Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277313
A. Aziz
Fuzzy systems have been proven very successfully in many important applications and are rapidly growing to become a powerful technique for multisenosr-multitarget data fusion. The functional paradigm for fuzzy multisenosr-multitarget data fusion consists of fuzzification, fuzzy knowledge-base, fuzzy inference mechanism, and defuzzification. In fuzzy system design, users start with some fuzzy rules, which are chosen heuristically based on their experience, and membership functions, which in many cases are chosen subjectively based on understanding the problem, and they use the developed system to tune these rules and membership functions. Constructing membership function is the most important step in the fuzzy system design. This paper addresses the problem of constructing the optimal membership functions from input data in a multisenosr-multitarget environment. This analysis has been applied to clustering of multisensor information in a two-dimensional multisenosr-multitarget data fusion system. Clustering performance using optimal membership functions is compared to that of clustering using non-optimal membership functions. The results show that there is a significant performance improvement when using optimal membership functions.
{"title":"Effects of fuzzy membership function shapes on clustering performance in multisensor-multitarget data fusion systems","authors":"A. Aziz","doi":"10.1109/FUZZY.2009.5277313","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277313","url":null,"abstract":"Fuzzy systems have been proven very successfully in many important applications and are rapidly growing to become a powerful technique for multisenosr-multitarget data fusion. The functional paradigm for fuzzy multisenosr-multitarget data fusion consists of fuzzification, fuzzy knowledge-base, fuzzy inference mechanism, and defuzzification. In fuzzy system design, users start with some fuzzy rules, which are chosen heuristically based on their experience, and membership functions, which in many cases are chosen subjectively based on understanding the problem, and they use the developed system to tune these rules and membership functions. Constructing membership function is the most important step in the fuzzy system design. This paper addresses the problem of constructing the optimal membership functions from input data in a multisenosr-multitarget environment. This analysis has been applied to clustering of multisensor information in a two-dimensional multisenosr-multitarget data fusion system. Clustering performance using optimal membership functions is compared to that of clustering using non-optimal membership functions. The results show that there is a significant performance improvement when using optimal membership functions.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115072925","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277406
Wei-Yen Wang, Ming-Chang Chen, Yi-Hsing Chien, Tsu-Tian Lee
Vehicles are not always driven on smooth roads. If parts of the suspension system fail, it becomes an uncertain system. Thus we need an approximator to remodel this uncertain system to maintain good control. In this paper, we propose a new method to on-line identify the uncertain suspension system and design a T-S fuzzy-neural controller to control it. We first use the mean value theorem to transform the active suspension system into a virtual linearized system. In addition, an on-line adaptive T-S fuzzy-neural modeling approach to the design of robust tracking controllers is developed for the uncertain active suspension system. Finally, this paper gives simulation results of an uncertain suspension system with the on-line adaptive T-S fuzzy-neural controller, and is shown to provide good effectiveness under the conditions that parts of the suspension system fail.
{"title":"On-line adaptive T-S fuzzy neural control for active suspension systems","authors":"Wei-Yen Wang, Ming-Chang Chen, Yi-Hsing Chien, Tsu-Tian Lee","doi":"10.1109/FUZZY.2009.5277406","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277406","url":null,"abstract":"Vehicles are not always driven on smooth roads. If parts of the suspension system fail, it becomes an uncertain system. Thus we need an approximator to remodel this uncertain system to maintain good control. In this paper, we propose a new method to on-line identify the uncertain suspension system and design a T-S fuzzy-neural controller to control it. We first use the mean value theorem to transform the active suspension system into a virtual linearized system. In addition, an on-line adaptive T-S fuzzy-neural modeling approach to the design of robust tracking controllers is developed for the uncertain active suspension system. Finally, this paper gives simulation results of an uncertain suspension system with the on-line adaptive T-S fuzzy-neural controller, and is shown to provide good effectiveness under the conditions that parts of the suspension system fail.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114141486","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277185
Kamran Mohajeri, M. Zakizadeh, B. Moaveni, M. Teshnehlab
Cerebellum Model Articulation Controller (CMAC) is known as a feedforward Neural Network (NN) with fast learning and performance. Many improvements have been introduced to it which fuzzy CMAC (FCMAC) is the most important one. Fuzzy CMAC as a neuro fuzzy system increases precision, reduces memory size and makes CMAC differentiable. In addition FCMAC converts CMAC NN as a black box to a white box that its operation is interpretable using fuzzy rules. Fuzzy CMAC has not a unique structure in literature and there are differences in many aspects as membership function, memory layered structure, deffuzification and the fuzzy system applied. Discussing these, this paper reviews fuzzy CMAC different structures in literature.
