Pub Date : 2021-07-11DOI: 10.1109/FUZZ45933.2021.9494421
Pedro Messias, Maria João Sousa, Alexandra Moutinho
Image-based fire detection is a safety-critical task, which requires high-quality datasets to ensure performance guarantees in real scenarios. Automatic fire detection systems are in ever-increasing demand, but the limited number and size of open datasets, and lack of annotations, hinder model development. Solving this issue requires that experts dedicate a significant time to classify and segment fire events in image datasets. Towards building large-scale curated datasets, this paper presents a data annotation method that leverages semantic segmentation based on superpixel aggregation and color features. The approach introduces interpretable linguistic models that generate pixel-wise fire segmentation and annotations, which are explainable through simple fine-tunable rules that can support subsequent annotation validation by fire domain experts. The performance of the proposed algorithm is evaluated for relevant scenarios using a publicly available dataset, namely through the assessment of the segmentation quality and the labeling of fire color categories. The outcomes of this approach pave the way for creating large-scale datasets that can empower future deployments of learning-based architectures in fire detection systems.
{"title":"Color-based Superpixel Semantic Segmentation for Fire Data Annotation","authors":"Pedro Messias, Maria João Sousa, Alexandra Moutinho","doi":"10.1109/FUZZ45933.2021.9494421","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494421","url":null,"abstract":"Image-based fire detection is a safety-critical task, which requires high-quality datasets to ensure performance guarantees in real scenarios. Automatic fire detection systems are in ever-increasing demand, but the limited number and size of open datasets, and lack of annotations, hinder model development. Solving this issue requires that experts dedicate a significant time to classify and segment fire events in image datasets. Towards building large-scale curated datasets, this paper presents a data annotation method that leverages semantic segmentation based on superpixel aggregation and color features. The approach introduces interpretable linguistic models that generate pixel-wise fire segmentation and annotations, which are explainable through simple fine-tunable rules that can support subsequent annotation validation by fire domain experts. The performance of the proposed algorithm is evaluated for relevant scenarios using a publicly available dataset, namely through the assessment of the segmentation quality and the labeling of fire color categories. The outcomes of this approach pave the way for creating large-scale datasets that can empower future deployments of learning-based architectures in fire detection systems.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"27 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120860116","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/FUZZ45933.2021.9494557
D. Sharma, Prashant K. Gupta, Javier Andreu-Perez, J. Mendel, Luis Martínez-López
Computing with Words (CWW) methodology has been used to design intelligent systems which make decisions by manipulating the linguistic information, like human beings. Human beings naturally understand (and express) themselves linguistically, and hence can reason (and make decision) just with linguistic information without any numerical measure. Perceptual Computing makes use of type 2 fuzzy sets for modeling the words in the CWW paradigm. This use of type-2 fuzzy sets enables better representation of the inherent uncertainty in the fuzzy linguistic semantics on numerous problems. To realise the potential of Perceptual Computing, its MATLAB implementation has been made freely available to the end-users/ researchers, and MATLAB is a proprietary development environment. Therefore, this contribution aims at proposing a python implementation of the Perceptual Computing, or its main processing element the perceptual computer that consists of three components viz., encoder, CWW engine and decoder. Our python implementation provides the end user with a seamless blending amongst all three components, which does not exist yet, to the best of our knowledge.
