With the exponential growth in data availability and the advancements in computing power, the importance of neural networks lies in its ability to process large-scale data, enable automation tasks, support decision-making, etc. The transformative power of neural networks has the potential to reshape industries, improve lives, and contribute to the advancement of society as a whole. Neural networks depicted in ordinary differential equations (ODEs) ingeniously integrate neural networks and differential equations, two prominent modeling approaches widely applied in various fields such as chemistry, physics, engineering, and economics. Serving as equations that describe the relationship between a class of functions and their derivatives, ODEs possess rich mathematical analysis methods and are thus integral tools in classical mathematical theory. Neural networks depicted in ODEs leverage the differential equation description of physical processes, combining it with the potent fitting capabilities of neural networks. In contrast to traditional neural networks that overlook physical information and rely solely on numerous neurons for fitting, neural networks depicted in ODEs can achieve more accurate estimates with fewer neurons, while maintaining robustness, generalization, and interpretability in the learned systems. To fulfill the powerful potential of robots, plenty of algorithms based on neural networks depicted in ODEs are researched to simulate human-like learning processes, realize decision-making tasks, and address the issues of uncertain models and control strategies. Robots have great application value in the fields of artificial intelligence, information technology, and intelligent manufacturing due to their efficient perception, decision-making, and execution capabilities.
However, fully realizing the potential of robots entails addressing a myriad of technology challenges which encompass data, noise, safety, cost, generalization, real-time applicability, and interpretability issues. Robots have difficulties in being applied to various fields of human society. Ensuring efficiency remains paramount in the face of these multifaceted challenges, reflecting the unique and evolving landscape of neural networks depicted in ODEs. The prime aim of this special issue is to motivate researchers to publish their latest research works focusing on the issues and challenges of neural networks depicted in ODEs and their solutions in emerging technologies such as soft robot technology, brain-computer interface technology, autonomous driving technology, and multi-robot cloud service technology. The proposed submissions and presentations should be original and unpublished works including but not limited to
SUBMISSION GUIDELINES
Papers submitted to this journal for possible publication must be original and must not be under consideration for publication in any other journals. Prospective authors should submit an electronic copy of their completed manuscript to https://mc03.manuscriptcentral.com/tst with manuscript type as “Special Issue on Neural networks depicted in ODEs with Applications”. Further information on the journal is available at: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5971803.
IMPORTANT DATES
Submission deadline: July 31, 2024
GUEST EDITORS
Prof. Long Jin, School of Information Science and Engineering, Lanzhou University, Lanzhou, China. Email: jinlong@lzu.edu.cn
Prof. Chenguang Yang, Bristol Robotics Laboratory, University of the West of England, Bristol, U.K. Email: Charlie.Yang@uwe.ac.uk
Prof. Shuai Li, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland. Email: shuai.li@oulu.fi
Prof. Predrag Stanimirović, Faculty of Sciences and Mathematics, University of Niš, Serbia. Email: pecko@pmf.ni.ac.rs
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