{"title":"Fuzzy CMAC structures","authors":"Kamran Mohajeri, M. Zakizadeh, B. Moaveni, M. Teshnehlab","doi":"10.1109/FUZZY.2009.5277185","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277185","url":null,"abstract":"Cerebellum Model Articulation Controller (CMAC) is known as a feedforward Neural Network (NN) with fast learning and performance. Many improvements have been introduced to it which fuzzy CMAC (FCMAC) is the most important one. Fuzzy CMAC as a neuro fuzzy system increases precision, reduces memory size and makes CMAC differentiable. In addition FCMAC converts CMAC NN as a black box to a white box that its operation is interpretable using fuzzy rules. Fuzzy CMAC has not a unique structure in literature and there are differences in many aspects as membership function, memory layered structure, deffuzification and the fuzzy system applied. Discussing these, this paper reviews fuzzy CMAC different structures in literature.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114094237","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277356
Tzu-Chao Lin
A novel decision-based fuzzy averaging filter consisting of a new Dempster-Shafer (D-S) noise detector and a two-pass noise filtering mechanism is proposed. Bodies of evidence are extracted, and the basic belief assignment is developed, avoiding the counter-intuitive problem of Dempster's combination rule. The combination belief value can be the decision rule for the D-S noise detector. A fuzzy averaging method where the weights are constructed using a predefined fuzzy set is developed to achieve noise cancellation. Besides that, a simple second-pass filter is also employed to improve the final filtering performance. Experimental results have confirmed the proposed filter outperforms other decision-based filters in terms of both noise suppression and detail preservation.
{"title":"Fuzzy image restoration for noise reduction based on dempster-shafer theory","authors":"Tzu-Chao Lin","doi":"10.1109/FUZZY.2009.5277356","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277356","url":null,"abstract":"A novel decision-based fuzzy averaging filter consisting of a new Dempster-Shafer (D-S) noise detector and a two-pass noise filtering mechanism is proposed. Bodies of evidence are extracted, and the basic belief assignment is developed, avoiding the counter-intuitive problem of Dempster's combination rule. The combination belief value can be the decision rule for the D-S noise detector. A fuzzy averaging method where the weights are constructed using a predefined fuzzy set is developed to achieve noise cancellation. Besides that, a simple second-pass filter is also employed to improve the final filtering performance. Experimental results have confirmed the proposed filter outperforms other decision-based filters in terms of both noise suppression and detail preservation.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114882349","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277251
Katsuhiro Honda, A. Notsu, H. Ichihashi
This paper considers a new approach to user-item clustering for collaborative filtering problems that achieves personalized recommendation. When user-item relations are given by an alternative process, personalized recommendation is performed by finding user-item neighborhoods (co-clusters) from a rectangular relational data matrix, in which users and items have mutually positive relations. In the proposed approach, user-item clusters are extracted one by one in a sequential manner via a structural balancing technique, used in conjunction with the sequential fuzzy cluster extraction method.
{"title":"Collaborative filtering by sequential extraction of user-item clusters based on structural balancing approach","authors":"Katsuhiro Honda, A. Notsu, H. Ichihashi","doi":"10.1109/FUZZY.2009.5277251","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277251","url":null,"abstract":"This paper considers a new approach to user-item clustering for collaborative filtering problems that achieves personalized recommendation. When user-item relations are given by an alternative process, personalized recommendation is performed by finding user-item neighborhoods (co-clusters) from a rectangular relational data matrix, in which users and items have mutually positive relations. In the proposed approach, user-item clusters are extracted one by one in a sequential manner via a structural balancing technique, used in conjunction with the sequential fuzzy cluster extraction method.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117168282","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5276879
Kwang-Baek Kim, Y. Woo, D. Song
Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM (Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM (Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.
{"title":"Improved SIM algorithm for effective image retrieval","authors":"Kwang-Baek Kim, Y. Woo, D. Song","doi":"10.1109/FUZZY.2009.5276879","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5276879","url":null,"abstract":"Contents-based image retrieval methods are in general more objective and effective than text-based image retrieval algorithms since they use color and texture in search and avoid annotating all images for search. SIM (Self-organizing Image browsing Map) is one of contents-based image retrieval algorithms that uses only browsable mapping results obtained by SOM (Self Organizing Map). However, SOM may have an error in selecting the right BMU in learning phase if there are similar nodes with distorted color information due to the intensity of light or objects' movements in the image. Such images may be mapped into other grouping nodes thus the search rate could be decreased by this effect. In this paper, we propose an improved SIM that uses HSV color model in extracting image features with color quantization. In order to avoid unexpected learning error mentioned above, our SOM consists of two layers. In learning phase, SOM layer 1 has the color feature vectors as input. After learning SOM Layer 1, the connection weights of this layer become the input of SOM Layer 2 and re-learning occurs. With this multi-layered SOM learning, we can avoid mapping errors among similar nodes of different color information. In search, we put the query image vector into SOM layer 2 and select nodes of SOM layer 1 that connects with chosen BMU of SOM layer 2. In experiment, we verified that the proposed SIM was better than the original SIM and avoid mapping error effectively.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116097578","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277299
D. Stavrakoudis, Ioannis B. Theocharis
This paper proposes the use of a Boosted Genetic Fuzzy Classifier (BGFC) for land cover classification from multispectral images. The model's learning algorithm is divided into two stages. The first stage iteratively generates fuzzy rules, employing a boosting algorithm that localizes new rules in uncovered subspaces of the feature space. Each rule is obtained through an efficient genetic rule extraction method, which both adapts the parameters of the fuzzy sets in the premise space and determines the required features of the rule, further improving the interpretability of the obtained model. The second stage fine-tunes the obtained rule base through an evolutionary algorithm (EA), improving the cooperation among the fuzzy rules and, thus, increasing the classification performance attained after the first stage. The BGFC is tested using an IKONOS multispectral VHR image, in the agricultural area surrounding a lake-wetland ecosystem in northern Greece. The results indicate that the proposed system is able to handle multi-dimensional feature spaces, effectively exploiting information from different feature sources.
