title: Exceptional reducibility of complex-valued neural networks.
abstract: A neural network is referred to as minimal if it cannot reduce the number of hidden neurons that maintain the input-output map. The condition in which the number of hidden neurons can be reduced is referred to as reducibility. Real-valued neural networks have only three simple types of reducibility. It can be naturally extended to complex-valued neural networks without bias terms of hidden neurons. However, general complex-valued neural networks have another type of reducibility, referred to herein as exceptional reducibility. In this paper, another type of reducibility is presented, and a method by which to minimize complex-valued neural networks is proposed.
authors.affiliation: Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Takeda, Kofu, Yamanashi 400-8511, Japan. k-masaki@yamanashi.ac.jp
authors.forename: Masaki
authors.initials: M
authors.lastname: Kobayashi
mesh_terms: D000465:Algorithms; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted