International Joint Conference on Neural Networks (WCCI-IJCNN 2020)
Complex-Valued and Quaternionic Neural Networks:
Theory and Applications
Complex-valued neural networks (CVNNs) and quaternionic neural networks (QNNs) constitute a rapidly growing research area that has attracted continued interest for the last decade. One of the most important characteristics of CVNNs is the proper treatment of phase and the information contained in phase, e.g., the treatment of wave- and rotation-related phenomena such as electromagnetism, light waves, quantum waves, and oscillatory phenomena. QNNs, which have potential applications in three- and four-dimensional data modeling, have been effectively used for processing and analysis of multivariate images such as color and polarimetric SAR images.
More generally, we can speak now about hypercomplex-valued neural networks (HVNNs), which include CVNNs and QNNs. treat multidimensional data as a single entity. There are several new directions in CVNNs, QNNs, and HVNNs: from formal generalization of the commonly used algorithms to the hypercomplex case that are mathematically richer than regular neurons, to the use of original activation functions that can increase significantly the neuron and network functionality. There are also many interesting applications in pattern recognition and classification, nonlinear filtering, intelligent image processing, brain-computer interfaces, time-series prediction, bioinformatics, robotics, etc.
The CVNN special session has become a traditional event of the IJCNN conference. Nine special sessions organized since 2006 (WCCI-IJCNN 2006, Vancouver, WCCI-IJCNN 2008, Hong Kong, IJCNN 2009, Atlanta, WCCI-IJCNN 2010, Barcelona, IJCNN-2011, San Jose, WCCI-IJCNN 2012, Brisbane, IJCNN-2013, Dallas, WCCI-IJCNN 2014, Beijing, WCCI-IJCNN 2016, Vancouver, WCCI-IJCNN 2018, Rio De Janeiro) attracted numerous submissions and had large audiences. They featured many interesting presentations and very productive discussions.
IJCNN 2020 will be a very attractive forum, where it will be possible to organize a systematic and comprehensive exchange of ideas in the area, to present the recent research results and to discuss the future trends. We hope that the proposed session will attract not only the potential speakers, but many new researches interested in joining the CVNNs community, including recent hardware/device researchers concerning reservoir computing (RC). We expect also that this session would be very beneficial for all computational intelligence researchers and other specialties that are in need of the sophisticated neural networks tools.
Scope. Papers that are, or might be, related to all aspects of the CVNNs, QVNNs and HVNNs are invited. We welcome contributions on theoretical advances as well as contributions of applied nature. We also welcome interdisciplinary contributions from other areas that are on the borders of the proposed scope. Topics include, but are not limited to:
Theoretical Aspects of CVNNs and QNNs
Complex-Valued and Quaternion Activation Functions
Learning Algorithms for CVNNs
Complex-Valued and Quaternionic Associative Memories
Pattern Recognition, Classification and Time Series Prediction using CVCNNs and QNNs
CVNNs and QNNs in Nonlinear Filtering
Dynamics of Complex-Valued and Quaternionic Neurons
Learning Algorithms for CVCNNs and QNNs
Chaos in Complex Domain
Spatiotemporal CVNNs Processing
Frequency Domain CVNNs Processing
Phase-Sensitive Signal Processing
Applications of CVNNs and QNNs in Image Processing, Speech Processing and
Quantum Computation and Quantum Neural Networks
CVNN in Brain-Computer Interfaces
CVNNs and QNNs in Robotics
Clifford and Hypercomplex-valued Neural Networks
CVNN-based neural hardware/devices such as lightwave and spin-wave neural devices
Danilo P. Mandic
Imperial College, London, United Kingdom
Manhattan College, New York City, USA
The University of Tokyo, Japan