A new learning algorithm without explicit error backpropagation
Hiroshi Ninomiya, N. Kinoshita · 2003
This paper describes a new supervised learning algorithm for multilayer neural networks without explicit error backpropagation (BP). The proposed method allows the asynchronous and parallel processing by neurons. Therefore this algorithm has an advantage over the standard backpropagation algorithm in hardware implementation of trainable artificial neural networks. We demonstrate the validity of the method through computer simulations. It is shown that the algorithm is not only almost equivalent to the BP algorithm from the viewpoint of the generalization ability, but also much superior to the one from the viewpoint of the convergence speed. As a result, it is confirmed that our algorithm is efficient and practical for the supervised learning of multilayer neural networks.