{"title":"A Python Software Library for Computing with Words and Perceptions","authors":"D. Sharma, Prashant K. Gupta, Javier Andreu-Perez, J. Mendel, Luis Martínez-López","doi":"10.1109/FUZZ45933.2021.9494557","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494557","url":null,"abstract":"Computing with Words (CWW) methodology has been used to design intelligent systems which make decisions by manipulating the linguistic information, like human beings. Human beings naturally understand (and express) themselves linguistically, and hence can reason (and make decision) just with linguistic information without any numerical measure. Perceptual Computing makes use of type 2 fuzzy sets for modeling the words in the CWW paradigm. This use of type-2 fuzzy sets enables better representation of the inherent uncertainty in the fuzzy linguistic semantics on numerous problems. To realise the potential of Perceptual Computing, its MATLAB implementation has been made freely available to the end-users/ researchers, and MATLAB is a proprietary development environment. Therefore, this contribution aims at proposing a python implementation of the Perceptual Computing, or its main processing element the perceptual computer that consists of three components viz., encoder, CWW engine and decoder. Our python implementation provides the end user with a seamless blending amongst all three components, which does not exist yet, to the best of our knowledge.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"57 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":"124520619","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/FUZZ45933.2021.9494547
S. Aguzzoli, P. Codara
The algebraic semantics of Gödel propositional logic is given by the variety of Gödel algebras, which in turns form a category dually equivalent to the pro-finite completion of the category of finite forests and order-preserving open maps. Forests provide a sound and complete semantics for propositional infinite-valued Gödel logic, while propositional k-valued Gödel logic is sound and complete for forests of height at most $k-1$. In this work we shall mainly deal with three-valued Gödel logic. We shall show that the subcategory of forests of height at most 2 (bushes) forms an elementary topos, thus providing naturally a generalisation to bushes of all classical first-order set concepts, suitable for developing a first-order three-valued Gödel logic semantics based on bush concepts instead of sets.
{"title":"Towards an Algebraic Topos Semantics for Three-valued Gödel Logic","authors":"S. Aguzzoli, P. Codara","doi":"10.1109/FUZZ45933.2021.9494547","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494547","url":null,"abstract":"The algebraic semantics of Gödel propositional logic is given by the variety of Gödel algebras, which in turns form a category dually equivalent to the pro-finite completion of the category of finite forests and order-preserving open maps. Forests provide a sound and complete semantics for propositional infinite-valued Gödel logic, while propositional k-valued Gödel logic is sound and complete for forests of height at most $k-1$. In this work we shall mainly deal with three-valued Gödel logic. We shall show that the subcategory of forests of height at most 2 (bushes) forms an elementary topos, thus providing naturally a generalisation to bushes of all classical first-order set concepts, suitable for developing a first-order three-valued Gödel logic semantics based on bush concepts instead of sets.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"203 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":"124545260","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/FUZZ45933.2021.9494483
Francisco J. Rodríguez-Lozano, J. C. Gámez-Granados, O. Baños, J. Alcalá-Fdez, J. M. Soto-Hidalgo
Internet of Things enables sensors and actuators to share heterogeneous data between different devices. Such data can be used to create intelligent systems to control diverse structures available in houses, cities, or industrial environments among others. In this context, one of the most used approaches to handle these intelligent systems is based on Fuzzy Rule-Based Systems (FRBS) due to their suitability for addressing complex data and managing their imprecision. However, most of the current developments in this area are usually ad-hoc solutions limited by the intercommunication between FRBS and IoT devices. This results into significant challenges in reusing these solutions to solve latent problems. To bridge this gap, a new module for the open source library JFML is proposed to offer a complete implementation of an IoT infrastructure to develop intelligent IoT solutions based on the IEEE std 1855–2016. Moreover, a case study with real IoT devices is presented to showcase the use of the proposed module.
{"title":"An approach to bridge the gap between ubiquitous embedded devices and JFML: A new module for Internet of Things","authors":"Francisco J. Rodríguez-Lozano, J. C. Gámez-Granados, O. Baños, J. Alcalá-Fdez, J. M. Soto-Hidalgo","doi":"10.1109/FUZZ45933.2021.9494483","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494483","url":null,"abstract":"Internet of Things enables sensors and actuators to share heterogeneous data between different devices. Such data can be used to create intelligent systems to control diverse structures available in houses, cities, or industrial environments among others. In this context, one of the most used approaches to handle these intelligent systems is based on Fuzzy Rule-Based Systems (FRBS) due to their suitability for addressing complex data and managing their imprecision. However, most of the current developments in this area are usually ad-hoc solutions limited by the intercommunication between FRBS and IoT devices. This results into significant challenges in reusing these solutions to solve latent problems. To bridge this gap, a new module for the open source library JFML is proposed to offer a complete implementation of an IoT infrastructure to develop intelligent IoT solutions based on the IEEE std 1855–2016. Moreover, a case study with real IoT devices is presented to showcase the use of the proposed module.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"68 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":"124553755","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/FUZZ45933.2021.9494595
Barbara Pekala, Krzysztof Dyczkowski, Jaroslaw Szkola, Dawid Kosior
The article deals with the problem of selecting the most appropriate attributes for a given classification method with the use of inclusion and similarity measures for interval-valued fuzzy sets. These types of measures with uncertainty were introduced using partial or linear order. The article introduces a modified IV-Relief algorithm using the above-mentioned measures. The theoretical considerations were supported by the analysis of the effectiveness of the proposed algorithm on a well-known dataset on breast cancer diagnostics. The proposed methods make it possible to extend the recognized classification methods so that they operate on uncertain data.
