Pub Date : 2007-07-23DOI: 10.1109/FUZZY.2007.4295593
J. Marín-Blázquez, G. Pérez, M. Pérez
Data-driven construction of fuzzy systems has followed two different approaches. One approach is termed precise (or approximative) fuzzy modelling, that aims at numerical approximation of functions by rules, but that pays little attention to the interpretability of the resulting rule base. On the other side is linguistic (or descriptive) fuzzy modelling, that aims at automatic rule extraction but that uses fixed human provided and linguistically labelled fuzzy sets. This work follows the linguistic fuzzy modelling approach. It uses an extended Classifier System (XCS) as mechanism to extract linguistic fuzzy rules. XCS is one of the most successful accuracy-based learning classifier systems. It provides several mechanisms for rule generalization and also allows for online training if necessary. It can be used in sequential and non-sequential tasks. Although originally applied in discrete domains it has been extended to continuous and fuzzy environments. The proposed Linguistic Fuzzy XCS has been applied to several well-known classification problems and the results compared with both, precise and linguistic fuzzy models.
{"title":"A Linguistic Fuzzy-XCS classifier system","authors":"J. Marín-Blázquez, G. Pérez, M. Pérez","doi":"10.1109/FUZZY.2007.4295593","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295593","url":null,"abstract":"Data-driven construction of fuzzy systems has followed two different approaches. One approach is termed precise (or approximative) fuzzy modelling, that aims at numerical approximation of functions by rules, but that pays little attention to the interpretability of the resulting rule base. On the other side is linguistic (or descriptive) fuzzy modelling, that aims at automatic rule extraction but that uses fixed human provided and linguistically labelled fuzzy sets. This work follows the linguistic fuzzy modelling approach. It uses an extended Classifier System (XCS) as mechanism to extract linguistic fuzzy rules. XCS is one of the most successful accuracy-based learning classifier systems. It provides several mechanisms for rule generalization and also allows for online training if necessary. It can be used in sequential and non-sequential tasks. Although originally applied in discrete domains it has been extended to continuous and fuzzy environments. The proposed Linguistic Fuzzy XCS has been applied to several well-known classification problems and the results compared with both, precise and linguistic fuzzy models.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125011726","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295460
M. Yildirim, Alper Bastürk, M. E. Yüksel
A novel filtering operator based on type-2 fuzzy logic techniques is proposed for detail preserving restoration of impulse noise corrupted images. The performance of the proposed operator is tested for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise removal operators from the literature. Experimental results show that the proposed operator exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.
{"title":"A Detail-Preserving Type-2 Fuzzy Logic Filter for Impulse Noise Removal from Digital Images","authors":"M. Yildirim, Alper Bastürk, M. E. Yüksel","doi":"10.1109/FUZZY.2007.4295460","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295460","url":null,"abstract":"A novel filtering operator based on type-2 fuzzy logic techniques is proposed for detail preserving restoration of impulse noise corrupted images. The performance of the proposed operator is tested for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise removal operators from the literature. Experimental results show that the proposed operator exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131132528","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295471
Alexandre Evsukoff
This paper presents a method for designing fuzzy rule based systems for pattern recognition. The resulting model is interpretable as linguistic rules and can be used for deep understanding of data. The classifier performance is optimized in the least squares sense and the model complexity is minimized in a structure selection search, performed by a genetic algorithm The method is tested against benchmark classification problems found in the literature, with good results.
