Nitta T. Complex-Valued Neural Networks. Utilizing High-Dimensional Parameters

IGI Global, 2009. — 504 p.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.Section I Complex-Valued Neural Network Models and Their Analysis
Complex-Valued Boltzmann Manifold
Complex-Valued Neural Network and Inverse Problems
Kolmogorov’s Spline Complex Network and Adaptive Dynamic Modeling of Data
A Complex-Valued Hopfield Neural Network: Dynamics and Applications
Global Stability Analysis for Complex-Valued Recurrent Neural Networks and Its Application to Convex Optimization Problem
Models of Complex-Valued Hopfield-Type Neural Networks and Their Dynamics
Section II Applications of Complex-Valued Neural Networks
Complex-Valued Symmetric Radial Basis Function Network for Beamforming
Complex–Valued Neural Networks for Equalization of Communication Channels
Learning Algorithms for Complex-Valued Neural Networks in Communication Signal Processing and Adaptive Equalization as its Application
Image Reconstruction by the Complex-Valued Neural Networks: Design by Using Generalized Projection Rule
A Method of Estimation for Magnetic Resonance Spectroscopy Using Complex-Valued Neural Networks
Flexible Blind Signal Separation in the Complex Domain
Section III Models with High-Dimensional Parameters
Qubit Neural Network: Its Performance and Applications
Neuromorphic Adiabatic Quantum Computation
Attractors and Energy Spectrum of Neural Structures Based on the Model of the Quantum Harmonic Oscillator
Quaternionic Neural Networks: Fundamental Properties and Applications