Fractional-Order Learning Systems: Theory, Algorithms, and Emerging 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: closed (20 January 2025) | Viewed by 1262
Special Issue Editor
Interests: distributed estimation and control; fractional-order learning systems; optimization; machine learning; high-dimensional algebras for control and signal processing applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Ideas of calculus based on the integration and differentiation of integer orders have firmly formed the mathematical basis for modelling signals, systems, and deriving methods for signal processing, learning, and control. However, integer-order calculus is a special case of a much wider framework that can accommodate the integration and differentiation of fractional orders. In recent years, it has come to the attention of researchers that using the integration and differentiation of fractional orders results in mathematical models of physical systems that are more accurate, highlighting the need for a comprehensive understanding of the deployment of fractional-order calculus in the fields of signal processing, control, learning, and circuit design. This Special Issue delves into recent advances in the theory of fractional-order calculus and its applications in information processing techniques, including the following:
- advances in the theory of fractional-order calculus and its application in information processing;
- machine learning techniques based on fractional-order calculus;
- the extension of signal processing and control algorithms from integer-order to fractional-order calculus;
- the application of fractional-order calculus in modelling behavior, modern circuits and systems.
Dr. Sayed Pouria Talebi
Guest Editor
Manuscript Submission Information
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Keywords
- fractional-order learning systems
- fractional-order calculus
- signal processing
- information processing
- machine learning
- control agorithms
- modern circuits
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