Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.0
Journals
Axioms
Special Issues
Mathematical Modelling of Complex Systems
share announcement

Mathematical Modelling of Complex Systems

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Physics".

Deadline for manuscript submissions: 31 July 2024 | Viewed by 3348

Special Issue Editors

Dr. Haichuan Yang

E-Mail Website
Guest Editor
Graduate School of Information Sciences, Tohoku University, Sendai 980-8579, Japan
Interests: meta-heuristic algorithms; neuron model; complex systems; renewable energy
Dr. Chaofeng Zhang

E-Mail Website
Guest Editor
Advanced Institute of Industrial Technology, Tokyo 140-0011, Japan
Interests: wireless communication technology; cloud computing; internet of things; cyber physical system; mobile computing

Special Issue Information

Dear Colleagues,

Complex systems, defined as systems composed of a large number of interacting parts and characterized by non-linearity, adaptability, and dynamic changes, span various significant fields such as climatology, ecology, economics, social networks, cyber physical system, and biology. Understanding these complex systems is crucial for interpreting many key phenomena in our world. However, due to their inherent characteristics, it is often challenging to accurately describe and predict complex systems using traditional mathematical and computational methods.

Against this backdrop, artificial intelligence (AI) technologies such as neural networks and evolutionary algorithms have become critically important in the study of complex systems. These AI algorithms themselves are examples of complex systems, displaying numerous interactions, non-linearity, dynamic changes, and adaptability. This complexity poses a challenge but also offers a new approach to understanding and researching complex systems.

On the one hand, AI technologies like neural networks and evolutionary algorithms can be employed for the simulation and optimization of complex systems. They can handle high-dimensional, non-linear, and dynamic data, thereby providing deep insights into and effective predictions of the behaviour of complex systems. Mathematical modelling plays a pivotal role in this process, offering a rigorous theoretical framework for the modelling of complex systems and providing an accurate computational foundation for the application of AI technologies such as neural networks and evolutionary algorithms.

On the other hand, the mathematical tools and concepts in complex systems theory provide valuable insights for understanding and improving AI algorithms. The dynamic, adaptive, and non-linear properties of complex systems offer new perspectives and tools for understanding the training process of neural networks, improving the search strategies of evolutionary algorithms, and designing more efficient AI systems.

This Special Issue, "Mathematical Modelling of Complex Systems," aims to explore this two-way relationship, with a particular focus on research using mathematical modelling methods to aid AI technologies in the modelling and optimization of complex systems. At the same time, we look forward to works that use mathematical tools from complex systems theory to understand and improve AI algorithms. We encourage interdisciplinary research, intending to study complex systems from an AI perspective and to investigate AI through the lens of complex systems, thereby promoting the development of this interdisciplinary field.

Dr. Haichuan Yang
Dr. Chaofeng Zhang
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. Axioms 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 2400 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

  • complex systems
  • mathematical modelling
  • artificial intelligence
  • neural networks
  • evolutionary algorithms
  • adaptability
  • dynamics
  • system optimization
  • Internet of Things
  • cyber physical system

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 817 KiB  
Article
A Hyperparameter Self-Evolving SHADE-Based Dendritic Neuron Model for Classification
by Haichuan Yang, Yuxin Zhang, Chaofeng Zhang, Wei Xia, Yifei Yang and Zhenwei Zhang
Axioms 2023, 12(11), 1051; https://doi.org/10.3390/axioms12111051 - 15 Nov 2023
Viewed by 962
Abstract
In recent years, artificial neural networks (ANNs), which are based on the foundational model established by McCulloch and Pitts in 1943, have been at the forefront of computational research. Despite their prominence, ANNs have encountered a number of challenges, including hyperparameter tuning and [...] Read more.
In recent years, artificial neural networks (ANNs), which are based on the foundational model established by McCulloch and Pitts in 1943, have been at the forefront of computational research. Despite their prominence, ANNs have encountered a number of challenges, including hyperparameter tuning and the need for vast datasets. It is because many strategies have predominantly focused on enhancing the depth and intricacy of these networks that the essence of the processing capabilities of individual neurons is occasionally overlooked. Consequently, a model emphasizing a biologically accurate dendritic neuron model (DNM) that mirrors the spatio-temporal features of real neurons was introduced. However, while the DNM shows outstanding performance in classification tasks, it struggles with complexities in parameter adjustments. In this study, we introduced the hyperparameters of the DNM into an evolutionary algorithm, thereby transforming the method of setting DNM’s hyperparameters from the previous manual adjustments to adaptive adjustments as the algorithm iterates. The newly proposed framework, represents a neuron that evolves alongside the iterations, thus simplifying the parameter-tuning process. Comparative evaluation on benchmark classification datasets from the UCI Machine Learning Repository indicates that our minor enhancements lead to significant improvements in the performance of DNM, surpassing other leading-edge algorithms in terms of both accuracy and efficiency. In addition, we also analyzed the iterative process using complex networks, and the results indicated that the information interaction during the iteration and evolution of the DNM follows a power-law distribution. With this finding, some insights could be provided for the study of neuron model training. Full article
(This article belongs to the Special Issue Mathematical Modelling of Complex Systems)
►▼ Show Figures

Figure 1

20 pages, 6303 KiB  
Article
Optimizing Port Multi-AGV Trajectory Planning through Priority Coordination: Enhancing Efficiency and Safety
by Yongjun Chen, Shuquan Shi, Zong Chen, Tengfei Wang, Longkun Miao and Huiting Song
Axioms 2023, 12(9), 900; https://doi.org/10.3390/axioms12090900 - 21 Sep 2023
Viewed by 1011
Abstract
Efficient logistics and transport at the port heavily relies on efficient AGV scheduling and planning for container transshipment. This paper presents a comprehensive approach to address the challenges in AGV path planning and coordination within the domain of intelligent transportation systems. We propose [...] Read more.
Efficient logistics and transport at the port heavily relies on efficient AGV scheduling and planning for container transshipment. This paper presents a comprehensive approach to address the challenges in AGV path planning and coordination within the domain of intelligent transportation systems. We propose an enhanced graph search method for constructing the global path of a single AGV that mitigates the issues associated with paths closely aligned with obstacle corner points. Moreover, a centralized global planning module is developed to facilitate planning and scheduling. Each individual AGV establishes real-time communication with the upper layers to accurately determine its position at complex intersections. By computing its priority sequence within a coordination circle, the AGV effectively treats the high-priority trajectories of other vehicles as dynamic obstacles for its local trajectory planning. The feasibility of trajectory information is ensured by solving the online real-time Optimal Control Problem (OCP). In the trajectory planning process for a single AGV, we incorporate a linear programming-based obstacle avoidance strategy. This strategy transforms the obstacle avoidance optimization problem into trajectory planning constraints using Karush-Kuhn-Tucker (KKT) conditions. Consequently, seamless and secure AGV movement within the port environment is guaranteed. The global planning module encompasses a global regulatory mechanism that provides each AGV with an initial feasible path. This approach not only facilitates complexity decomposition for large-scale problems, but also maintains path feasibility through continuous real-time communication with the upper layers during AGV travel. A key advantage of our progressive solution lies in its flexibility and scalability. This approach readily accommodates extensions based on the original problem and allows adjustments in the overall problem size in response to varying port cargo throughput, all without requiring a complete system overhaul. Full article
(This article belongs to the Special Issue Mathematical Modelling of Complex Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop