随机自动学习机在BP神经网络中的应用

Application of Stochastic Learning Automata for BP Neural Networks

BP算法在许多领域中得到了很好的应用,但它有很多局限性。对复杂的问题,BP需要很长的时间训练网络,而且不一定能得到最佳的网络参数,因此找到合适的网络参数是比较困难的。本文将引入随机自动学习机模型来对BP网络的参数进行调整优化。实验证明所提出的方法不仅能提高网络训练的收敛速度,而且避免了训练陷入局部最小点。

Despite of the many successful applications of backpropagation, it has many drawbacks. For complex problems it may require a long time to train the networks, and it may not attain optimum values for the networks, h is not easy to choose appropriate values for the BP parameters. In this paper, we apply stochastic learning automata for adjusting these BP parameters. Experiments show that the adaptation of these parameters using this method not only increases the convergence rate of learning but it avoids the likelihood of escaping from the local minima.