Advances in Machine Learning, Optimization, and Control Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Engineering Mathematics".
Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 44047
Special Issue Editors
Interests: large-scale pattern recognition; signal processing; machine learning; control systems
Special Issues, Collections and Topics in MDPI journals
Interests: sparse optimization; distributed optimization; deep learning; data-driven fault detection
Special Issues, Collections and Topics in MDPI journals
Interests: data-driven control systems; intelligent control; optimization; robot control
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In practice, many systems such as industrial processes, aerospace systems, transportation systems, power grid systems, etc., are becoming more and more complex. Moreover, these systems may suffer from various uncertainties, high nonlinearities, external disturbances, stochastic effects, etc., which significantly challenge model-based control and optimization, while with the development of information science and sensing technology, huge amounts of data are constantly emerging. Both academia and industry have put much effort into mining valuable information from data to facilitate control and optimization of practical systems.
Over the past few decades, data science and machine learning have demonstrated tremendous success in many areas of science and engineering, such as large-scale pattern recognition, computer vision, multiagent control, industrial engineering, etc. The connection between machine learning and control theory is becoming a popular research topic, which may endow control systems with learning ability and thus improve the control ability and performance of conventional control approaches. However, the coupling of a learning algorithm with a control loop requires a combined treatment as a dynamic process, which raises fundamental questions about stability, robustness, and safety for control systems. Additionally, insights from robust control theory may, in turn, help to enhance the robustness of machine learning algorithms. In order to leverage the potential of data-based and learning methods for control and optimization, we therefore believe that principled approaches integrating with machine learning and control theory are needed urgently, which therefore put forward new demands for novel mathematical theory, new optimization algorithms, and statistical techniques behind machine learning.
This Special Issue on “Advances in Machine Learning, Optimization, and Control Applications” aims to present the latest theoretical and technical advancements in the broad areas of machine learning, optimization, and control applications, and also to explore potential problems and challenges in connections of these techniques. Topics of interest in this Special Issue include but are not limited to machine learning, neural networks, statistical optimization learning, parallel and distributed optimization, sparse optimization, intelligent control via neural networks, and other applications of machine learning.
Prof. Dr. Wanquan Liu
Dr. Xianchao Xiu
Prof. Dr. Xuefang Li
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- neural networks
- mathematical models
- distributed systems
- optimization methods
- scientific computing
- pattern recognition
- data-driven control systems
- learning control systems
- reinforcement learning control and optimization
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