Intelligent Modeling and Control
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: closed (23 April 2024) | Viewed by 7817
Special Issue Editor
Special Issue Information
Dear Colleagues,
System identification, modeling and control algorithms have always been the core issues of control theory. Creating and studying the mathematical model through the representation data of systems is the basis for recognizing, regulating and controlling systems. However, with the improvement of the requirements for system cognition, interpretability and modeling accuracy, the traditional system modeling technologies have certain limitations and defects, which created greater difficulties and challenges for system control. In recent years, due to the progress of science and technology and the development of artificial intelligence, system modeling and control has ushered in a new opportunity. More and more intelligent technologies including machine learning, fuzzy logic system, neural networks, evolutionary algorithms, reinforcement learning, deep learning and so on are being applied to system modeling and system control. Although these intelligent technologies have developed rapidly, there are still some new technology and application problems that needed to be solved, such as the compromise in the cost and accuracy of system modeling, the effectiveness and efficiency of control algorithms, etc. Therefore, innovative intelligent algorithms, advanced system modeling strategies and practical control methods are urgently needed.
Entropy and information-theoretic concepts also have strong relevance to intelligent modeling and control. To be specific, in intelligent modeling and control, entropy can be used to describe the complexity and randomness of the system. For example, in machine learning, entropy can be used to calculate the information entropy of data sets, thus helping select the optimal segmentation point or decision tree. In addition, in cybernetics, entropy can be used to describe the stability and controllability of the system, which helps design effective controllers. Furthermore, entropy in information theory can also be used to build machine learning models. For example, in natural language processing, entropy can be used to evaluate the complexity and prediction ability of language models. In deep learning, the cross-entropy loss function is often used to measure the prediction accuracy of the model. Therefore, it is a popular research direction to apply entropy or information-theoretic concepts with intelligent technology to system modeling and control.
The main objective of this Special Issue is, through scientific researchers and technical engineers, to introduce the latest research in the field on intelligent modeling and control, including multi-agent modeling and control, intelligent modeling and control of complex systems, new insights into intelligent modeling and control, human–computer cooperation, advanced control strategies, the application of entropy or information-theoretic concepts in intelligent modeling and control, etc. Furthermore, intelligent solutions for complex engineering and future research prospects will also be included.
Authors are encouraged to submit their original contributions regarding intelligent modeling and control or information-theoretic concepts in modeling and control. Potential topics include but are not limited to the following:
- Intelligent modeling technology (e.g., entropy-based methods including Shannon entropy, K–L Divergence, minimizing of relative entropy and so on, fuzzy logic systems, neural network systems and evolutionary learning systems, information-related methods, etc.)
- Intelligent control algorithms applied to practical systems (e.g., fuzzy control, neural network control, reinforcement learning control, adaptive control, sliding mode control, optimal control, fractional order control, etc.)
- Muti-agent intelligent modeling and control technology (e.g., machine learning, deep learning, natural language processing, biological recognition, computer vision, modeling strategies and optimization decisions, etc.)
- Complex system modeling and control (e.g., self-organization, chaos and nonlinear dynamics, simplicity and complexity, networks, symmetry breaking, similarity, etc.)
- Intelligent technology in related fields (such as prediction, robots, physics, computing, information, biology, materials, energy, environment, food, pharmaceuticals and manufacturing, etc.)
- Experimental examples of the intelligent modeling and control technology are also encouraged to submit to this Special Issue.
Dr. Tao Zhao
Guest Editor
Manuscript Submission Information
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