Издательство IEEE/John Wiley, 2013, -303 pp.Complex-valued neural networks (CVNNs) have continued to open doors to various new applications. The CVNNs are the neural networks that deal with complex amplitude, i.e. signal having phase and amplitude, which is one of the most core concepts in science and technology, in particular in electrical and electronic engineering. A CVNN is not equivalent to a double-dimensional real-valued neural network. It has different dynamics and characteristics such as generalization, which is significantly useful in treatment of complex-amplitude information and wave-related phenomena. This is a critical point in applications in engineering fields. It is also crucial for developing new devices in the future. That is, the CVNN framework will play an important role in introduction of learning and self-organization into future quantum devices dealing with electron waves and photonic waves.
We can further expect that broad-sense CVNNs such as quatemion neural networks break ground in unique directions respectively. Quatemion has been essential in computer graphics to render three-dimensional moving objects. When we introduce learning and self-organization in virtual realities and computer-aided amenities, quatemion neural networks will surely bring an important fundamental basis. CVNNs may be useful even in physiological analysis and modeling where the researchers suggest, for example, that the phase information of neuron firing timing against the theta wave in electroencephalography possesses a close relationship to short-term position memory in the brain.Application Fields and Fundamental Merits
Neural System Learning on Complex-Valued Manifolds
N -Dimensional Vector Neuron and Parity Problem Its Application to the N -Bit
Learning Algorithms in Complex-Valued Neural Networks using Wirtinger Calculus
Quaternionic Neural Networks for Associative Memories
Models of Recurrent Clifford Neural Networks and Their Dynamics
Meta-cognitive Complex-valued Relaxation Network and its Sequential Learning Algorithm
Multilayer Feedforward Neural Network with Multi-Valued Neurons for Brain-Computer Interfacing
Complex-Valued B-Spline Neural Networks for Modeling and Inverse of Wiener Systems
Quaternionic Fuzzy Neural Network for View-invariant Color Face Image Recognition