{"title":"Classification of uncertain data with a selection of relevant features based on similarities measures of Interval-Valued Fuzzy Sets","authors":"Barbara Pekala, Krzysztof Dyczkowski, Jaroslaw Szkola, Dawid Kosior","doi":"10.1109/FUZZ45933.2021.9494595","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494595","url":null,"abstract":"The article deals with the problem of selecting the most appropriate attributes for a given classification method with the use of inclusion and similarity measures for interval-valued fuzzy sets. These types of measures with uncertainty were introduced using partial or linear order. The article introduces a modified IV-Relief algorithm using the above-mentioned measures. The theoretical considerations were supported by the analysis of the effectiveness of the proposed algorithm on a well-known dataset on breast cancer diagnostics. The proposed methods make it possible to extend the recognized classification methods so that they operate on uncertain data.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"234 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":"124574294","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/FUZZ45933.2021.9494391
Lewis Veryard, H. Hagras, A. Conway, G. Owusu
In this paper, we present a novel Type-2 fuzzy multi-objective multi-chromosomal optimisation algorithm for capacity planning within telecommunication networks. The proposed system is compared to one of the most successful multi-objective optimisation algorithms which is NSGA-II. This comparison shows that in the capacity planning problems the proposed algorithm can produce a better solution front than NSGA-II in 80% - 93 % of cases. Additionally the use of Type-2 fuzzy logic produces a better solution front in 72% of cases when compared to using Type-1 fuzzy logic.
{"title":"A Type-2 Fuzzy Multi-Objective Multi-Chromosomal Optimisation for Capacity Planning within Telecommunication Networks","authors":"Lewis Veryard, H. Hagras, A. Conway, G. Owusu","doi":"10.1109/FUZZ45933.2021.9494391","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494391","url":null,"abstract":"In this paper, we present a novel Type-2 fuzzy multi-objective multi-chromosomal optimisation algorithm for capacity planning within telecommunication networks. The proposed system is compared to one of the most successful multi-objective optimisation algorithms which is NSGA-II. This comparison shows that in the capacity planning problems the proposed algorithm can produce a better solution front than NSGA-II in 80% - 93 % of cases. Additionally the use of Type-2 fuzzy logic produces a better solution front in 72% of cases when compared to using Type-1 fuzzy logic.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"38 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":"132609378","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/FUZZ45933.2021.9494469
M. Badurowicz, Paweł Karczmarek, J. Montusiewicz
In the presented paper the authors are showing the usage of fuzzy extensions of isolations forests for detecting road anomalies like potholes. Using the data acquired by the accelerometer in the smartphone and the proper smartphone application, the vibrations while driving over road were analyzed using multiple variants of extended isolation forests - n-ary (NIF), with fuzzy membership function (MIF), with k-means clustering (KIF), with two fuzzy clusters incorporated (CIF) or two fuzzy clusters and the distance to the cluster center (prototype) utilized (C2DIF). The presented research shows that in comparison to the state-of-the-art methods previously discussed by the authors, the accuracy and false positive rate have improved, while the sensitivity has been improved to reach 100%.
