Fractional-Order Dynamics in AI: Neural Networks and Applications
A special issue of Fractal and Fractional (ISSN 2504-3110). This special issue belongs to the section "Engineering".
Deadline for manuscript submissions: 20 August 2026 | Viewed by 5
Special Issue Editor
Interests: fractional calculus; control engineering; biochemical engineering; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in artificial intelligence (AI) have demonstrated the transformative potential of incorporating complex mathematical frameworks into neural network models. One such framework, fractional-order calculus, offers an enhanced approach for modelling dynamic systems with inherent memory and nonlinearity. Unlike traditional integer-order models, fractional-order systems are capable of capturing the long-term memory effects and intricate dynamics observed in many real-world processes. This characteristic positions fractional-order dynamics as a powerful tool for AI applications, enabling the more accurate and robust modelling of complex, time-varying data.
This Special Issue aims to explore the integration of fractional-order dynamics within the realm of AI, with a particular focus on their applications in neural networks and other machine learning paradigms. We invite contributions that examine the theoretical foundations of fractional calculus in AI, along with its practical applications in areas such as optimization, signal processing, autonomous systems, and natural language processing. From enhancing the learning efficiency of neural networks to improving the modelling of chaotic or time-dependent systems, fractional-order dynamics present unique opportunities for advancing AI technologies.
We welcome original research articles, reviews, and case studies addressing the following topics:
- The development and application of fractional-order neural networks;
- AI-driven optimization using fractional-order models;
- Fractional calculus for memory-based learning systems;
- Applications of fractional-order dynamics in signal processing and data analysis;
- Enhancing deep learning models with fractional-order frameworks;
- Fractional-order models for autonomous and control systems;
- Advanced applications of fractional dynamics in natural language processing.
We look forward to your innovative contributions and to advancing this exciting intersection of AI and fractional-order dynamics.
Finally, I would like to thank Alexandru-George Berciu for his valuable work and for assisting me with this Special Issue.
Prof. Dr. Eva H. Dulf
Guest Editor
Manuscript Submission Information
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Keywords
- fractional-order calculus
- artificial intelligence
- neural networks
- machine learning
- nonlinear dynamics
- memory effects
- optimisation
- deep learning
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