Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494512
Gabriella Casalino, G. Castellano, Katarzyna Kaczmarek-Majer, O. Hryniewicz
Voice features from everyday phone conversations are regarded as a sensitive digital marker of mood phases in bipolar disorder. At the same time, although acoustic data collected from smartphones are relatively large, their psychiatric labelling is usually very limited, and there is still a need for intelligent and interpretable approaches to process such multiple data streams with a low percentage of labelling. Furthermore, both acoustic data and psychiatric labels are subject to several sources of uncertainty (e.g., irregular phone usage, background noises, subjectivity in psychiatric evaluation). To cope with these characteristics of an acoustic data stream, this paper introduces an intelligent qualitative and quantitative analysis based on the Dynamic Incremental Semi-Supervised Fuzzy C-Means algorithm (DISSFCM) for supporting bipolar disorder monitoring. The proposed approach is illustrated with real-life data collected from smartphones and psychiatric assessments of a bipolar disorder patient. Analysis of the dynamics of data streams basing on the cluster prototypes from fuzzy semi-supervised learning is a highly novel approach. It is also showed that the DISSFCM algorithm obtains relatively high classification performance (accuracy ranging from 0.66 to 0.76) already with 25% labelling percentage, thanks to the splitting mechanism that is adapting the number of clusters to the structure of data.
{"title":"Intelligent analysis of data streams about phone calls for bipolar disorder monitoring","authors":"Gabriella Casalino, G. Castellano, Katarzyna Kaczmarek-Majer, O. Hryniewicz","doi":"10.1109/FUZZ45933.2021.9494512","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494512","url":null,"abstract":"Voice features from everyday phone conversations are regarded as a sensitive digital marker of mood phases in bipolar disorder. At the same time, although acoustic data collected from smartphones are relatively large, their psychiatric labelling is usually very limited, and there is still a need for intelligent and interpretable approaches to process such multiple data streams with a low percentage of labelling. Furthermore, both acoustic data and psychiatric labels are subject to several sources of uncertainty (e.g., irregular phone usage, background noises, subjectivity in psychiatric evaluation). To cope with these characteristics of an acoustic data stream, this paper introduces an intelligent qualitative and quantitative analysis based on the Dynamic Incremental Semi-Supervised Fuzzy C-Means algorithm (DISSFCM) for supporting bipolar disorder monitoring. The proposed approach is illustrated with real-life data collected from smartphones and psychiatric assessments of a bipolar disorder patient. Analysis of the dynamics of data streams basing on the cluster prototypes from fuzzy semi-supervised learning is a highly novel approach. It is also showed that the DISSFCM algorithm obtains relatively high classification performance (accuracy ranging from 0.66 to 0.76) already with 25% labelling percentage, thanks to the splitting mechanism that is adapting the number of clusters to the structure of data.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130128330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494413
Fatemeh Rezaee-Ahmadi, H. Rafiei, M. Akbarzadeh-T.
Z-numbers consist of two components, restriction and restriction reliability, to cover both possibilistic and probabilistic uncertainties. So far, the components of Z-numbers are merely determined by expert knowledge and lack automated learning/training. To overcome this limitation, we propose a Z-Adaptive Fuzzy Inference System (ZAFIS) that systematically learns the parameters of Z-numbers from input-output data pairs. We first convert the second component of Z-numbers to a crisp number. We then use this number as a weight for the first fuzzy membership part of Z-numbers. Finally, the resultant membership is placed in a fuzzy inference system, and the parameters of the system are learned based on the input-output data pairs using a gradient descent algorithm. The proposed method is evaluated on several functions (sine, increasing sine, Hermite, Gabor, and a nonlinear function) with/without added noise scenarios. The results show that the ZAFIS is more robust against the noisy inputs and is superior to the Fuzzy Inference Systems (FISs) in terms of MSE.
{"title":"Z-Adaptive Fuzzy Inference Systems","authors":"Fatemeh Rezaee-Ahmadi, H. Rafiei, M. Akbarzadeh-T.","doi":"10.1109/FUZZ45933.2021.9494413","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494413","url":null,"abstract":"Z-numbers consist of two components, restriction and restriction reliability, to cover both possibilistic and probabilistic uncertainties. So far, the components of Z-numbers are merely determined by expert knowledge and lack automated learning/training. To overcome this limitation, we propose a Z-Adaptive Fuzzy Inference System (ZAFIS) that systematically learns the parameters of Z-numbers from input-output data pairs. We first convert the second component of Z-numbers to a crisp number. We then use this number as a weight for the first fuzzy membership part of Z-numbers. Finally, the resultant membership is placed in a fuzzy inference system, and the parameters of the system are learned based on the input-output data pairs using a gradient descent algorithm. The proposed method is evaluated on several functions (sine, increasing sine, Hermite, Gabor, and a nonlinear function) with/without added noise scenarios. The results show that the ZAFIS is more robust against the noisy inputs and is superior to the Fuzzy Inference Systems (FISs) in terms of MSE.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129336892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494487
E. Rak, A. Szczur
Currently, we observe an enormous growth in the frequency of using the Internet, which is also causing an increase in attacks on computer nets. These phenomena significantly raise the importance of the use of Intrusion Detection Systems (IDS). Classification systems are an essential part of a cyber-attack detection task by classifying the attacks based on certain criteria. The purpose of this research is to assess the relative performance of five extensions of well-known classification methods using the distributivity law. The results of this investigation can help in the design of classification systems that use several classification methods, namely k-Nearest Neighbor, Naive Bayes, Support Vector Machine, Random Forests, and Multilayer Perceptron Network can be employed to increase the accuracy of the classification. This method requires the use of some adequate aggregation operators (e.g. average functions and triangular norms/conorms) for which the distributivity law occurs. The work contains principally the results of experiments carried out on the KDD'Cup 99 dataset using WEKA (Waikato Environment for Knowledge Analysis) tool.
目前，我们观察到使用互联网的频率有了巨大的增长，这也导致了对计算机网络的攻击增加。这些现象显著地提高了使用入侵检测系统(IDS)的重要性。分类系统是网络攻击检测任务的重要组成部分，它根据一定的标准对攻击进行分类。本研究的目的是评估使用分配律的五种知名分类方法的扩展的相对性能。本研究的结果可以帮助分类系统的设计，这些分类系统可以使用k-最近邻、朴素贝叶斯、支持向量机、随机森林和多层感知器网络等几种分类方法来提高分类的准确性。这种方法需要使用一些适当的聚合算子(例如，平均函数和三角规范/规范)，其中分布律出现。这项工作主要包含使用WEKA (Waikato Environment for Knowledge Analysis)工具在KDD'Cup 99数据集上进行的实验结果。
{"title":"Comparative assessment of aggregated classification algorithms with the use to mining a cyber-attack dataset","authors":"E. Rak, A. Szczur","doi":"10.1109/FUZZ45933.2021.9494487","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494487","url":null,"abstract":"Currently, we observe an enormous growth in the frequency of using the Internet, which is also causing an increase in attacks on computer nets. These phenomena significantly raise the importance of the use of Intrusion Detection Systems (IDS). Classification systems are an essential part of a cyber-attack detection task by classifying the attacks based on certain criteria. The purpose of this research is to assess the relative performance of five extensions of well-known classification methods using the distributivity law. The results of this investigation can help in the design of classification systems that use several classification methods, namely k-Nearest Neighbor, Naive Bayes, Support Vector Machine, Random Forests, and Multilayer Perceptron Network can be employed to increase the accuracy of the classification. This method requires the use of some adequate aggregation operators (e.g. average functions and triangular norms/conorms) for which the distributivity law occurs. The work contains principally the results of experiments carried out on the KDD'Cup 99 dataset using WEKA (Waikato Environment for Knowledge Analysis) tool.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"31 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124594346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494571
Carmen Biedma-Rdguez, M. J. Gacto, R. Alcalá, J. Alcalá-Fdez
A large number of systems with a great predictive capacity, such as Deep Learning, are being currently used to solve a wide variety of real problems. However, the models obtained are not easy to understand by scientists, giving rise to the field of eXplainable Artificial Intelligence, which encourage techniques that obtain accurate and understandable models. Fuzzy Association Rules are models that can be understood by themselves, but its interpretability can be improved by representing the same information with fewer and simpler rules. In this work, we propose Meta-Fuzzy Items, which allows to define more generic fuzzy items to represent the same information with fewer rules, and to extend the type of associations that can be represented. Based on this proposal, a new fuzzy data-mining algorithm is presented to extract interesting and interpretable rules from quantitative transactions. The quality of our approach is analyzed using statistical analysis and comparing with a well-known fuzzy data-mining algorithm.
{"title":"Meta-Fuzzy Items for Fuzzy Association Rules","authors":"Carmen Biedma-Rdguez, M. J. Gacto, R. Alcalá, J. Alcalá-Fdez","doi":"10.1109/FUZZ45933.2021.9494571","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494571","url":null,"abstract":"A large number of systems with a great predictive capacity, such as Deep Learning, are being currently used to solve a wide variety of real problems. However, the models obtained are not easy to understand by scientists, giving rise to the field of eXplainable Artificial Intelligence, which encourage techniques that obtain accurate and understandable models. Fuzzy Association Rules are models that can be understood by themselves, but its interpretability can be improved by representing the same information with fewer and simpler rules. In this work, we propose Meta-Fuzzy Items, which allows to define more generic fuzzy items to represent the same information with fewer rules, and to extend the type of associations that can be represented. Based on this proposal, a new fuzzy data-mining algorithm is presented to extract interesting and interpretable rules from quantitative transactions. The quality of our approach is analyzed using statistical analysis and comparing with a well-known fuzzy data-mining algorithm.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121263730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494500
T. Reddy, Yu-kai Wang, Chin-Teng Lin, Javier Andreu-Perez
Neurons usually converse through electrochemical signals and pooled neuronal firings feasibly be recorded on the scalp through the medium of electroencephalogram (EEG). EEG waveforms are recorded, analysed and categorized across directives concerning a Brain-Computer Interface (BCI). Deteriorated signal to noise ratio and non-stationarities stand as a paramount obstacle in steady decoding of EEG. Appearance of non-stationarities across EEG patterns notably upset the feature waveforms thus worsening the functioning of detection block and as a whole the Brain Computer Interface. Stationary Subspace schemes bring to light subspaces within which data distribution persists stably over time. Current work focuses on the development of a novel spatial transform based feature extraction scheme to address nonstationarity in EEG signals recorded against a drowsiness detection problem (a machine learning regression scenario). The presented approach: F-DIV-IT-JAD-WS derived features distinctly surpassed DivOVR-FuzzyCSP-WS based standard features across RMSE and CC performance criteria pair. We construe that the propounded feature derivation approach based on F-DIV-IT-JAD-WS will usher a significant attention in researchers who are developing algorithms for signal processing, specifically, for BCI regression scenarios.
{"title":"JOINT APPROXIMATE DIAGONALIZATION DIVERGENCE BASED SCHEME FOR EEG DROWSINESS DETECTION BRAIN COMPUTER INTERFACES","authors":"T. Reddy, Yu-kai Wang, Chin-Teng Lin, Javier Andreu-Perez","doi":"10.1109/FUZZ45933.2021.9494500","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494500","url":null,"abstract":"Neurons usually converse through electrochemical signals and pooled neuronal firings feasibly be recorded on the scalp through the medium of electroencephalogram (EEG). EEG waveforms are recorded, analysed and categorized across directives concerning a Brain-Computer Interface (BCI). Deteriorated signal to noise ratio and non-stationarities stand as a paramount obstacle in steady decoding of EEG. Appearance of non-stationarities across EEG patterns notably upset the feature waveforms thus worsening the functioning of detection block and as a whole the Brain Computer Interface. Stationary Subspace schemes bring to light subspaces within which data distribution persists stably over time. Current work focuses on the development of a novel spatial transform based feature extraction scheme to address nonstationarity in EEG signals recorded against a drowsiness detection problem (a machine learning regression scenario). The presented approach: F-DIV-IT-JAD-WS derived features distinctly surpassed DivOVR-FuzzyCSP-WS based standard features across RMSE and CC performance criteria pair. We construe that the propounded feature derivation approach based on F-DIV-IT-JAD-WS will usher a significant attention in researchers who are developing algorithms for signal processing, specifically, for BCI regression scenarios.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122334425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494452
A. C. V. Pinto, Petrônio C. L. Silva, F. Guimarães, Christian Wagner, E. Aguiar
Time series forecasting is an essential research field that provides significant data to help professionals in several areas. Thus, growing research and development in this area have been conducted, aiming at developing new forecasting methods with higher performance levels, but always also with low processing costs. One of this methods is Fuzzy Time Series - FTS. However, one great problem of FTS prediction is how to properly deal with the uncertainty associated to the time series and to model's design. Thus, in this paper we propose a univariate interval type-2 fuzzy time series model combined with the concept of Self-organised Direction Aware Data Partitioning Algorithm (SODA) for universe of discourse partitioning. All experiments were performed using the TAIEX data set and the results were then compared to other forecasting models from literature. A sliding window methodology was applied and the forecast error metric chosen was the Root Mean Squared Error (RMSE) for all methods. SODA-T2FTS results show that it outperformed other forecasting methods confirming that interval type-2 fuzzy logic can be a reliable tool for time series prediction.
