Data-Based Learning Methods and Their Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".
Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 2529
Special Issue Editors
Interests: distributed coordination and control of multi-agent systems; analysis and control of complex systems; swarm behavior and swarm intelligence; cyber-physical systems; basic theory of artificial intelligence
Special Issue Information
Dear Colleagues,
Data-based learning methods are an important and hot research direction in modern control theory and pattern recognition, among other fields. In contrast to traditional control techniques, data-based learning control methods require less information about system dynamics and use collected and stored data to construct the controllers or the control inputs and to discover underlying patterns; these methods have demonstrated superior performance. Despite the success of data-based learning methods for repetitive or non-strict repetitive control systems, pattern recognition, reinforcement learning, etc., data-based learning paradigms and their applications are still lacking.
This Special Issue aims to collect works on novel data-driven methods and their applications for repetitive or non-strict repetitive control systems. Works that include topics such as the design and analysis of iterative learning control systems, data-driven learning control techniques, non-standard iterative learning control, reinforcement learning, pattern recognition, and other learning control topics based on data are of particular interest.
Potential topics of interest include, but are not limited to, the following:
- Design and analysis of iterative learning control systems;
- Networked iterative learning control systems;
- Data-driven control techniques;
- Non-standard iterative learning control;
- Optimization iterative learning control;
- Iterative learning control for large-scale systems;
- Robust iterative learning control;
- Reinforcement learning, pattern recognition and their applications.
Prof. Dr. Yuanshi Zheng
Dr. Jian Liu
Guest Editors
Manuscript Submission Information
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Keywords
- iterative learning control
- robust iterative learning control
- optimization iterative learning control
- reinforcement learning
- pattern recognition
- data-based learning control
