1. Summary and Scope
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 of energy system. However, with the widely use of information and communications techniques in energy system, the new problems such as cybersecurity pose huge challenges to energy system. Therefore, it has become critical to explore neural learning techniques for life system and energy system.
The special issue is intended to be an international forum for researchers to report the recent developments in these fields in an original research paper style. We solicit original contributions in three categories, all of which are expected to have an emphasis on neural learning in life system and energy system: (1) surveys of recent progress in this area; (1) state-of-the-art theories and novel application scenarios related to life system and energy system; (2) novel neural learning methods and applications.
The list of possible topics includes, but is not limited to:
l Advanced neural network and learning theory and algorithm
l Training of deep learning networks in life and energy systems
l Analysis of overﬁtting and generalization of deep networks training neural learning
l Neural network in modeling and design in life and energy systems
l Neural and learning-based optimal and security control methods
l Neural-based big-data analysis and applications in life and energy systems
2. Submission Guidelines
Authors should prepare their manuscripts according to the \”Instructions for Authors\” guidelines of “Neurocomputing” outlined at the journal website https://www.elsevier.com/journals/neurocomputing/0925-2312/guide-for-authors. All papers will be peer-reviewed following a regular reviewing procedure. Each submission should clearly demonstrate evidence of benefits to society or large communities. Originality and impact on society, in combination with a media-related focus and innovative technical aspects of the proposed solutions will be the major evaluation criteria.
3. Important Dates
Submission Deadline: September 30, 2017
First Review Decision: November 30, 2017
Revisions Due: February 30, 2018
Final Manuscript: March 30, 2018
Expected publication date: May, 30, 2018 (tentative)
4. Guest Editors
Prof. Chen Peng, Shanghai University, China (email@example.com)
Prof. Dong Yue, Nanjing University of Posts and Telecommunications, China (firstname.lastname@example.org)
Dr. Dajun Du, Shanghai University, China (email@example.com)
Dr. Huiyu Zhou, Queen’s University Belfast, United Kingdom (firstname.lastname@example.org)
Dr. Aolei Yang, Shanghai University, China (email@example.com)