{"title":"Self-Organised Direction Aware Data Partitioning for Type-2 Fuzzy Time Series Prediction","authors":"A. C. V. Pinto, Petrônio C. L. Silva, F. Guimarães, Christian Wagner, E. Aguiar","doi":"10.1109/FUZZ45933.2021.9494452","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494452","url":null,"abstract":"Time series forecasting is an essential research field that provides significant data to help professionals in several areas. Thus, growing research and development in this area have been conducted, aiming at developing new forecasting methods with higher performance levels, but always also with low processing costs. One of this methods is Fuzzy Time Series - FTS. However, one great problem of FTS prediction is how to properly deal with the uncertainty associated to the time series and to model's design. Thus, in this paper we propose a univariate interval type-2 fuzzy time series model combined with the concept of Self-organised Direction Aware Data Partitioning Algorithm (SODA) for universe of discourse partitioning. All experiments were performed using the TAIEX data set and the results were then compared to other forecasting models from literature. A sliding window methodology was applied and the forecast error metric chosen was the Root Mean Squared Error (RMSE) for all methods. SODA-T2FTS results show that it outperformed other forecasting methods confirming that interval type-2 fuzzy logic can be a reliable tool for time series prediction.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126583026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494462
A. Buck, Derek T. Anderson, James M. Keller, R. Luke, G. Scott
Three dimensional point cloud data sets are easy to acquire and manipulate, but are often too large to process directly for embedded real-time applications. The spatial information in a point cloud can be represented in a variety of reduced forms, such as voxel grids, Gaussian mixture models, or spatial semantic structures. In this article, we show how a segmented point cloud can be represented as a spatial relationship graph using bounding boxes and triangular fuzzy numbers. This model is a lightweight encoding of the relative distance and direction between objects, and can be used to describe and query for particular spatial configurations using linguistic terms in a multicriteria framework. We show how this approach can be applied on a hand-segmented subset of the NPM3D data set with several illustrative examples. The work herein has useful applications in many applied domains, such as human-robot interaction with unmanned aerial systems.
{"title":"A Fuzzy Spatial Relationship Graph for Point Clouds Using Bounding Boxes","authors":"A. Buck, Derek T. Anderson, James M. Keller, R. Luke, G. Scott","doi":"10.1109/FUZZ45933.2021.9494462","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494462","url":null,"abstract":"Three dimensional point cloud data sets are easy to acquire and manipulate, but are often too large to process directly for embedded real-time applications. The spatial information in a point cloud can be represented in a variety of reduced forms, such as voxel grids, Gaussian mixture models, or spatial semantic structures. In this article, we show how a segmented point cloud can be represented as a spatial relationship graph using bounding boxes and triangular fuzzy numbers. This model is a lightweight encoding of the relative distance and direction between objects, and can be used to describe and query for particular spatial configurations using linguistic terms in a multicriteria framework. We show how this approach can be applied on a hand-segmented subset of the NPM3D data set with several illustrative examples. The work herein has useful applications in many applied domains, such as human-robot interaction with unmanned aerial systems.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126586179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494601
Yifan Wang, H. Ishibuchi, Jihua Zhu, Yaxiong Wang, Tao Dai
Fuzzy systems have proven to be an effective tool for classification and regression. However, they have been mainly applied to supervised tasks. In this paper, we extend fuzzy systems to tackle unsupervised problems based on the manifold regularization framework and convolution/pooling technologies. The proposed fuzzy system, referred to as the unsupervised fuzzy neural network, can extract features from raw images accurately and perform well on image clustering. The main structure of the proposed approach is divided into three parts: fuzzy mapping, unsupervised feature extraction and manifold representation. We adopt K-means to perform clustering in the low-dimensional manifold space. Experimental results on image datasets demonstrate that our approach is competitive with classical and state-of-the-art algorithms. We also identify the relative contributions of each component of the proposed approach in experiments.
