Guest Editorial Special Issue on Complex- and Hypercomplex-Valued Neural Networks
Akira Hirose, Igor N. Aizenberg, Danilo P. Mandic · IEEE Transactions on Neural Networks and Learning Systems · 2014
The fifteen papers in this special issue focus on complex and hyper-complex neural network applications. Complex-valued neural networks (CVNNs) exhibit very desirable characteristics in their learning, self-organizing, and processing dynamics, which makes them attractive for applications in various areas in science and technology. For example, they are perfectly suited to deal with complex amplitude, composed of amplitude and phase, which is one of the core concepts in physical systems dealing with electromagnetic, light, sonic/ultrasonic, and quantum waves.