{"title":"Fuzzy Extensions of Isolation Forests for Road Anomaly Detection","authors":"M. Badurowicz, Paweł Karczmarek, J. Montusiewicz","doi":"10.1109/FUZZ45933.2021.9494469","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494469","url":null,"abstract":"In the presented paper the authors are showing the usage of fuzzy extensions of isolations forests for detecting road anomalies like potholes. Using the data acquired by the accelerometer in the smartphone and the proper smartphone application, the vibrations while driving over road were analyzed using multiple variants of extended isolation forests - n-ary (NIF), with fuzzy membership function (MIF), with k-means clustering (KIF), with two fuzzy clusters incorporated (CIF) or two fuzzy clusters and the distance to the cluster center (prototype) utilized (C2DIF). The presented research shows that in comparison to the state-of-the-art methods previously discussed by the authors, the accuracy and false positive rate have improved, while the sensitivity has been improved to reach 100%.","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":"130801736","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/FUZZ45933.2021.9494344
Namal Rathnayake, Tuan Linh Dang, Y. Hoshino
Performing an accurate and smooth trajectory of a quad-copter is a crucial aspect in autonomous controls due to its non-linearity and under-actuated characteristic. Adaptive Neuro-Fuzzy Inference System (ANFIS) is well-known for nonlinear controls. This paper focuses on comparing the performance of ANFIS based quad-copter systems to identify the best optimization algorithm. Two famous algorithms called Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) was used as the optimization algorithms and to tune the gains of the Fuzzy Inference Systems (FIS). The analysis was performed using two different simulations namely, altitude control and trajectory navigation. The final results were compared between traditional PID, conventional ANFIS, GA-ANFIS and PSO-ANFIS. PSO-ANFIS obtained the highest performance in our experiments.
{"title":"Performance Comparison of the ANFIS based Quad-Copter Controller Algorithms","authors":"Namal Rathnayake, Tuan Linh Dang, Y. Hoshino","doi":"10.1109/FUZZ45933.2021.9494344","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494344","url":null,"abstract":"Performing an accurate and smooth trajectory of a quad-copter is a crucial aspect in autonomous controls due to its non-linearity and under-actuated characteristic. Adaptive Neuro-Fuzzy Inference System (ANFIS) is well-known for nonlinear controls. This paper focuses on comparing the performance of ANFIS based quad-copter systems to identify the best optimization algorithm. Two famous algorithms called Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) was used as the optimization algorithms and to tune the gains of the Fuzzy Inference Systems (FIS). The analysis was performed using two different simulations namely, altitude control and trajectory navigation. The final results were compared between traditional PID, conventional ANFIS, GA-ANFIS and PSO-ANFIS. PSO-ANFIS obtained the highest performance in our experiments.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"46 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":"128744616","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/FUZZ45933.2021.9494513
João C. B. Oliveira, Ricardo Rios, E. Almeida, C. Sant'Anna, T. N. Rios
Source code quality plays a key role in software quality mainly due to its impact on software maintainability. Software engineers have been using source code metrics to support them to assess source code quality. Source code metrics quantify different source code characteristics. However, source code metric analysis still involves subjectivity. For instance, it is not trivial to decide whether a metric value is high or low. To reduce the eventual subjectivity of source code metrics analysis, several researchers are using Machine Learning algorithms. Therefore, in this paper, we designed a Fuzzy-based approach to extract characteristics and patterns present in source code versioning repositories in order to: i) assist the specialist in the interpretation of releases, especially when working with large volumes of source code; ii) from the release interpretation, specialists can improve the quality of the source code; and iii) monitor the evolution of the software as new releases are submitted to the repositories. We evaluated the proposed approach with the Linux Test Project repository, emphasizing the interpretability of large source code versioning repositories.
