Complex-valued multilayer perceptron learning using singular regions and search pruning
Seiya Satoh, Ryohei Nakano · 2015
In the search space of a complex-valued multilayer perceptron (C-MLP) there exist flat areas called singular regions. Although singular regions cause serious stagnation of learning, there exist descending paths from the regions. Based on this observation, a completely new learning method for C-MLP, called C-SSF1.0, was proposed, making good use of singular regions to stably find excellent solutions of successive C-MLPs. However, the method takes longer time than an existing method. This paper proposes a faster version of C-SSF called C-SSF1.1 by introducing search pruning. Our experiments showed the proposed method ran a few times faster than C-SSF1.0 without losing excellent solutions quality.