Machine Learning in Computational Complex Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 20 May 2025 | Viewed by 1761

Special Issue Editor


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Guest Editor
School of Mathematics, Southeast University, Nanjing 210096, China
Interests: causal inference, prediction/generation, and system identification techniques for complex networks and systems science; artificial intelligence-related theory and applications

Special Issue Information

Dear Colleagues,

Machine learning has become essential for studying and managing computational complex systems, characterized by intricate, interdependent components and unpredictable behaviors. These systems span diverse fields, including biological networks, social systems, power models, and engineering infrastructures. By integrating machine learning, the modeling, analysis, and optimization of these systems can be improved, uncovering patterns and relationships that traditional methods may overlook. Advancements in machine learning are paving the way for more sophisticated approaches to managing and optimizing complex systems, enhancing their robustness, scalability, and adaptability in the face of growing complexity and uncertainty. Therefore, we are organizing this Special Issue, entitled “Machine Learning in Computational Complex Systems”, to stimulate researchers’ creativity and provide a platform for innovative ideas.

Dr. Duxin Chen
Guest Editor

Mengli Wei
Guest Editor Assistant

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Keywords

  • computational complex systems
  • machine learning
  • distributed control and optimization
  • decentralized learning
  • robust optimization and privacy protection in computational complex systems
  • time series analysis in computational complex systems
  • applications of computational complex systems

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Published Papers (2 papers)

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Research

20 pages, 1508 KiB  
Article
Reliability Growth Method for Electromechanical Products Based on Organizational Reliability Capability Evaluation and Decision-Making
by Zongyi Mu, Jian Li, Xiaogang Zhang, Genbao Zhang, Jinyuan Li and Hao Wei
Mathematics 2024, 12(23), 3754; https://doi.org/10.3390/math12233754 - 28 Nov 2024
Viewed by 566
Abstract
The reliability growth of electromechanical products is a continuous process of addressing reliability defects, which is very important for manufacturing enterprises. At present, research on the reliability growth of electromechanical products mostly focuses on the reliability defects of the products themselves, ignoring the [...] Read more.
The reliability growth of electromechanical products is a continuous process of addressing reliability defects, which is very important for manufacturing enterprises. At present, research on the reliability growth of electromechanical products mostly focuses on the reliability defects of the products themselves, ignoring the fact that manufacturing enterprises are the executors of product reliability related work. Improving the organizational reliability capability of manufacturing enterprises can enhance the reliability of electromechanical products. In order to understand the current situation of organizational reliability capability (ORC) in electromechanical product manufacturing enterprises and make improvements, this paper establishes an ORC evaluation indicator framework for electromechanical product manufacturing enterprises and evaluates it using the grey evaluation method. Firstly, an evaluation indicator framework for ORC is established based on enterprise research. Secondly, the ORC of electromechanical product manufacturing enterprises is evaluated by combining the three-parameter interval grey number and projection index function. Then, the evaluation results are analyzed from multiple perspectives to understand the current situation and shortcomings of ORC and guide its improvement. Finally, the evaluation indicator framework and method are explained through practical application in CNC machine tool manufacturing enterprises, and the effectiveness of the framework and method is demonstrated through the MTBF growth of CNC machine tools. Full article
(This article belongs to the Special Issue Machine Learning in Computational Complex Systems)
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17 pages, 804 KiB  
Article
R-DOCO: Resilient Distributed Online Convex Optimization Against Adversarial Attacks
by Zhixiang Kong, Huajian Xu and Chengsheng Pan
Mathematics 2024, 12(21), 3439; https://doi.org/10.3390/math12213439 - 3 Nov 2024
Cited by 1 | Viewed by 829
Abstract
This paper addresses the problem of distributed constrained optimization in a multi-agent system where some agents may deviate from the prescribed update rules due to failures or malicious adversarial attacks. The objective is to minimize the collective cost of the unattacked agents while [...] Read more.
This paper addresses the problem of distributed constrained optimization in a multi-agent system where some agents may deviate from the prescribed update rules due to failures or malicious adversarial attacks. The objective is to minimize the collective cost of the unattacked agents while respecting the constraint limitations. To tackle this, we propose a resilient distributed projected gradient descent algorithm for online optimization that achieves sublinear individual regret, defined as the difference between the online and offline solutions. Additionally, we extend the cost function from convex combinations to more general distributed optimization scenarios. The proposed algorithm demonstrates resilience under adversarial conditions, allowing it to handle an unknown number of adversarial nodes while maintaining performance. Compared to existing methods, this approach offers a robust solution to adversarial attacks in constrained distributed optimization problems. Full article
(This article belongs to the Special Issue Machine Learning in Computational Complex Systems)
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