Priority ordered BP neural network and the application for speaker identification

The backpropagation neural network (BPNN) has been researched and applied to solve the problem that the training time of the backpropagation network can be excessive, so the structure and training algorithm of priority ordered BP neural networks are proposed. The neurons of its output layer have priority ordered interconnections, during the training course, the training data tails off gradually, so the algorithm may converge rapidly because of the decrease of the complexity of performance function. Compared with the conventional BPNN, the total iterative epochs of priority ordered BPNN are far lower and the performance function can converge more rapidly in a text-independent speaker identification task.