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Informationen zum Autor AKIRA HIROSE, PhD , is a Professor in the Department of Electrical Engineering and Information Systems, the University of Tokyo, Japan. His main fields of interest are wireless electronics and neural networks on which he has published several books. Dr. Hirose is a Fellow of the IEEE, a senior member of the IEICE, and Vice President of the Japanese Neural Network Society. All contributors are members of the Task Force on Complex-Valued Neural Networks, IEEE Computational Intelligence Society Neural Network Technical Committee. Klappentext Presents the latest advances in complex-valued neural networks by demonstrating the theory in a wide range of applicationsComplex-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 networksPresented 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. "In summary, this book contains a wide variety of hot topics on advanced computational intelligence methods which incorporate the concept of complex and hypercomplex number systems into the framework of artificial neural networks ... Nevertheless, it seems that the applications of CVNNs and hypercomplex-valued neural networks are very promising." ( IEEE Computational intelligence magazine , 1 May 2013) Zusammenfassung This book covers the hot topics and applications surrounding complex-valued neural networks. It demonstrates advanced theories with a wide range of applications, including optoelectronics systems, imaging systems, and remote sensing systems. Inhaltsverzeichnis Preface xv 1 Application Fields and Fundamental Merits 1 Akira Hirose 1.1 Introduction 1 1.2 Applications of Complex-Valued Neural Networks 2 1.3 What is a complex number? 5 1.4 Complex numbers in feedforward neural networks 8 1.5 Metric in complex domain 12 1.6 Experiments to elucidate the generalization characteristics 16 1.7 Conclusions 26 2 Neural System Learning on Complex-Valued Manifolds 33 Simone Fiori 2.1 Introduction 34 2.2 Learning Averages over the Lie Group of Unitary Matrices 35 2.3 Riemannian-Gradient-Based Learning on the Complex Matrix-Hypersphere 41 2.4 Complex ICA Applied to Telecommunications 49 2.5 Conclusion 53 3 N -Dimensional Vector Neuron and Its Application to the N -Bit Parity Problem 59 Tohru Nitta 3.1 Introduction 59 3.2 Neuron Models with High-Dimensional Parameters 60 3.3 N-Dimensional Vector Neuron 65 3.4 Discussion 69 3.5 Conclusion 70 <...
