International Journal of Fuzzy Systems最新文献_第7页

Z-cloud Rough Fuzzy-Based PIPRECIA and CoCoSo Integration to Assess Agriculture Decision Support Tools Z-cloud 基于粗糙模糊的 PIPRECIA 和 CoCoSo 集成来评估农业决策支持工具

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-04 DOI: 10.1007/s40815-024-01771-7

Alhamzah Alnoor, Yousif Raad Muhsen, Nor Azura Husin, XinYing Chew, Maslina Binti Zolkepli, Noridayu Manshor

{"title":"Z-cloud Rough Fuzzy-Based PIPRECIA and CoCoSo Integration to Assess Agriculture Decision Support Tools","authors":"Alhamzah Alnoor, Yousif Raad Muhsen, Nor Azura Husin, XinYing Chew, Maslina Binti Zolkepli, Noridayu Manshor","doi":"10.1007/s40815-024-01771-7","DOIUrl":"https://doi.org/10.1007/s40815-024-01771-7","url":null,"abstract":"The livestock sector has exacerbated the problems of ensuring global food safety and greenhouse gas emissions. The rapid increase in livestock production has called to shed light on decision-support tools that develop sustainable production strategies. In this context, this study aims to expand the application of multiple-criteria decision analysis (MCDM) methods to assign weights to criteria and classify decision support tools for livestock with a high degree of certainty. In order to begin serious steps to address the global sustainability problem, this study extended the PIPRECIA method with a high-certainty fuzzy environment called Z-cloud rough numbers (ZCRNs) to record the weight of 19 criteria for decision support tools in livestock farming. An innovative and advanced method called CoCoSo has been utilized to rank decision-support tools for livestock farming. The methodology included two stages. The first phase involved developing the decision matrix. The second phase encompassed developing MCDM methods by clarifying the steps of the PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) method for assigning weight to criteria, in addition to highlighting the steps of the CoCoSo method for classifying decision support tools in the livestock industry. The results of the PIPRECIA method extended to the fuzzy environment of ZCRNs confirmed that visualization and herd characteristics received the highest weight compared to the rest of the criteria of decision support tools. The CoCoSo results provided insight into ranking alternatives for livestock decision support tools. AgRECalc has the highest ranking, and FCFC has the lowest ranking. This study conducted an evaluation test to increase the chances of generalizing the results of ranking decision-support tools of the livestock industry.","PeriodicalId":14056,"journal":{"name":"International Journal of Fuzzy Systems","volume":"19 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Guaranteed Cost Output Feedback Control for Nonlinear Systems via Networks with Adaptive Event-Triggered SCP and Hybrid Attacks 通过具有自适应事件触发 SCP 和混合攻击的网络实现非线性系统的保证成本输出反馈控制

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-04 DOI: 10.1007/s40815-024-01737-9

Xi Su, Xiaoming Tang, Xiao Lv, Yunjiao Zhu

{"title":"Guaranteed Cost Output Feedback Control for Nonlinear Systems via Networks with Adaptive Event-Triggered SCP and Hybrid Attacks","authors":"Xi Su, Xiaoming Tang, Xiao Lv, Yunjiao Zhu","doi":"10.1007/s40815-024-01737-9","DOIUrl":"https://doi.org/10.1007/s40815-024-01737-9","url":null,"abstract":"This paper addresses the problem of guaranteed cost output feedback control for a class of networked interval type-2 Takagi–Sugeno (IT2 T-S) fuzzy systems with adaptive event-triggered stochastic communication protocol (AETSCP) scheduling and hybrid attacks. A novel AETSCP scheduling is designed to judge whether or not data are triggered as well as to determine which node transmits data to the controller. Meanwhile, the security problem of hybrid attacks with respect to denial-of-service (DoS) attacks and deception attacks on the system is considered. The quadratic boundedness (QB) technique is employed to depict the closed-loop stability of the concerned networked control systems (NCSs). Two adequate theorems are given based on Lyapunov stability theory for designing the observer-based and dynamic output feedback-based controllers, which guarantee the stability and robust performance of the required system. In the end, a simulation example of the mass-spring-damping system is provided to confirm the effectiveness of the presented control strategy.","PeriodicalId":14056,"journal":{"name":"International Journal of Fuzzy Systems","volume":"ahead-of-print 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Percentage Comparison of Fuzzy Numbers Using a Newly Presented Method in the Context of Surrogate Modeling 在代用模型中使用新方法比较模糊数的百分比

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-04 DOI: 10.1007/s40815-024-01732-0

Thomas Oberleiter, Kai Willner

引用次数: 0

A Positive Sample Enhancement Algorithm with Fuzzy Nearest Neighbor Hybridization for Imbalance Data 针对不平衡数据的模糊近邻混合正样本增强算法

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-03 DOI: 10.1007/s40815-024-01721-3

Jiapeng Yang, Lei Shi, Tielin Lu, Lu Yuan, Nanchang Cheng, Xiaohui Yang, Jia Luo, Mingying Xu

{"title":"A Positive Sample Enhancement Algorithm with Fuzzy Nearest Neighbor Hybridization for Imbalance Data","authors":"Jiapeng Yang, Lei Shi, Tielin Lu, Lu Yuan, Nanchang Cheng, Xiaohui Yang, Jia Luo, Mingying Xu","doi":"10.1007/s40815-024-01721-3","DOIUrl":"https://doi.org/10.1007/s40815-024-01721-3","url":null,"abstract":"The class imbalance problem is one of the critical research areas of machine learning and deep learning and has received widespread attention from researchers. To solve the class imbalance problem, current typical methods only use positive samples to generate synthetic samples that are similar to the minority class while ignoring the characteristic information of negative samples. Therefore, when the number of positive samples is too small and has highly similar features, it will cause the classifier to have fitting problems. In response to the above problems, we propose a new positive sample enhancement algorithm (PENH) to solve the class imbalance by simulating the process of chromosome cross-fusion. We select the fuzzy negative sample set around the positive sample by the K-nearest neighbor algorithm and adopt the beyond empirical risk minimization (Mixup) to randomly hybridize the positive sample with the negative sample of the set. To overcome the problem of sample imbalance, we adopt the One-class SVM with overfitting of positive samples to select the newly generated unlabeled samples to obtain the balanced dataset. We construct multiple experiments in 20 open datasets. The results show that our PENH outperforms the other six baseline methods in multiple evaluation indicator.","PeriodicalId":14056,"journal":{"name":"International Journal of Fuzzy Systems","volume":"41 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Monotonic Fuzzy Systems With Goniometric Membership Functions 具有测角成员函数的单调模糊系统

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-03 DOI: 10.1007/s40815-024-01758-4

Petr Hušek

引用次数: 0

Cognitive and Non-cognitive States in Romeo and Juliet’s Love Model and Its Chaotic Behaviors by Complex Fuzzy Numbers 罗密欧与朱丽叶爱情模型中的认知和非认知状态及其复杂模糊数的混沌行为

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-03 DOI: 10.1007/s40815-024-01733-z

Jin Hee Yoon, Youngchul Bae

{"title":"Cognitive and Non-cognitive States in Romeo and Juliet’s Love Model and Its Chaotic Behaviors by Complex Fuzzy Numbers","authors":"Jin Hee Yoon, Youngchul Bae","doi":"10.1007/s40815-024-01733-z","DOIUrl":"https://doi.org/10.1007/s40815-024-01733-z","url":null,"abstract":"When a person feels some emotion such as happiness, sadness, or love, the person consciously knows that such feelings appear, or it is done unconsciously, and sometimes it appears as a compound result of the two. In this paper, these two are defined as cognition and noncognition. By applying these two to the fuzzy love model using complex fuzzy numbers, we observe the chaotic behavior that appears in this model. We verify chaotic behaviors in the love model with fuzzy triangular and trapezoidal external forces using phase portrait and bifurcation diagram. The love model is known as the differential equation that can represent how a person feels love with respect to the time when the positive or negative external force is changed. Because love is a person’s feeling that includes vagueness and ambiguity of human emotion. Even more the external force also can express some external influence that is also can be human’s response. Because a person’s feeling is vague and ambiguous, fuzzy valued sinusoidal functions and Gaussian have been used to express those feelings and external forces.","PeriodicalId":14056,"journal":{"name":"International Journal of Fuzzy Systems","volume":"14 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fuzzy Adaptive Containment Control for Fractional-Order Heterogeneous Multi-agent Systems with Distributed Time-Varying Delays and Input Saturation 具有分布式时变延迟和输入饱和的分数阶异构多代理系统的模糊自适应遏制控制

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-03 DOI: 10.1007/s40815-024-01760-w

Zhile Xia, Zhenpeng Li, Jinping Mou

{"title":"Fuzzy Adaptive Containment Control for Fractional-Order Heterogeneous Multi-agent Systems with Distributed Time-Varying Delays and Input Saturation","authors":"Zhile Xia, Zhenpeng Li, Jinping Mou","doi":"10.1007/s40815-024-01760-w","DOIUrl":"https://doi.org/10.1007/s40815-024-01760-w","url":null,"abstract":"This paper studies the input-constrained containment control problem for a class of fractional-order unknown nonlinear heterogeneous multi-agent systems with distributed time-varying delays under a directed communication network topology. To deal with the unknown time-delay function, we extend the signal permutation method to multiple leaders case and represent it as a bounded function with a generalized consensus tracking error function. To handle input saturation and system uncertainties, we design a distributed adaptive controller using interval type-2 fuzzy logic system theory and projection algorithm, which effectively avoids the complexity caused by general model reduction and ensures the boundedness of estimated parameters. To analyze the convergence of the error system, we construct a new Lyapunov-Krasovskii functional that fully considers the effects of system uncertainties and time delay without requiring the Lyapunov matrix to satisfy a special diagonal form. Then, combining with fractional calculus theory and linear matrix inequality (LMI) method, the sufficient conditions for implementing containment control have been proposed. A new controller design method has also been proposed, ensuring that all followers converge within the convex hull spanned by the leaders. The designed controller is fully distributed and easy to implement in practical applications, as each controller only uses its own and neighboring nodes’ information. Finally, simulation example is presented to demonstrate the effectiveness of the proposed methods.","PeriodicalId":14056,"journal":{"name":"International Journal of Fuzzy Systems","volume":"19 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scikit-ANFIS: A Scikit-Learn Compatible Python Implementation for Adaptive Neuro-Fuzzy Inference System Scikit-ANFIS：自适应神经模糊推理系统的 Scikit-Learn 兼容 Python 实现

