Pub Date : 2026-01-23DOI: 10.1007/s40747-026-02230-6
Qinghua Liu, Romana Ashfaq, Azmat Ullah Khan Niazi, Mohammed M. A. Almazah, Aseel Smerat, Yi Chai
{"title":"Distributed robust control for consensus in heterogeneous multi-agent systems with delayed and disturbed inputs","authors":"Qinghua Liu, Romana Ashfaq, Azmat Ullah Khan Niazi, Mohammed M. A. Almazah, Aseel Smerat, Yi Chai","doi":"10.1007/s40747-026-02230-6","DOIUrl":"https://doi.org/10.1007/s40747-026-02230-6","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"220 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146048558","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-19DOI: 10.1007/s40747-025-02225-9
Yi Liu, Qingwang Wang, Wei Chen, Tao Shen
{"title":"TPSformer: knowledge graph representation learning with text and position structure for link prediction","authors":"Yi Liu, Qingwang Wang, Wei Chen, Tao Shen","doi":"10.1007/s40747-025-02225-9","DOIUrl":"https://doi.org/10.1007/s40747-025-02225-9","url":null,"abstract":"","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"50 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-17DOI: 10.1007/s40747-025-02213-z
Bingxin Liu, Xiaoyu Cheng, Wei He, Guohui Zhou
Fault diagnosis is crucial for complex system health management. The intelligent fault diagnosis model based on belief rule bases (BRB) effectively handles small sample data and uncertain information. However, due to the limitations of expert knowledge, simply utilizing this knowledge to construct initial rules may not fully capture the highly nonlinear relationships between inputs and outputs in complex systems, resulting in reduced model accuracy. While optimization algorithms can dynamically adjust the parameters and structure of the rule base to improve model accuracy, the iterative process of seeking an optimal solution can inevitably compromise the model's interpretability. To address the aforementioned issues, a fault diagnosis model based on interpretable BRB with fault tree analysis (FTA) has been proposed. In this model, initial rules are constructed through a structured representation of fault relationships using FTA. Additionally, interpretability constraints are introduced during optimization to limit the range of parameters, ensuring that the optimization results align with real-world situations. A case study on a CNC milling machine was conducted to validate the proposed method. The results indicate that the model identifies fault states in complex systems more effectively and accurately than existing models, maintaining a balance between accuracy and interpretability throughout the diagnosis process.
{"title":"A new fault diagnosis model for complex systems based on interpretable belief rule base with fault tree analysis","authors":"Bingxin Liu, Xiaoyu Cheng, Wei He, Guohui Zhou","doi":"10.1007/s40747-025-02213-z","DOIUrl":"https://doi.org/10.1007/s40747-025-02213-z","url":null,"abstract":"Fault diagnosis is crucial for complex system health management. The intelligent fault diagnosis model based on belief rule bases (BRB) effectively handles small sample data and uncertain information. However, due to the limitations of expert knowledge, simply utilizing this knowledge to construct initial rules may not fully capture the highly nonlinear relationships between inputs and outputs in complex systems, resulting in reduced model accuracy. While optimization algorithms can dynamically adjust the parameters and structure of the rule base to improve model accuracy, the iterative process of seeking an optimal solution can inevitably compromise the model's interpretability. To address the aforementioned issues, a fault diagnosis model based on interpretable BRB with fault tree analysis (FTA) has been proposed. In this model, initial rules are constructed through a structured representation of fault relationships using FTA. Additionally, interpretability constraints are introduced during optimization to limit the range of parameters, ensuring that the optimization results align with real-world situations. A case study on a CNC milling machine was conducted to validate the proposed method. The results indicate that the model identifies fault states in complex systems more effectively and accurately than existing models, maintaining a balance between accuracy and interpretability throughout the diagnosis process.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"12 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146005717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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