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":"30 1","pages":"0"},"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.2009.5277299
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":"13 1","pages":"0"},"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}
This paper presents an intelligent filter design method with a fixed-point digital signal processor (DSP) and illustrates its performance on the application of active noise cancellation (ANC) system. The proposed designing method uses magnitude and phase compensation techniques to eliminate the errors associated with the nonlinear distortion of analog devices in the application, and hence, to improve the ANC performance. The quantization and rounding errors associated with the fixed-point DSP are also compensated for. Several experiments verify the enhancement.
{"title":"Efficient adaptive filter design to the active noise control system","authors":"I-Ling Chung, Fuh-Hsin Hwang, Cheng-Yuan Chang, Chang-Min Chou","doi":"10.1109/FUZZY.2009.5277339","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277339","url":null,"abstract":"This paper presents an intelligent filter design method with a fixed-point digital signal processor (DSP) and illustrates its performance on the application of active noise cancellation (ANC) system. The proposed designing method uses magnitude and phase compensation techniques to eliminate the errors associated with the nonlinear distortion of analog devices in the application, and hence, to improve the ANC performance. The quantization and rounding errors associated with the fixed-point DSP are also compensated for. Several experiments verify the enhancement.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133112619","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.2009.5277333
Tomohiro Matsui, Katsuhiro Honda, Chi-Hyon Oh, A. Notsu, H. Ichihashi
PCA-guided k-Means is a technique for analytically estimating a relaxed solution for k-Means clustering, while the derived cluster indicator is a rotated solution and the rotation matrix cannot be explicitly estimated. Then, an approach such as visualization by ordering of samples in connectivity matrices is applied for visually accessing cluster structures. This paper introduces a technique for estimating a rotation matrix by Procrustean transformation of principal component scores in order to select the optimal solution from multiple solutions derived by k-Means, and proposes a cluster validation measure calculating the deviation between k-Means solutions and a re-constructed membership indicator matrix.
{"title":"Cluster validation in k-Means clustering based on PCA-guided k-Means and procrustean transformation of PC scores","authors":"Tomohiro Matsui, Katsuhiro Honda, Chi-Hyon Oh, A. Notsu, H. Ichihashi","doi":"10.1109/FUZZY.2009.5277333","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277333","url":null,"abstract":"PCA-guided k-Means is a technique for analytically estimating a relaxed solution for k-Means clustering, while the derived cluster indicator is a rotated solution and the rotation matrix cannot be explicitly estimated. Then, an approach such as visualization by ordering of samples in connectivity matrices is applied for visually accessing cluster structures. This paper introduces a technique for estimating a rotation matrix by Procrustean transformation of principal component scores in order to select the optimal solution from multiple solutions derived by k-Means, and proposes a cluster validation measure calculating the deviation between k-Means solutions and a re-constructed membership indicator matrix.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127979609","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.2009.5277261
Kanitsorn Suriyapaiboonwattana, C. Pornavalai, G. Chakraborty
Vehicular Ad-hoc Network (VANET) is gaining much attention recently because of its many important applications in transportation, to improve road safety, reduce traffic congestion, to enable efficient traffic management etc. However, there are several technical issues to be addressed for its effective deployment. Stability in communication in VANET is difficult to achieve due to rapid network changes. Restoration is inefficient while using traditional protocols based on broadcast storm. In this paper, we propose a new adaptive protocol to improve performance for on road safety alert application in VANET. It can alleviate the broadcast storm problem using adaptive wait-windows and adaptive probability to transmit. Simulation shows that our proposed approach has better performances in terms of number of collision, success rate, and delay, when compared with other existing protocols.
{"title":"An adaptive alert message dissemination protocol for VANET to improve road safety","authors":"Kanitsorn Suriyapaiboonwattana, C. Pornavalai, G. Chakraborty","doi":"10.1109/FUZZY.2009.5277261","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277261","url":null,"abstract":"Vehicular Ad-hoc Network (VANET) is gaining much attention recently because of its many important applications in transportation, to improve road safety, reduce traffic congestion, to enable efficient traffic management etc. However, there are several technical issues to be addressed for its effective deployment. Stability in communication in VANET is difficult to achieve due to rapid network changes. Restoration is inefficient while using traditional protocols based on broadcast storm. In this paper, we propose a new adaptive protocol to improve performance for on road safety alert application in VANET. It can alleviate the broadcast storm problem using adaptive wait-windows and adaptive probability to transmit. Simulation shows that our proposed approach has better performances in terms of number of collision, success rate, and delay, when compared with other existing protocols.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116141190","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.2009.5277158
Sungyoung Jung, Jungmin Kim, Sungshin Kim
In the multi-path planning, every autonomous vehicle normally receives her path from the server and sends her position to the server. If server estimates collision between two vehicles, then the path should be re-planned by an algorithm in the server. Path could be compensated by fuzzy expert systems (FESs) that is designed using heuristic method for collision avoidance in multi-path planning. The server calculates the assistance via point and send to each vehicle. The algorithm was evaluated it's stability by simulation test, and then experimented by real autonomous vehicle. The experimental result proved this collision avoidance algorithm is good for multi-path planning.
