Complex-Valued Neural Networks: Advances and Applications

Publisher: Wiley-IEEE Press
Year: 2013
Tongue: English
Leaves: 302
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applications

Complex-valued neural networks is a rapidly developing neural network framework that utilizes complex arithmetic, exhibiting specific characteristics in its learning, self-organizing, and processing dynamics. They are highly suitable for processing complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems to deal with electromagnetic, light, sonic/ultrasonic waves as well as quantum waves, namely, electron and superconducting waves. This fact is a critical advantage in practical applications in diverse fields of engineering, where signals are routinely analyzed and processed in time/space, frequency, and phase domains.

Complex-Valued Neural Networks: Advances and Applications covers cutting-edge topics and applications surrounding this timely subject. Demonstrating advanced theories with a wide range of applications, including communication systems, image processing systems, and brain-computer interfaces, this text offers comprehensive coverage of:

Conventional complex-valued neural networks
Quaternionic neural networks
Clifford-algebraic neural networks

Presented by international experts in the field, Complex-Valued Neural Networks: Advances and Applications is ideal for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians interested in computational intelligence, artificial intelligence, machine learning theories, and algorithms.

Content:
Chapter 1 Application Fields and Fundamental Merits of Complex?Valued Neural Networks (pages 1–31): Akira Hirose
Chapter 2 Neural System Learning on Complex?Valued Manifolds (pages 33–57): Simone Fiori
Chapter 3 N?Dimensional Vector Neuron and Its Application to the N?Bit Parity Problem (pages 59–74): Tohru Nitta
Chapter 4 Learning Algorithms in Complex?Valued Neural Networks using Wirtinger Calculus (pages 75–102): Md. Faijul Amin and Kazuyuki Murase
Chapter 5 Quaternionic Neural Networks for Associative Memories (pages 103–131): Teijiro Isokawa, Haruhiko Nishimura and Nobuyuki Matsui
Chapter 6 Models of Recurrent Clifford Neural Networks and Their Dynamics (pages 133–151): Yasuaki Kuroe
Chapter 7 Meta?Cognitive Complex?Valued Relaxation Network and Its Sequential Learning Algorithm (pages 153–183): Ramasamy Savitha, Sundaram Suresh and Narasimhan Sundarara
Chapter 8 Multilayer Feedforward Neural Network with Multi?Valued Neurons for Brain–Computer Interfacing (pages 185–208): Nikolay V. Manyakov, Igor Aizenberg, Nikolay Chumerin and Marc M. Van Hulle
Chapter 9 Complex?Valued B?Spline Neural Networks for Modeling and Inverse of Wiener Systems (pages 209–234): Xia Hong, Sheng Chen and Chris J. Harris
Chapter 10 Quaternionic Fuzzy Neural Network for View?Invariant Color Face Image Recognition (pages 235–278): Wai Kit Wong, Gin Chong Lee, Chu Kiong Loo, Way Soong Lim and Raymond Lock

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