PNN Based Fault Diagnosis for Analog Circuit with Tolerance
Peng Mei-fang · Microcomputer Information · 2008
The variety of faults in analog circuit with tolerance makes the number of training samples of neural network greatly increase. Structure of BP network tends to be complex and training rate is greatly reduced. Against the shortcomings of Back-propaga- tion Neural Network (BPNN),which include slow learning speed of convergence and the nature which is easy to fall into local mini- mum value, PNN based diagnostic method for faults of analog circuit with tolerance is proposed. Compared to the traditional network model of BP, it has advantages of short training time and difficult converging to local minimum. Simulation results show that: the di- agnostic method is rapid, accurate and also have higher recognition ability for the soft faults.