{"title":"Learning Fuzzy Rule Based Classifier with Rule Weights Optimization and Structure Selection by a Genetic Algorithm","authors":"Alexandre Evsukoff","doi":"10.1109/FUZZY.2007.4295471","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295471","url":null,"abstract":"This paper presents a method for designing fuzzy rule based systems for pattern recognition. The resulting model is interpretable as linguistic rules and can be used for deep understanding of data. The classifier performance is optimized in the least squares sense and the model complexity is minimized in a structure selection search, performed by a genetic algorithm The method is tested against benchmark classification problems found in the literature, with good results.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129914617","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295641
M. Falda
This paper describes a framework for temporal reasoning that allows managing a restricted form of disjunctive temporal constraints without making the modelled problems intractable as in the case of general DTPs. This is obtained by assigning classes to the constraints and by allowing only one constraint per class, in order to build a collection of independent STPs that can share sub-problems and therefore allows increasing algorithm efficiency. The model proposed is directly applied to fuzzy constraint satisfaction problems and can be solved using an extended fuzzy path-consistency algorithm, also presented in the paper. A simple application to medical diagnosis shows its expressive power over previous tractable temporal reasoning models.
{"title":"Fuzzy Disjunctive Temporal Problems with Classes","authors":"M. Falda","doi":"10.1109/FUZZY.2007.4295641","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295641","url":null,"abstract":"This paper describes a framework for temporal reasoning that allows managing a restricted form of disjunctive temporal constraints without making the modelled problems intractable as in the case of general DTPs. This is obtained by assigning classes to the constraints and by allowing only one constraint per class, in order to build a collection of independent STPs that can share sub-problems and therefore allows increasing algorithm efficiency. The model proposed is directly applied to fuzzy constraint satisfaction problems and can be solved using an extended fuzzy path-consistency algorithm, also presented in the paper. A simple application to medical diagnosis shows its expressive power over previous tractable temporal reasoning models.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121679808","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295513
C. Fayad, J. Garibaldi, D. Ouelhadj
This paper considers the problem of grid scheduling in which different jobs are assigned to different processors, and a scheduling algorithm is devised, using tabu search, to find optimal solutions in order to maximize the number of scheduled jobs. However, inherent in the nature of the application, the processing times of jobs are not precise but are estimates that vary between minimal values, in case of premature failure of jobs, to maximal values as specified 'a priori' by well-experienced users. Fuzzy methodology becomes instrumental in this application as it allows the use of fuzzy sets to represent the processing times of jobs, modelling their uncertainty. This work presents the implementation of a tabu search algorithm to create good schedules and explores the robustness of the schedule when processing times do vary by assessing its performance in both fuzzy and crisp modes. Finally, the impact of changing the shapes of fuzzy completion times and the average job length on the schedule performance is discussed.
{"title":"Fuzzy Grid Scheduling Using Tabu Search","authors":"C. Fayad, J. Garibaldi, D. Ouelhadj","doi":"10.1109/FUZZY.2007.4295513","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295513","url":null,"abstract":"This paper considers the problem of grid scheduling in which different jobs are assigned to different processors, and a scheduling algorithm is devised, using tabu search, to find optimal solutions in order to maximize the number of scheduled jobs. However, inherent in the nature of the application, the processing times of jobs are not precise but are estimates that vary between minimal values, in case of premature failure of jobs, to maximal values as specified 'a priori' by well-experienced users. Fuzzy methodology becomes instrumental in this application as it allows the use of fuzzy sets to represent the processing times of jobs, modelling their uncertainty. This work presents the implementation of a tabu search algorithm to create good schedules and explores the robustness of the schedule when processing times do vary by assessing its performance in both fuzzy and crisp modes. Finally, the impact of changing the shapes of fuzzy completion times and the average job length on the schedule performance is discussed.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122302904","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295375
M. S. Abadeh, J. Habibi
The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). The objective of this paper is to extract fuzzy classification rules for intrusion detection in computer networks. The proposed method is based on the iterative rule learning approach (IRL) to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The performance of final fuzzy classification system has been investigated using intrusion detection problem as a high-dimensional classification problem. Results show that the presented algorithm produces fuzzy rules, which can be used to construct a reliable intrusion detection system.
{"title":"Computer Intrusion Detection Using an Iterative Fuzzy Rule Learning Approach","authors":"M. S. Abadeh, J. Habibi","doi":"10.1109/FUZZY.2007.4295375","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295375","url":null,"abstract":"The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). The objective of this paper is to extract fuzzy classification rules for intrusion detection in computer networks. The proposed method is based on the iterative rule learning approach (IRL) to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The performance of final fuzzy classification system has been investigated using intrusion detection problem as a high-dimensional classification problem. Results show that the presented algorithm produces fuzzy rules, which can be used to construct a reliable intrusion detection system.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"241 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122375421","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295553
M. Taherbaneh, Hasan Ghafori Frard, A. Rezaie, Shahab Karbasian
Solar panels are power sources in photovoltaic applications. Solar panels I-V curves depend on environmental conditions such as irradiance, temperature, load and degradation level. In this paper, design and implementation of simultaneous fuzzy-based maximum power point tracker (MPPT) and sun tracker are presented for deployable solar panels. A digital controller was implemented by an AVR microcontroller. Results showed that the proposed system ensure to have photovoltaic system with higher efficiency. Finally, we observed that, using the proposed fuzzy-based MPP tracking and sun tracking simultaneously, solar panel output power can be remarkably increased leading in turn to reduction of the size, weight and cost of solar panels in photovoltaic systems.
{"title":"Combination of Fuzzy-Based Maximum Power Point Tracker and Sun Tracker for Deployable Solar Panels in Photovoltaic Systems","authors":"M. Taherbaneh, Hasan Ghafori Frard, A. Rezaie, Shahab Karbasian","doi":"10.1109/FUZZY.2007.4295553","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295553","url":null,"abstract":"Solar panels are power sources in photovoltaic applications. Solar panels I-V curves depend on environmental conditions such as irradiance, temperature, load and degradation level. In this paper, design and implementation of simultaneous fuzzy-based maximum power point tracker (MPPT) and sun tracker are presented for deployable solar panels. A digital controller was implemented by an AVR microcontroller. Results showed that the proposed system ensure to have photovoltaic system with higher efficiency. Finally, we observed that, using the proposed fuzzy-based MPP tracking and sun tracking simultaneously, solar panel output power can be remarkably increased leading in turn to reduction of the size, weight and cost of solar panels in photovoltaic systems.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127125181","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295333
Dongrui Wu, J. Mendel
Fuzzy logic is frequently used in computing with words (CWW). When input words to a CWW engine are modeled by interval type-2 fuzzy sets (IT2 FSs), the CWW engine's output can also be an IT2 FS, A tilde, which needs to be mapped to a linguistic label so that it can be understood. Because each linguistic label is represented by an IT2 FS Bi, there is a need to compare the similarity of A tilde and B tildei to find the B tildei most similar to A tilde. In this paper, a vector similarity measure (VSM) is proposed for IT2 FSs, whose two elements measure the similarity in shape and proximity, respectively. A comparative study shows that the VSM gives more reasonable results than all other existing similarity measures for IT2 FSs.
{"title":"A Vector Similarity Measure for Interval Type-2 Fuzzy Sets","authors":"Dongrui Wu, J. Mendel","doi":"10.1109/FUZZY.2007.4295333","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295333","url":null,"abstract":"Fuzzy logic is frequently used in computing with words (CWW). When input words to a CWW engine are modeled by interval type-2 fuzzy sets (IT2 FSs), the CWW engine's output can also be an IT2 FS, A tilde, which needs to be mapped to a linguistic label so that it can be understood. Because each linguistic label is represented by an IT2 FS Bi, there is a need to compare the similarity of A tilde and B tildei to find the B tildei most similar to A tilde. In this paper, a vector similarity measure (VSM) is proposed for IT2 FSs, whose two elements measure the similarity in shape and proximity, respectively. A comparative study shows that the VSM gives more reasonable results than all other existing similarity measures for IT2 FSs.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124074368","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295604
J. Montes, R.M. Llorca, L. Fernandez
A novel approach for the development of linguistically interpretable fuzzy models from data is proposed. Based on this approach a methodology for inverse and indirect adaptive fuzzy control is presented. The proposed methodology includes clustering techniques to determine rules, the minimum squares method to adjust consequents and, for a sharp tuning, the descendant gradient to adjust the modal values of sets that confirm the antecedent. The antecedent partition uses triangular sets with 0.5 interpolations. The most promissory aspect in our proposal consists in achieving a great precision without sacrificing the fuzzy system interpretability. The real-world applicability of the proposed approach is demonstrated by application to a classic benchmark in system modeling and identification (Box-Jenkins gas furnace) and to a temperature control of a food process.
