A yet faster version of complex-valued multilayer perceptron learning using singular regions and search pruning

In the search space of a complex-valued multilayer perceptron having J hidden units, C-MLP(J), there are singular regions, where the gradient is zero. Although singular regions cause serious stagnation of learning, there exist narrow descending paths from the regions. Based on this observation, a completely new learning method called C-SSF (complex singularity stairs following) 1.0 was proposed, which utilizes singular regions to generate starting points of C-MLP(J) search. Although C-SSF1.0 finds excellent solutions of successive C-MLPs, it takes long CPU time because the number of searches increases as J gets larger. To deal with this problem, C-SSF1.1 was proposed, a few times faster by the introduction of search pruning, but it still remained unsatisfactory. In this paper we propose a yet faster C-SSF1.3, going further with search pruning, and then evaluate the method in terms of solution quality and processing time.