Novel backpropagation algorithm for reduction of hidden units and acceleration of convergence using artificial selection

A novel backpropagation algorithm with artificial selection is proposed. It is effective for both fast convergence and reduction of the number of hidden units. The main feature of the proposed algorithm is detection of the worst hidden unit. This is done by using the proposed badness factor, which indicates the badness of each hidden unit. It is the sum of backpropagated error components over all patterns for each hidden unit. For fast convergence, all the weights connected to the detected worst unit are reset to small random values at a suitable time. As for the reduction of hidden units, detected bad units are erased by precedent. Computer simulation results show the effectiveness of the proposed algorithm; for example, the number of hidden units in the EX-OR problems converge to two (theoretical number)