{"title":"Fuzzy Software Analyzer (FSA): A New Approach for Interpreting Source Code Versioning Repositories","authors":"João C. B. Oliveira, Ricardo Rios, E. Almeida, C. Sant'Anna, T. N. Rios","doi":"10.1109/FUZZ45933.2021.9494513","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494513","url":null,"abstract":"Source code quality plays a key role in software quality mainly due to its impact on software maintainability. Software engineers have been using source code metrics to support them to assess source code quality. Source code metrics quantify different source code characteristics. However, source code metric analysis still involves subjectivity. For instance, it is not trivial to decide whether a metric value is high or low. To reduce the eventual subjectivity of source code metrics analysis, several researchers are using Machine Learning algorithms. Therefore, in this paper, we designed a Fuzzy-based approach to extract characteristics and patterns present in source code versioning repositories in order to: i) assist the specialist in the interpretation of releases, especially when working with large volumes of source code; ii) from the release interpretation, specialists can improve the quality of the source code; and iii) monitor the evolution of the software as new releases are submitted to the repositories. We evaluated the proposed approach with the Linux Test Project repository, emphasizing the interpretability of large source code versioning repositories.","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":"126832956","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/FUZZ45933.2021.9494472
Chao Chen, Yu Zhao, Christian Wagner, Direnc Pekaslan, J. Garibaldi
Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition approaches to modelling the interaction between the non-singleton input and the antecedent fuzzy sets enable the efficient handling of uncertainty without requiring changes in a system's rule base, with benefits both in terms of performance and interpretability. As thus far few current software toolkit support non-singleton fuzzy systems, this paper presents an extension of the FuzzyR toolbox, which is a freely available R package on CRAN, for non-singleton fuzzy logic systems. The updated toolbox enables a non-singleton model to be conveniently built from scratch, or for existing singleton fuzzy logic systems built using FuzzyR to be converted easily. Predefined operations include fuzzification of crisp inputs (e.g. into Gaussian membership functions), and a variety of composition approaches for computing rules' firing-strengths, based on the standard, centroid-based, and similarity-based methods. It is also possible to include user-defined options for these abovementioned methods, without the need to modify (or update) the FuzzyR toolbox itself. In this paper, detailed introductions for the new non-singleton features of the toolkit are presented, complete with code samples in R to facilitate adoption both within and beyond the community. Further, the paper presents a series of validation experiments, replicating a recent empirical analysis of non-singleton fuzzy logic systems in the context of time-series prediction with different levels of noise.
{"title":"An Extension of the FuzzyR Toolbox for Non-Singleton Fuzzy Logic Systems","authors":"Chao Chen, Yu Zhao, Christian Wagner, Direnc Pekaslan, J. Garibaldi","doi":"10.1109/FUZZ45933.2021.9494472","DOIUrl":"https://doi.org/10.1109/FUZZ45933.2021.9494472","url":null,"abstract":"Recent years have seen a surge in interest in non-singleton fuzzy systems. These systems enable the direct modelling of uncertainty affecting systems' inputs using the fuzzification stage. Moreover, recent work has shown how different composition approaches to modelling the interaction between the non-singleton input and the antecedent fuzzy sets enable the efficient handling of uncertainty without requiring changes in a system's rule base, with benefits both in terms of performance and interpretability. As thus far few current software toolkit support non-singleton fuzzy systems, this paper presents an extension of the FuzzyR toolbox, which is a freely available R package on CRAN, for non-singleton fuzzy logic systems. The updated toolbox enables a non-singleton model to be conveniently built from scratch, or for existing singleton fuzzy logic systems built using FuzzyR to be converted easily. Predefined operations include fuzzification of crisp inputs (e.g. into Gaussian membership functions), and a variety of composition approaches for computing rules' firing-strengths, based on the standard, centroid-based, and similarity-based methods. It is also possible to include user-defined options for these abovementioned methods, without the need to modify (or update) the FuzzyR toolbox itself. In this paper, detailed introductions for the new non-singleton features of the toolkit are presented, complete with code samples in R to facilitate adoption both within and beyond the community. Further, the paper presents a series of validation experiments, replicating a recent empirical analysis of non-singleton fuzzy logic systems in the context of time-series prediction with different levels of noise.","PeriodicalId":151289,"journal":{"name":"2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","volume":"26 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":"114775345","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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