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

Front Cover
Nitta, Tohru
Idea Group Inc (IGI), Feb 28, 2009 - Computers - 504 pages
0 Reviews

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.

 

What people are saying - Write a review

We haven't found any reviews in the usual places.

Contents

ComplexValued Neural Network and Inverse Problems
27
Kolmogorovs Spline ComplexNetwork and Adaptive DynamicModeling of Data
56
Dynamics and Applications
79
Global Stability Analysis forComplexValued RecurrentNeural Networks and ItsApplication to ConvexOptimization Problems
104
Models of ComplexValuedHopfieldType Neural Networksand Their Dynamics
123
ComplexValued SymmetricRadial Basis Function Networkfor Beamforming
143
ComplexValued NeuralNetworks for Equalization ofCommunication Channels
168
Learning Algorithms forComplexValued NeuralNetworks in CommunicationSignal Processing and AdaptiveEqualization as its Application
194
Flexible Blind Signal Separationin the Complex Domain
284
Its Performance and Applications
325
Neuromorphic AdiabaticQuantum Computation
352
Attractors and Energy Spectrumof Neural Structures Based onthe Model of the QuantumHarmonic Oscillator
376
Fundamental Properties andApplications
411
Compilation of References
440
About the Contributors
470
Index
476

Design by Using GeneralizedProjection Rule
236
A Method of Estimation forMagnetic ResonanceSpectroscopy UsingComplexValued NeuralNetworks
256

Other editions - View all

Common terms and phrases

About the author (2009)

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]

Bibliographic information