By Akira Hirose
Presents the most recent advances in complex-valued neural networks via demonstrating the speculation in quite a lot of applications
Complex-valued neural networks is a quickly constructing neural community framework that makes use of advanced mathematics, showing particular features in its studying, self-organizing, and processing dynamics. they're hugely appropriate for processing complicated amplitude, composed of amplitude and section, that's one of many center innovations in actual platforms to accommodate electromagnetic, mild, sonic/ultrasonic waves in addition to quantum waves, particularly, electron and superconducting waves. This truth is a serious virtue in functional purposes in assorted fields of engineering, the place indications are mostly analyzed and processed in time/space, frequency, and part domains.
Complex-Valued Neural Networks: Advances and Applications covers state-of-the-art issues and functions surrounding this well timed topic. Demonstrating complex theories with a variety of purposes, together with communique platforms, snapshot processing structures, and brain-computer interfaces, this article deals finished insurance of:
- Conventional complex-valued neural networks
- Quaternionic neural networks
- Clifford-algebraic neural networks
Presented by means of overseas specialists within the box, Complex-Valued Neural Networks: Advances and Applications is perfect for advanced-level computational intelligence theorists, electromagnetic theorists, and mathematicians attracted to computational intelligence, synthetic intelligence, computing device studying theories, and algorithms.
Chapter 1 program Fields and primary advantages of Complex?Valued Neural Networks (pages 1–31): Akira Hirose
Chapter 2 Neural method studying on Complex?Valued Manifolds (pages 33–57): Simone Fiori
Chapter three N?Dimensional Vector Neuron and Its software 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 structures (pages 209–234): Xia Hong, Sheng Chen and Chris J. Harris
Chapter 10 Quaternionic Fuzzy Neural community for View?Invariant colour Face picture attractiveness (pages 235–278): Wai equipment Wong, Gin Chong Lee, Chu Kiong bathroom toilet, method Soong Lim and Raymond Lock
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Additional info for Complex-Valued Neural Networks: Advances and Applications
Frontiers of Electrical and Electronic Engineering in China, 6(1): 171-180, 2011. 29. Akira Hirose. Advances in applications of complex-valued neural networks. Journal of the Society of Instrument and Control Engineering, 51(4): 351-357, April 2012. REFERENCES 29 30. Akira Hirose, Yasufumi Asano, and Toshihiko Hamano. Developmental learning with behavioral mode tuning by carrier-frequency modulation in coherent neural networks. IEEE Transactions on Neural Networks, 17(6): 1532-1543, November 2006.
Springer-Verlag, Heidelberg, Berline, New York, 2006. 28. Akira Hirose. Nature of complex number and complex-valued neural networks. Frontiers of Electrical and Electronic Engineering in China, 6(1): 171-180, 2011. 29. Akira Hirose. Advances in applications of complex-valued neural networks. Journal of the Society of Instrument and Control Engineering, 51(4): 351-357, April 2012. REFERENCES 29 30. Akira Hirose, Yasufumi Asano, and Toshihiko Hamano. Developmental learning with behavioral mode tuning by carrier-frequency modulation in coherent neural networks.
For instance, unitary matrices are involved in almost all modern multi-antenna transceiver techniques and in sensor array applications to biomedicine and to astronomy. A typical application in signal processing is to array and multichannel signal processing techniques, which appear to be key technologies in wireless communication systems . An interesting signal processing application involving sets of unitary matrices is the design of symbol constellations with maximal diversity . Multiple antennas can enhance the data rate for wireless communication systems without increasing the error probability.