The invention discloses a neural
network method for diagnosing analog circuit failures which is based on a
particle swarm algorithm, and comprises the following steps: imposing an actuating
signal to an analog circuit to be tested, measuring an actuating response
signal in the testing nodes of the circuit, extracting the candidate
signal of failure characteristics by implementing
noise elimination and then
wavelet packet transformation on the measured actuating response signal, extracting the failure characteristics information by further implementing orthogonal
principal component analysis and normalization
processing on the candidate signal of failure characteristics, and sending the failure characteristics information as samples to the neural network for implementing classification. The method adopts the
particle swarm algorithm instead of a
gradient descent method in traditional BP algorithms, thus leading the
improved algorithm to be characterized in that the
algorithm avoids the local minimum problem and has better generalization performance. The BP neural
network method for diagnosing the analog circuit failures which is optimized on the basis of particle swarm can obviously reduce iteration times in the
algorithm, improve the precision of
network convergence, and improve diagnosis speed and precision.