Alternate Learning Algorithm on Multilayer Perceptrons

Multilayer perceptrons have been applied successfully to solve some difficult and diverse problems with the backpropagation learning algorithm. However, the algorithm is known to have slow and false convergence aroused from flat surface and local minima on the cost function. Many algorithms announced so far to accelerate convergence speed and avoid local minima appear to pay some trade-off for convergence speed and stability of convergence. Here, a new algorithm is proposed, which gives a novel learning strategy for avoiding local minima as well as providing relatively stable and fast convergence with low storage requirement. This is the alternate learning algorithm in which the upper connections, hidden-to-output, and the lower connections, input-tohidden, alternately trained. This algorithm requires less computational time for learning than the backpropagation with momentum and is shown in a parity check problem to be relatively reliable on the overall performance.