{"title":"A genetic fuzzy rule-based classifier for land cover image classification","authors":"D. Stavrakoudis, Ioannis B. Theocharis","doi":"10.1109/FUZZY.2009.5277299","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277299","url":null,"abstract":"This paper proposes the use of a Boosted Genetic Fuzzy Classifier (BGFC) for land cover classification from multispectral images. The model's learning algorithm is divided into two stages. The first stage iteratively generates fuzzy rules, employing a boosting algorithm that localizes new rules in uncovered subspaces of the feature space. Each rule is obtained through an efficient genetic rule extraction method, which both adapts the parameters of the fuzzy sets in the premise space and determines the required features of the rule, further improving the interpretability of the obtained model. The second stage fine-tunes the obtained rule base through an evolutionary algorithm (EA), improving the cooperation among the fuzzy rules and, thus, increasing the classification performance attained after the first stage. The BGFC is tested using an IKONOS multispectral VHR image, in the agricultural area surrounding a lake-wetland ecosystem in northern Greece. The results indicate that the proposed system is able to handle multi-dimensional feature spaces, effectively exploiting information from different feature sources.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121932483","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277076
M. Štěpnička, B. Jayaram
The compositional rule of inference (CRI) is widely used in approximate reasoning schemes using fuzzy sets. In this work we discuss the suitability of the Bandler-Kohout subproduct for an alternative inference mechanism from the computational point of view.
{"title":"On the computational aspects of the BK-subproduct inference mechanism","authors":"M. Štěpnička, B. Jayaram","doi":"10.1109/FUZZY.2009.5277076","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277076","url":null,"abstract":"The compositional rule of inference (CRI) is widely used in approximate reasoning schemes using fuzzy sets. In this work we discuss the suitability of the Bandler-Kohout subproduct for an alternative inference mechanism from the computational point of view.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129483675","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277184
M. Carpentieri, Alessandro Pappalardo, Domenica Sileo, G. Summa
We review some main theoretical results about genetic algorithms. We shall take into account some central open problems related with the combinatorial optimization and neural networks theory. We exhibit experimental evidence suggesting that several crossover techniques are not, by themselves, eilective in solving hard problems ii compared with traditional combinatorial optimization techniques. Eventually, we propose a hybrid approach based on the idea oí' combining the action oí crossover, rotation operators and short deterministic simulations oí noiidc tor minis tic searches that are promising to be eilective for hard problems (according to the polynomial reduction theory).
{"title":"On hybrid genetic models for hard problems","authors":"M. Carpentieri, Alessandro Pappalardo, Domenica Sileo, G. Summa","doi":"10.1109/FUZZY.2009.5277184","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277184","url":null,"abstract":"We review some main theoretical results about genetic algorithms. We shall take into account some central open problems related with the combinatorial optimization and neural networks theory. We exhibit experimental evidence suggesting that several crossover techniques are not, by themselves, eilective in solving hard problems ii compared with traditional combinatorial optimization techniques. Eventually, we propose a hybrid approach based on the idea oí' combining the action oí crossover, rotation operators and short deterministic simulations oí noiidc tor minis tic searches that are promising to be eilective for hard problems (according to the polynomial reduction theory).","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128395782","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 : 2009-10-02DOI: 10.1109/FUZZY.2009.5277373
Yi Hong, Guopu Zhu, S. Kwong, Qingsheng Ren
To demonstrate the usefulness of low quality individuals for estimation of distribution algorithms, estimation of distribution algorithms using both high quality and low quality individuals are tested on several benchmark problems and their results are compared with those obtained by estimation of distribution algorithms where only high quality individuals are used. The usefulness of low quality individuals for speeding up the search of estimation of distribution algorithms is confirmed by the experimental results.
{"title":"Estimation of distribution algorithms making use of both high quality and low quality individuals","authors":"Yi Hong, Guopu Zhu, S. Kwong, Qingsheng Ren","doi":"10.1109/FUZZY.2009.5277373","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277373","url":null,"abstract":"To demonstrate the usefulness of low quality individuals for estimation of distribution algorithms, estimation of distribution algorithms using both high quality and low quality individuals are tested on several benchmark problems and their results are compared with those obtained by estimation of distribution algorithms where only high quality individuals are used. The usefulness of low quality individuals for speeding up the search of estimation of distribution algorithms is confirmed by the experimental results.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128684335","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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