Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters: Utilizing High-Dimensional Parameters

Nitta, Tohru

2/2009 · IGI Global

Táto e‑kniha

Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are in fact useful, containing characteristics bringing about many significant applications.

Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters covers the current state-of-the-art theories and applications of neural networks with high-dimensional parameters such as complex-valued neural networks, quantum neural networks, quaternary neural networks, and Clifford neural networks, which have been developing in recent years. Graduate students and researchers will easily acquire the fundamental knowledge needed to be at the forefront of research, while practitioners will readily absorb the materials required for the applications.

O autorovi

Tohru Nitta received a BS degree in mathematics, MS and PhD degrees in information science from University of Tsukuba (Japan) in 1983, 1985, and 1995 respectively. From 1985 to 1990, he was with NEC Corporation and engaged in research on expert systems. He joined the Electrotechnical Laboratory, Agency of Industrial Science and Technology, Ministry of International Trade and Industry (1990). He is currently a senior research Scientist in National Institute of Advanced Industrial Science and Technology (former Electrotechnical Laboratory), Japan. He was also with Department of Mathematics, Graduate School of Science, Osaka University as an associate professor from 2000 to 2006, and as a professor from 2006 to 2008 (additional post). His research interests include complex adaptive systems such as neural networks. [Editor]