Fractional-Order Modeling in Data-Driven Intelligent Systems: From Optimization to AI/ML Applications
A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Optimization, Big Data, and AI/ML".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 32
Special Issue Editors
Interests: fractional order control systems; fractional calculus; data driven modeling; intelligent manufacturing; smart energy; intelligent control
Interests: fractional-order control; machine learning/ML; sliding-mode control; robust control; formation control; multi-agent systems; automation and robotics; fault detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The explosive growth of data and the increasing complexity of intelligent systems have ushered in a new era where optimization, Big Data technologies, and AI/ML (Artificial Intelligence and Machine Learning) must be tightly integrated. From massive-scale data analytics to real-time decision-making in uncertain environments, modern intelligent systems demand solutions that are not only data-driven, but also theoretically sound, scalable, and robust.
This Special Issue serves as a platform to advance the theoretical foundations and practical methodologies that drive scalable and intelligent systems, with a special focus on the intersection of optimization algorithm, Big Data infrastructure, and AI/ML algorithm design. We particularly welcome contributions that explore novel algorithmic paradigms, efficient system architectures, and robust decision-making strategies tailored for large-scale, data-intensive environments. Approaches that incorporate memory-aware modeling, such as those based on fractional-order calculus or fractal structures, are of special interest, as they offer enhanced flexibility and precision in capturing the complex dynamics of modern intelligent systems.
Topics of interest include, but are not limited to, the following:
- Fractional-order control and optimization algorithm for AI/ML
- Control theoretic analysis and design of ML algorithms (e.g., stability, robustness, convergence)
- Optimization-driven AI and ML frameworks for high-dimensional or streaming data
- Integration of optimization, control, and ML in Big Data environments
- Scalable and distributed optimization methods for large-scale ML models
- Safe and explainable AI through control and optimization perspectives
- Real-time ML and optimization for dynamic data streams in edge/cloud computing
- Interdisciplinary methods bridging optimization theory, control, and data science
We welcome original research articles, comprehensive review papers, and experimental studies that explore novel methodologies or demonstrate real-world impact through the integration of optimization, Big Data, and AI/ML, while embracing the analytical rigor and system insight provided by control theory and fractional-order methodologies.
Prof. Dr. Yan Li
Dr. Ziquan Yu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Fractal and Fractional is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- fractional-order calculus in AI/ML
- control theoretic ML
- large-scale optimization
- safe/explainable AI
- real-time ML
- interdisciplinary integration
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