Fractional Calculus for Network Sciences
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Difference and Differential Equations".
Deadline for manuscript submissions: closed (24 December 2022) | Viewed by 425
Special Issue Editors
Interests: time delay; fractional-order; neural network; complex network; stability; synchronization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
There are many current scientific challenges in the field of network sciences. Fractional calculus is a branch of mathematical analysis that includes fractional differentiation and fractional integration in a narrow sense, as well as fractional difference and fractional sum in a broad sense.
The theory of fractional calculus has been successfully applied to various fields, and it can describe some non-classical phenomena in natural science and engineering. For example, fractional-order models are often used in neural networks. Fractional-order models are a powerful tool for advancing scientific research in these fields. Therefore, we hope that researchers will discuss various applications of fractional calculus in network sciences and thereby inspire readers in this Special Issue.
The purpose of this Special Issue is mainly to gather the latest research on recent advances in the theory and application of mathematical models in the broad fields of the theory, design, and application of neural networks and complex networks. We invite the authors to submit original research articles and high-quality review articles in network sciences obtained from the development, analysis, and simulation of mathematical models based on ordinary differential equations, difference equations, dynamical systems, integrodifferential equations, impulsive differential equations, fractional order differential equations, optimal control, Markov models, and others.
Dr. Feifei Du
Prof. Dr. Baoguo Jia
Guest Editors
Manuscript Submission Information
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Keywords
- differential and difference equations
- fractional order
- neural network
- complex network
- time delay
- stability
- synchronization
- fractional order differential equations
- impulsive differential equations
