By Akira Hirose
Presents the newest advances in complex-valued neural networks through demonstrating the speculation in a variety of applications
Complex-valued neural networks is a speedily constructing neural community framework that makes use of complicated mathematics, displaying particular features in its studying, self-organizing, and processing dynamics. they're hugely compatible for processing advanced amplitude, composed of amplitude and section, that's one of many center ideas in actual structures to accommodate electromagnetic, gentle, sonic/ultrasonic waves in addition to quantum waves, particularly, electron and superconducting waves. This truth is a severe virtue in useful purposes in various fields of engineering, the place indications are many times analyzed and processed in time/space, frequency, and section domains.
Complex-Valued Neural Networks: Advances and Applications covers state-of-the-art themes and purposes surrounding this well timed topic. Demonstrating complex theories with quite a lot of purposes, together with conversation structures, snapshot processing structures, and brain-computer interfaces, this article deals complete assurance of:
- Conventional complex-valued neural networks
- Quaternionic neural networks
- Clifford-algebraic neural networks
Presented through overseas specialists within the box, Complex-Valued Neural Networks: Advances and Applications is perfect for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians drawn to computational intelligence, synthetic intelligence, laptop studying theories, and algorithms.
Chapter 1 software Fields and basic advantages of Complex?Valued Neural Networks (pages 1–31): Akira Hirose
Chapter 2 Neural procedure studying on Complex?Valued Manifolds (pages 33–57): Simone Fiori
Chapter three N?Dimensional Vector Neuron and Its program to the N?Bit Parity challenge (pages 59–74): Tohru Nitta
Chapter four studying Algorithms in Complex?Valued Neural Networks utilizing Wirtinger Calculus (pages 75–102): Md. Faijul Amin and Kazuyuki Murase
Chapter five Quaternionic Neural Networks for Associative stories (pages 103–131): Teijiro Isokawa, Haruhiko Nishimura and Nobuyuki Matsui
Chapter 6 versions of Recurrent Clifford Neural Networks and Their Dynamics (pages 133–151): Yasuaki Kuroe
Chapter 7 Meta?Cognitive Complex?Valued rest community and Its Sequential studying set of rules (pages 153–183): Ramasamy Savitha, Sundaram Suresh and Narasimhan Sundarara
Chapter eight Multilayer Feedforward Neural community with Multi?Valued Neurons for Brain–Computer Interfacing (pages 185–208): Nikolay V. Manyakov, Igor Aizenberg, Nikolay Chumerin and Marc M. Van Hulle
Chapter nine Complex?Valued B?Spline Neural Networks for Modeling and Inverse of Wiener platforms (pages 209–234): Xia Hong, Sheng Chen and Chris J. Harris
Chapter 10 Quaternionic Fuzzy Neural community for View?Invariant colour Face picture reputation (pages 235–278): Wai equipment Wong, Gin Chong Lee, Chu Kiong bathroom lavatory, means Soong Lim and Raymond Lock
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Additional info for Complex-Valued Neural Networks: Advances and Applications
The signal has a carrier of 100 kHz with thermal noise. It is converted into 100 Hz in-phase and quadrature-phase (IQ) intermediate-frequency (IF) signals through an IQ mixer. 01 in amplitude and 3 degrees in phase, which is common in this type of systems. The IF signal is recorded by a personal computer (PC) through an analogue/digital converter with 600 k Sample/s sampling frequency. We aim at appropriate interpolation of the signals in time and/or space domain for post-processing to generate high-quality time-space images.
1 INTRODUCTION Complex-valued neural systems are made of basic processing elements with complexvalued weights and complex-valued activation functions. A number of signal/data processing methods based on complex-valued neural systems are nowadays made use of in pattern recognition and classification, in artificial neural information processing and in image processing. The use of complex-valued weights and complex-valued activation functions is not simply a theoretical generalization of the real-valued case but it makes it possible to extend the functionality of a single basic neural processing element and of an artificial neural system to solve applied problems that do not accommodate well in the framework of real-valued neural systems , Complexvalued neural systems find applications in adaptive signal processing for highly functional sensing and imaging, in automatic control in unknown and changing environment, in brain-like information processing and in robotics inspired by human neural systems .
12), 2002. REFERENCES 31 60. Andriyan Bayu Suksmono and Akira Hirose. Beamforming of ultra-wideband pulses by a complex-valued spatio-temporal multilayer neural network. International Journal of Neural Systems, 15(1): 1-7, 2005. 61. Hidetosi Takahasi. An exerimental decimal calculator (in Japanese). Paper of Technical Group of Electronic Computers, IECE, March 1956. 62. Chor Shen Tay, Ken Tanizawa, and Akira Hirose. Error reduction in holographic movies using a hybrid learning method in coherent neural networks.