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Editorial: Neural learning in life system and energy system
journal contributionposted on 2020-03-19, 12:15 authored by Chen Peng, Dong Yue, Dajun Du, Huiyu Zhou, Aolei Yang
As well recognized, neural learning is one of the most powerful and popular techniques. The last decade has also witnessed the rapid advancements of neural learning techniques, which consists of various neural learning approaches such as neural networks, deep learning, evolutionary learning, etc. In recent years, to understand the interaction between components (i.e., cells, tissues and organisms) of life system and predict system behaviors, people have started using neural learning techniques to model and simulate life systems. Although significant progress has been made in the research of life systems, the recently developed neural learning methods still cannot match the demands of exploiting life systems due to the complexity of a life system. Meanwhile, neural learning techniques have been employed to model and control energy systems. However, with the widely use of information and communications techniques in energy system, the new problems such as cyber security pose huge challenges to energy system. Therefore, it has become critical to explore neural learning techniques for life system and energy system. This special issue collected nine papers reporting the recent developments of neural learning in life system and energy system.
CitationNeurocomputing, Volume 344, 2019, Pages 1-2
Author affiliationDepartment of Informatics
- AM (Accepted Manuscript)