Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters

Complex-Valued Neural Networks: Utilizing High-Dimensional Parameters

Tohru Nitta (National Institute of Advanced Industrial Science and Technology, Japan)
Indexed In: SCOPUS View 1 More Indices
Release Date: February, 2009|Copyright: © 2009 |Pages: 504
DOI: 10.4018/978-1-60566-214-5
ISBN13: 9781605662145|ISBN10: 1605662143|EISBN13: 9781605662152
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Description & Coverage
Description:

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.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Attractors and energy spectrum of neural structures
  • Communication signal processing
  • Complex-valued magnetic resonance images
  • Complex-valued neural network
  • Complex-valued recurrent neural networks
  • Complex-valued symmetric radial basis function network
  • Complex-valued time delay neural networks
  • Flexible blind signal separation
  • Global stability analysis
  • Image Reconstruction
  • Magnetic resonance spectroscopy
  • Model of the quantum harmonic oscillator
  • Quantum neural networks
  • Quaternionic neural networks
  • Qubit neural networks
Reviews and Testimonials

This book provides a snapshot of current research and thus serves as a workbench for further developments in neural networks with high-dimensional parameters.

– Tohru Nitta, National Institute of Advanced Industrial Science and Technology, Japan

This collection of research articles on complex-valued neural networks with high-dimensional parameters concentrates on how this technology can find non-linear relationships in data through development processes based on anatomy and physiology.

– Book News Inc. (June 2009)
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Editor/Author Biographies
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.
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Editorial Review Board
  • Sven Buchholz, Universitaet Kiel, Germany
  • Simone Fiori, Universita Politecnica delle Marche, Italy
  • George M. Georgiou, California State University, USA
  • Danilo P. Mandic, Imperial College, United Kingdom
  • Bernard Widrow, Stanford University, USA