Complex-valued Neural Networks: Utilizing High-dimensional Parameters

Authors:
Tohru Nitta;Tohru Nitta
Affiliations:
-;-
Venue:
Complex-valued Neural Networks: Utilizing High-dimensional Parameters
Year:
2009

Citing 0
Cited 6

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Convergence analysis of three classes of split-complex gradient algorithms for complex-valued recurrent neural networks

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Models of hopfield-type clifford neural networks and their energy functions - hyperbolic and dual valued networks -

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Local minima in hierarchical structures of complex-valued neural networks

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Letters: Blind sequence estimation of MPSK signals using dynamically driven recurrent neural networks

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Abstract

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.