Applications of Advanced Deep Learning Technology in Control and Intelligent Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 September 2024 | Viewed by 470
Special Issue Editors
Interests: singular systems; fractional systems; robust control
Special Issues, Collections and Topics in MDPI journals
Interests: intelligent control; dynamics and control; mechanism and machine theory; autonoumous system; fault tolerant control; artificial intelligence with engineering applications; machine learning methods; signal processing; intelligent transportation; system modeling and identification
Interests: fractional-order systems; nonlinear systems; multi-agent systems; prescribed performance control; nonlinear control
Special Issues, Collections and Topics in MDPI journals
Interests: non-asymptotic state estimation; non-asymptotic fractional order differentiator
Special Issues, Collections and Topics in MDPI journals
Interests: estimation and control for fractional order systems; numerical solutions for fractional order differential equations
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, numerous authors from diverse science and engineering fields have explored dynamical systems using advanced deep learning algorithms and fractional differential operators, leading to the proposal of many computational fractional intelligence systems and their reasoning applications. This Special Issue aims to provide an international platform for researchers to contribute original research focusing on the integration of mathematical ideas with optimal neural network algorithms and fractional operators. Interdisciplinary studies encompass theoretical frameworks, computational algorithm development, and applications in mechatronic systems and artificial intelligence. Fractional-order systems, which extend classical integer-order systems, accurately describe real-world physical phenomena. Constructing computational neuronal network models is essential for conducting experiments, either on computers or silicon chips, particularly in exploring virtual brain scenarios. Control systems derive significant benefits from artificial neural networks, facilitating the creation of intelligent interfaces and the storage of imprecise linguistic information. This intersects closely with computational intelligence, including neural networks and genetic and evolutionary algorithms. The exploration of advanced learner models and training approaches has demonstrated growing potential for industrial applications such as data modeling and predictive analytics. Additionally, the combination of powerful fractional operators and optimal algorithms exhibits promise for effectively analyzing and designing nonlinear and complex control systems, thus advancing control engineering. The interdisciplinary topics covered include control theory, fractional calculus, and the diverse applications of neural networks in intelligence systems.
Dr. Xuefeng Zhang
Prof. Dr. Jing Zhao
Dr. Jinxi Zhang
Prof. Dr. Driss Boutat
Dr. Dayan Liu
Guest Editors
Manuscript Submission Information
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Keywords
- fractional-order systems
- deep learning strategies
- multi-agent systems
- prescribed performance control
- rough set and fuzzy set reasoning
- genetic algorithms and modelling
- machine learning
- recurrent neural networks
- image processing and computer vision systems.
- time series forecasting
