Pattern learning by multilayer neural networks trained by a moderatism-based new algorithm

There are many learning algorithms for artificial neural networks today. However, most of these algorithms do not consider the learning characteristics of living creatures. We propose a new learning algorithm that is based on such a learning characteristic called "Moderatism". This new rule shows superiority over the well-known error backpropagation in some pattern learning experiments. Also the inclusion of the error of Moderatism in error backpropagation brings better learning performance.