In the search space of a complex-valued multilayer perceptron having
hidden units, C-MLP(
), there exist flat areas called singular regions, as is the case with a real-valued MLP. The singular regions cause serious stagnation of learning, preventing usual search methods from finding an excellent solution. However, there exist descending paths from the regions since most points in the regions are saddles. This paper proposes a completely new learning method that does not avoid but makes good use of singular regions to stably and successively find excellent solutions commensurate with C-MLP(
). Our experiments showed the proposed method worked well.