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Reservoir Computing: Trends and Future Prospects

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Closed
Reservoir Computing (RC) has the knowledge and data to predict the placement of a point with time, it is now being used in time series prediction, ESN predictors, etc. In the field of biology, RC is used in the detection of diseases, cognitive prediction, neuroscience and medication. The recent trends in RC include the application in financial markets. The changing world scenario needs micro and macro-economic prediction for an acceleration of economy and business. Thus, RC with its capability to model complex data sets can be used in financial modelling as well. As RC is suited for temporal data processing, it can be applied in electronic, photonic and mechanical modelling and research. The context reverberation systems can be used in high-dimensional dynamical systems. The main advancement in RC is quantum reservoir computing that enables quantum mechanical interaction. The other recent advancements in RC are reservoir memory machines, pyramidal state echo networks, simplified deep reservoir architectures, self-organised dynamic attractors in recurrent neural networks, self-organised echo state networks and human action recognition. Hardware RC implementations with low-cost computation can be used for efficient training. Additionally, RC can also be used in memory augmented neural networks. One main advantage of RC is its universality. The application of RC in all the fields and related advancements make it more reliable. With the increased application, the non-compromising accuracy in RC is another added benefit. This issue tends to research the current trends and future prospects of reservoir computing.

Editors

  • Dr. Ahmed A. Abd El-Lati Dr. Ahmed A. Abd El-Lati , 

    Dr. Ahmed A. Abd El-Lati

    Ahmed A. Abd El-Latif received the B.Sc. degree (Hons.) in mathematics and computer science and the M.Sc. degree in computer science from Menoufia University, Egypt, in 2005 and 2010, respectively, and the Ph.D. degree in computer science and technology from the Harbin Institute of Technology (H.I.T), Harbin, China, in 2013. He is currently an Associate Professor of computer science with Menoufia University and School of Information Technology and Computer Science, Nile University, Egypt. He is authored or co-authored of more than 130 papers, including refereed IEEE/ACM/Springer/Elsevier journals, conference papers, and book chapters.

  • Dr. Edmond Shu-lim Ho Dr. Edmond Shu-lim Ho  &

    Dr. Edmond Shu-lim Ho

    Dr. Edmond Shu-lim Ho is currently a Senior Lecturer in the Department of Computer and Information Sciences at Northumbria University, Newcastle, UK. Prior to joining Northumbria University in 2016, he was a Research Assistant Professor in the Department of Computer Science at Hong Kong Baptist University. He received the BSc degree in Computer Science from the Hong Kong Baptist University, the MPhil degree from the City University of Hong Kong, and the PhD degree from the University of Edinburgh. His research interests include human motions analysis and synthesis, machine learning, physically based animation, and robotics.

  • Dr. Jialiang Peng Dr. Jialiang Peng

    Dr. Jialiang Peng

    Dr. Jialiang Peng received his B.S. and M.S. degrees in computer science from Heilongjiang University and his Ph.D. degree in computer science at Harbin Institute of Technology, China. He is an associate professor in the School of Data Science and Technology, Heilongjiang University. He worked as a postdoctoral scholar at the Norwegian University of Science and Technology, Gjøvik, Norway. His research interests include biometric recognition, data management, and cyberspace security.

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