A fast neural network learning with guaranteed convergence to zero system error

It is thought that we have generally succeeded in establishing learning algorithms for neural networks, such as the back-propagation algorithm. However two major issues remain to be solved. First, there are possibilities of being trapped at a local minimum in learning. Second, the convergence rate is too slow. Chang and Ghaffar proposed to add a new hidden node, whenever stopping at a local minimum, and restart to train the new net until the error converges to zero. Their method designs newly generated weights so that the new net after introducing a new hidden node has less error than that at the original local minimum. In this paper, we propose a new method that improves their convergence rate. Our proposed method is expected to give a Lower system error and a larger error gradient magnitude than their method at a starling point of the new net, which leads to a faster convergence rate. Actually it is shown through numerical examples that the proposed method gives a much better performance than the conventional Chang and Ghaffar's method.