Two-level learning algorithm for multilayer neural networks

Abstract

A two-level learning algorithm that decomposes multilayer neural networks into a set of sub-networks is presented. A lot of popular optimization methods, such as conjugate-gradient and quasi-Neurton methods, cart be utilized to train these sub-networks. In addition, if the activation functions are hard-limiting functions, the multilayer neural networks can be trained by the perceptron learning rule in this two-level learning algorithm. Two experimental problems are given as examples for this algorithm.