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-03 DOI: 10.1007/s40815-024-01697-0

Dongsong Zhang, Tianhua Chen

{"title":"Scikit-ANFIS: A Scikit-Learn Compatible Python Implementation for Adaptive Neuro-Fuzzy Inference System","authors":"Dongsong Zhang, Tianhua Chen","doi":"10.1007/s40815-024-01697-0","DOIUrl":"https://doi.org/10.1007/s40815-024-01697-0","url":null,"abstract":"The Adaptative neuro-fuzzy inference system (ANFIS) has shown great potential in processing practical data from control, prediction, and inference applications, reflecting advantages in both high performance and system interpretability as a result of the hybridization of neural networks and fuzzy systems. Matlab has been a prevalent platform that allows to utilize and deploy ANFIS conveniently. On the other hand, due to the recent popularity of machine learning and deep learning, which are predominantly Python-based, implementations of ANFIS in Python have attracted recent attention. Although there are a few Python-based ANFIS implementations, none of them are directly compatible with scikit-learn, one of the most frequently used libraries in machine learning. As such, this paper proposes Scikit-ANFIS, a novel scikit-learn compatible Python implementation for ANFIS by adopting a uniform format such as fit() and predict() functions to provide the same interface as scikit-learn. Our Scikit-ANFIS is designed in a user-friendly way to not only manually generate a general fuzzy system and train it with the ANFIS method but also to automatically create an ANFIS fuzzy system. We also provide four kinds of representative cases to show that Scikit-ANFIS represents a valuable addition to the scikit-learn compatible Python software that supports ANFIS fuzzy reasoning. Experimental results on four datasets show that our Scikit-ANFIS outperforms recent Python-based implementations while achieving parallel performance to ANFIS in Matlab, a standard implementation officially realized by Matlab, which indicates the performance advantages and application convenience of our software.","PeriodicalId":14056,"journal":{"name":"International Journal of Fuzzy Systems","volume":"16 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fusion of Metaheuristic Fuzzy Neural Network and Self-tuning Autonomous Control for Omnidirectional Mobile Platforms in Robotic Cyber-Physical Systems 机器人网络物理系统中全向移动平台的元启发式模糊神经网络与自调整自主控制的融合

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-03 DOI: 10.1007/s40815-024-01752-w

Hsu-Chih Huang, Jing-Jun Xu, Han-Lung Kuo

引用次数: 0

Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations 转变可持续水产养殖：模糊系统与深度学习创新的协同作用

IF 4.3 3区计算机科学

International Journal of Fuzzy Systems Pub Date : 2024-06-03 DOI: 10.1007/s40815-024-01744-w

Basanta Haobijam, Yo-Ping Huang, Yue-Shan Chang, Tsun-Wei Chang

{"title":"Transforming Sustainable Aquaculture: Synergizing Fuzzy Systems and Deep Learning Innovations","authors":"Basanta Haobijam, Yo-Ping Huang, Yue-Shan Chang, Tsun-Wei Chang","doi":"10.1007/s40815-024-01744-w","DOIUrl":"https://doi.org/10.1007/s40815-024-01744-w","url":null,"abstract":"Pisciculture encounters an array of intricate challenges that span disease management, preservation of water quality, prevention of genetic hybridization, ensuring the integrity of net systems, sourcing sustainable aquatic feed, and comprehending fish growth and reproductive dynamics. Addressing these multifaceted challenges necessitates a comprehensive research approach. This study employs an innovative synergy of fuzzy logic and deep learning techniques, resulting in a robust strategy to tackle these obstacles effectively. Fuzzy logic excels in assessing stressed fish conditions by handling inherent uncertainties. Simultaneously, YOLOv7 with fuzzy color enhancement (YOLOv7FCE) is used to detect damaged fish nets, thereby mitigating losses and upholding the integrity of the net infrastructure. This approach also leverages YOLOv7FCE for identifying Cobia fish within shoals, streamlining the identification process. Subsequently, DeepLabv3 is implemented to meticulously segment the recognized Cobia fish, facilitating precise measurements of their physical attributes. This comprehensive methodology yields profound insights into growth patterns and feeding tendencies within the confined aquatic environment. By embracing this approach, the research presents a versatile and adaptive framework that not only enhances our comprehension of piscine dynamics but also holds the potential to revolutionize the aquaculture industry.","PeriodicalId":14056,"journal":{"name":"International Journal of Fuzzy Systems","volume":"58 1","pages":""},"PeriodicalIF":4.3,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0