{"title":"Unsupervised Fuzzy Neural Network for Image Clustering","authors":"Yifan Wang, H. Ishibuchi, Jihua Zhu, Yaxiong Wang, Tao Dai","doi":"10.1109/FUZZ45933.2021.9494601","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494601","url":null,"abstract":"Fuzzy systems have proven to be an effective tool for classification and regression. However, they have been mainly applied to supervised tasks. In this paper, we extend fuzzy systems to tackle unsupervised problems based on the manifold regularization framework and convolution/pooling technologies. The proposed fuzzy system, referred to as the unsupervised fuzzy neural network, can extract features from raw images accurately and perform well on image clustering. The main structure of the proposed approach is divided into three parts: fuzzy mapping, unsupervised feature extraction and manifold representation. We adopt K-means to perform clustering in the low-dimensional manifold space. Experimental results on image datasets demonstrate that our approach is competitive with classical and state-of-the-art algorithms. We also identify the relative contributions of each component of the proposed approach in experiments.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126594517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494468
Norbert Kukurowski, M. Pazera, M. Witczak
The paper proposes a robust observer-based fault-tolerant tracking control scheme for Takagi-Sugeno fuzzy systems along with its actuator remaining useful life estimation. The difficulty lies in the fact that the system can be occupied by an external disturbances as well as the sensor and actuator faults. A robust stability of the proposed observer and controller is guaranteed by using a quadratic boundedness approach, which uses a simplifying assumption stating that an external disturbances are bounded by an ellipsoid. Subsequently, the actuator remaining useful life scheme for the faulty actuator is developed. Finally, a Takagi-Sugeno fuzzy model of the twin-rotor laboratory system is used to verify the correctness and performance of the proposed strategy.
{"title":"Fault-Tolerant Tracking Control and Remaining Useful Life Estimation for Takagi-Sugeno fuzzy system","authors":"Norbert Kukurowski, M. Pazera, M. Witczak","doi":"10.1109/FUZZ45933.2021.9494468","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494468","url":null,"abstract":"The paper proposes a robust observer-based fault-tolerant tracking control scheme for Takagi-Sugeno fuzzy systems along with its actuator remaining useful life estimation. The difficulty lies in the fact that the system can be occupied by an external disturbances as well as the sensor and actuator faults. A robust stability of the proposed observer and controller is guaranteed by using a quadratic boundedness approach, which uses a simplifying assumption stating that an external disturbances are bounded by an ellipsoid. Subsequently, the actuator remaining useful life scheme for the faulty actuator is developed. Finally, a Takagi-Sugeno fuzzy model of the twin-rotor laboratory system is used to verify the correctness and performance of the proposed strategy.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125448312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494561
Lijie Han, M. Song, W. Pedrycz
In this paper, we propose a new approach to solve linguistic group decision making (GDM) problems through defining different linguistic terms for each expert and optimizing those terms. Information granules are often designed as the framework of linguistic terms and to vividly describe the approach, intervals are selected to express linguistic terms as large, medium, and small in the paper. Analytic Hierarchy Process (AHP) is set as the basic model and abstracted as linguistic reciprocal matrices. The abstraction process is carefully designed considering two strategies: each expert owns same linguistic terms (same distribution of cutting-points in an interval) and each expert owns different linguistic terms. As comparison, three methods of cutting-points allocation for the two strategies are realized with a synthetic example: optimizing allocation, uniform allocation and random allocation. The results coincide with theoretical analysis: each expert owns different linguistic terms reach the highest consensus.
{"title":"An Approach to Determine Best Cutting-points in Group Decision Making Problems with Information Granules","authors":"Lijie Han, M. Song, W. Pedrycz","doi":"10.1109/FUZZ45933.2021.9494561","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494561","url":null,"abstract":"In this paper, we propose a new approach to solve linguistic group decision making (GDM) problems through defining different linguistic terms for each expert and optimizing those terms. Information granules are often designed as the framework of linguistic terms and to vividly describe the approach, intervals are selected to express linguistic terms as large, medium, and small in the paper. Analytic Hierarchy Process (AHP) is set as the basic model and abstracted as linguistic reciprocal matrices. The abstraction process is carefully designed considering two strategies: each expert owns same linguistic terms (same distribution of cutting-points in an interval) and each expert owns different linguistic terms. As comparison, three methods of cutting-points allocation for the two strategies are realized with a synthetic example: optimizing allocation, uniform allocation and random allocation. The results coincide with theoretical analysis: each expert owns different linguistic terms reach the highest consensus.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126497278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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