{"title":"Interpretable Fuzzy Models from Data and Adaptive Fuzzy Control: A New Approach","authors":"J. Montes, R.M. Llorca, L. Fernandez","doi":"10.1109/FUZZY.2007.4295604","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295604","url":null,"abstract":"A novel approach for the development of linguistically interpretable fuzzy models from data is proposed. Based on this approach a methodology for inverse and indirect adaptive fuzzy control is presented. The proposed methodology includes clustering techniques to determine rules, the minimum squares method to adjust consequents and, for a sharp tuning, the descendant gradient to adjust the modal values of sets that confirm the antecedent. The antecedent partition uses triangular sets with 0.5 interpolations. The most promissory aspect in our proposal consists in achieving a great precision without sacrificing the fuzzy system interpretability. The real-world applicability of the proposed approach is demonstrated by application to a classic benchmark in system modeling and identification (Box-Jenkins gas furnace) and to a temperature control of a food process.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124076214","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 : 2007-07-23DOI: 10.1109/FUZZY.2007.4295670
Caixia Wang, A. Croitoru, A. Stefanidis, P. Agouris
The registration of imagery to maps and GIS layers is a fundamental operation for the management of spatial data in GIS. This paper introduces automated algorithms for the registration of sequences of aerial imagery to vector map data using linear features (primarily roads) as control information. Our algorithms support both the use of single elements as well as complete networks. Regarding single elements, our method is based on the extraction of linear features using active contour models (a.k.a. snakes), followed by the construction of a polygonal template upon which a matching process is applied. To accommodate more robust matching, this work presents both exact and inexact matching schemes for linear features. Additionally, in order to overcome the influence of the snakes-based extraction process on the matching results, a matching refinement process is suggested. This information is used to generate image mosaics and register these mosaics to a map. The performance of the proposed scheme was tested on sequences of aerial imagery of 1 m resolution that were subjected to shifts and rotations using both the exact and inexact matching scheme, and was shown to produce angular accuracies of less than 0.7 degrees and positional accuracies of less than 2 pixels.
{"title":"Image-to-X Registration using Linear Features","authors":"Caixia Wang, A. Croitoru, A. Stefanidis, P. Agouris","doi":"10.1109/FUZZY.2007.4295670","DOIUrl":"https://doi.org/10.1109/FUZZY.2007.4295670","url":null,"abstract":"The registration of imagery to maps and GIS layers is a fundamental operation for the management of spatial data in GIS. This paper introduces automated algorithms for the registration of sequences of aerial imagery to vector map data using linear features (primarily roads) as control information. Our algorithms support both the use of single elements as well as complete networks. Regarding single elements, our method is based on the extraction of linear features using active contour models (a.k.a. snakes), followed by the construction of a polygonal template upon which a matching process is applied. To accommodate more robust matching, this work presents both exact and inexact matching schemes for linear features. Additionally, in order to overcome the influence of the snakes-based extraction process on the matching results, a matching refinement process is suggested. This information is used to generate image mosaics and register these mosaics to a map. The performance of the proposed scheme was tested on sequences of aerial imagery of 1 m resolution that were subjected to shifts and rotations using both the exact and inexact matching scheme, and was shown to produce angular accuracies of less than 0.7 degrees and positional accuracies of less than 2 pixels.","PeriodicalId":236515,"journal":{"name":"2007 IEEE International Fuzzy Systems Conference","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126759234","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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