Utilization of stochastic automata for neural network learning

Backpropagation method has been applied to various pattern classification problems. However, one of the most important limitations of this method is that it often fails to find a global minimum of total error function of neural networks. In order to overcome this limitation, me have recently proposed a hybrid algorithm which combines the random optimization method with the modified backpropagation method. This hybrid algorithm has been successfully applied to several actual problems, such as air pollution density forecasting, stock price forecasting, etc. In this paper, the learning performance of stochastic automaton is utilized to accelerate the convergence of this hybrid algorithm. Several computer simulation results confirm our ideas.