Complex-Valued Neural Networks: Advances and Applications

Complex-Valued Neural Networks: Advances and Applications (IEEE Press Series on Computational Intelligence)

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.

Table of Contents

1 Application Fields and Fundamental Merits 1
2 Neural System Learning on Complex-Valued Manifolds 33
3 N-Dimensional Vector Neuron and Its Application to the N-Bit Parity Problem 59
4 Learning Algorithms in Complex-Valued Neural Networks using Wirtinger Calculus 75
5 Quaternionic Neural Networks for Associative Memories 103
6 Models of Recurrent Clifford Neural Networks and Their Dynamics 133
7 Meta-cognitive Complex-valued Relaxation Network and its Sequential Learning Algorithm 153
8 Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing 185
9 Complex-Valued B-Spline Neural Networks for Modeling and Inverse of Wiener Systems 209
10 Quaternionic Fuzzy Neural Network for View-invariant Color Face Image Recognition 235