By Akira Hirose
Presents the most recent advances in complex-valued neural networks by means of demonstrating the idea in a variety of applications
Complex-valued neural networks is a swiftly constructing neural community framework that makes use of complicated mathematics, showing particular features in its studying, self-organizing, and processing dynamics. they're hugely compatible for processing complicated amplitude, composed of amplitude and section, that's one of many middle recommendations in actual structures to accommodate electromagnetic, gentle, sonic/ultrasonic waves in addition to quantum waves, specifically, electron and superconducting waves. This truth is a severe virtue in useful functions in various fields of engineering, the place indications are in many instances analyzed and processed in time/space, frequency, and section domains.
Complex-Valued Neural Networks: Advances and Applications covers state-of-the-art subject matters and functions surrounding this well timed topic. Demonstrating complex theories with a variety of functions, together with communique platforms, snapshot processing platforms, and brain-computer interfaces, this article deals accomplished insurance 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 attracted to computational intelligence, synthetic intelligence, laptop studying theories, and algorithms.
Chapter 1 software Fields and primary benefits of Complex?Valued Neural Networks (pages 1–31): Akira Hirose
Chapter 2 Neural approach 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 types of Recurrent Clifford Neural Networks and Their Dynamics (pages 133–151): Yasuaki Kuroe
Chapter 7 Meta?Cognitive Complex?Valued leisure 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 popularity (pages 235–278): Wai package Wong, Gin Chong Lee, Chu Kiong bathroom lavatory, manner Soong Lim and Raymond Lock
Read or Download Complex-Valued Neural Networks: Advances and Applications PDF
Similar ai & machine learning books
Studying sciences researchers wish to study studying in actual contexts. They gather either qualitative and quantitative information from a number of views and stick with developmental micro-genetic or historic ways to information statement. studying sciences researchers behavior learn with the purpose of deriving layout ideas wherein switch and innovation should be enacted.
Describes scientists' makes an attempt to determine how lifestyles started, together with such subject matters as spontaneous iteration and evolution.
Even supposing speech is the main usual kind of verbal exchange among people, most folks locate utilizing speech to speak with machines whatever yet typical. Drawing from psychology, human-computer interplay, linguistics, and communique conception, sensible Speech consumer Interface layout presents a entire but concise survey of functional speech person interface (SUI) layout.
This booklet, via the authors of the Neural community Toolbox for MATLAB, offers a transparent and exact assurance of basic neural community architectures and studying ideas. In it, the authors emphasize a coherent presentation of the significant neural networks, equipment for education them and their functions to useful difficulties.
- Coarse-to-Fine Natural Language Processing
- Information Processing by Biochemical Systems: Neural Network-Type Configurations
- Neural Networks for Time Series Forecasting with R
- Corpus Applications in Applied Linguistics
- Tractable Reasoning in Artificial Intelligence
Extra info for Complex-Valued Neural Networks: Advances and Applications
44. Soichi Masuyama, Kenzo Yasuda, and Akira Hirose. Multiple mode selection of walledltsa array elements for high resolution imaging to visualize antipersonnel plastic landmines. IEEE Geoscience and Remote Sensing Letters, 5(4):745-749, October 2008. 30 APPLICATION FIELDS AND FUNDAMENTAL MERITS 45. Nobuyuki Matsui, Teijiro Isokawa, Hiromi Kusamichi, Ferdinand Peper, and Haruhiko Nishimura. Quaternion neural network with geometrical operators. Journal of Intelligent and Fuzzy Systems, 15:149-164, 2004.
Yukimasa Nakano and Akira Hirose. Improvement of plastic landmine visualization performance by use of ring-csom and frequency-domain local correlation. IEICE Transactions on Electronics, E92-CQ): 102-108, January 2009. 50. Ikuko Nishikawa and Yasuaki Kuroe. Dynamics of complex-valued neural networks and its relation to a phase oscillator system. In International Conference on Neural Information Processing (ICONIP) 2004 Calcutta, pages 122-129, Berlin, November 2004. Springer. 51. Tomohiro Nishino, Ryo Yamaki, and Akira Hirose.
An augumented echo state network for nonlinear adaptive flletering of complex noncircular signals. IEEE Transactions on Neural Networks, 22(l):74-83, January 2011. 65. Ryo Yamaki and Akira Hirose. Singular unit restoration in interferograms based on complex-valued Markov random field model for phase unwrapping. IEEE Geoscience and Remote Sensing Letters, 6(1): 18-22, January 2009. 66. K. Bose. Landmine detection and classification with complexvalued hybrid neural network using scattering parameters dataset.