be adjusted according to actual conditions,small-scale intermittent fault data can be trained to achieve a good result, the requirement of deep learning for a large-scale data set is avoided, and meanwhile the detection effect comparable to that of a deep learning algo