{"title":"Collision avoidance algorithm based on fuzzy expert systems for multi-path planning","authors":"Sungyoung Jung, Jungmin Kim, Sungshin Kim","doi":"10.1109/FUZZY.2009.5277158","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277158","url":null,"abstract":"In the multi-path planning, every autonomous vehicle normally receives her path from the server and sends her position to the server. If server estimates collision between two vehicles, then the path should be re-planned by an algorithm in the server. Path could be compensated by fuzzy expert systems (FESs) that is designed using heuristic method for collision avoidance in multi-path planning. The server calculates the assistance via point and send to each vehicle. The algorithm was evaluated it's stability by simulation test, and then experimented by real autonomous vehicle. The experimental result proved this collision avoidance algorithm is good for multi-path planning.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"279 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116192420","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.2009.5277052
Debasish Datta, A. Konar, A. Chowdhury, Swagatam Das, A. Nagar
In fuzzy abduction, one needs to evaluate the membership distribution of the premise (antecedent clause), when the membership distribution of the consequent clause, and the fuzzy implication relations between the antecedent and the consequent clauses are provided. The paper formulates and solves the problem of fuzzy abduction by using type-2 fuzzy sets. It presumes background knowledge about the primary and the secondary antecedent to consequent implication relations to uniquely determine the type-2 fuzzy set corresponding to the antecedent clause, when the same for the consequent clause is provided. The proposed methodology of abduction would serve many interesting applications on predictions, forecasting, and diagnosis, where the environmental factor can be modeled with type-2 secondary distributions.
{"title":"Abductive reasoning with type 2 fuzzy sets","authors":"Debasish Datta, A. Konar, A. Chowdhury, Swagatam Das, A. Nagar","doi":"10.1109/FUZZY.2009.5277052","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277052","url":null,"abstract":"In fuzzy abduction, one needs to evaluate the membership distribution of the premise (antecedent clause), when the membership distribution of the consequent clause, and the fuzzy implication relations between the antecedent and the consequent clauses are provided. The paper formulates and solves the problem of fuzzy abduction by using type-2 fuzzy sets. It presumes background knowledge about the primary and the secondary antecedent to consequent implication relations to uniquely determine the type-2 fuzzy set corresponding to the antecedent clause, when the same for the consequent clause is provided. The proposed methodology of abduction would serve many interesting applications on predictions, forecasting, and diagnosis, where the environmental factor can be modeled with type-2 secondary distributions.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128031564","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.2009.5277273
J. Banda, R. Angryk
This paper presents experimental results on the utilization of fuzzy clustering as a discretization technique for purpose of solar images recognition. By extracting texture features from our solar images, and consequently applying fuzzy clustering techniques on these features, we were able to determine what clustering algorithm and what algorithm's initialization parameters produced the best data discretization. Based on these results we discretized some of our texture features and ran them on two different classifiers comparing how well the classifiers performed on our original data versus the discretized data. Our experimental results demonstrate that discretization of our data via fuzzy clustering carries significant potential since on our classifiers produced similar results on the original and the discretized data, and the reduction of storage space achieved through cluster-based discretization has been very significant.
{"title":"On the effectiveness of fuzzy clustering as a data discretization technique for large-scale classification of solar images","authors":"J. Banda, R. Angryk","doi":"10.1109/FUZZY.2009.5277273","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277273","url":null,"abstract":"This paper presents experimental results on the utilization of fuzzy clustering as a discretization technique for purpose of solar images recognition. By extracting texture features from our solar images, and consequently applying fuzzy clustering techniques on these features, we were able to determine what clustering algorithm and what algorithm's initialization parameters produced the best data discretization. Based on these results we discretized some of our texture features and ran them on two different classifiers comparing how well the classifiers performed on our original data versus the discretized data. Our experimental results demonstrate that discretization of our data via fuzzy clustering carries significant potential since on our classifiers produced similar results on the original and the discretized data, and the reduction of storage space achieved through cluster-based discretization has been very significant.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128176959","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.2009.5277265
T. Hiroyasu, Hisatake Yokouchi, Misato Tanaka, M. Miki
Interactive Genetic Algorithm (iGA) is one of evolutionary computations in which the design candidates are evaluated by human. Using iGA, the sensibility and subjective feelings of humans can be optimized by learning the user's evaluation of presented individuals. In this research, iGA was applied to product recommendation on shopping sites. One of the most difficult points to be addressed in construction of a product recommendation system is to taking a long time to extract and assign values to design variables from all of the actual products on the site. It is also difficult to define product design variables appropriately. To address these problems, we propose a method to generate design variables automatically based on a lot of users' preference data on the Web. We constructed the design variables using the relevance of products obtained by Collaborative Filtering and discussed them. Through the simulation experiments, the effectiveness of the proposed method is discussed.
{"title":"Extraction of design variables using collaborative filtering for interactive genetic algorithms","authors":"T. Hiroyasu, Hisatake Yokouchi, Misato Tanaka, M. Miki","doi":"10.1109/FUZZY.2009.5277265","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277265","url":null,"abstract":"Interactive Genetic Algorithm (iGA) is one of evolutionary computations in which the design candidates are evaluated by human. Using iGA, the sensibility and subjective feelings of humans can be optimized by learning the user's evaluation of presented individuals. In this research, iGA was applied to product recommendation on shopping sites. One of the most difficult points to be addressed in construction of a product recommendation system is to taking a long time to extract and assign values to design variables from all of the actual products on the site. It is also difficult to define product design variables appropriately. To address these problems, we propose a method to generate design variables automatically based on a lot of users' preference data on the Web. We constructed the design variables using the relevance of products obtained by Collaborative Filtering and discussed them. Through the simulation experiments, the effectiveness of the proposed method is discussed.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"29 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125798202","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.2009.5277199
Lily Lin, Huey-Ming Lee
An evaluation of survey by fuzzy linguistics has been conducted using both the signed distance and centroid method. As to both methods, the proposed approaches, different from conventional survey algorithms via questionnaire rating item by linguistic variables, possessing the vague nature, we employed fuzzy sense of sampling to express the degree of interviewee's feelings based on his own concept, the result will be closer to interviewee's real thought. In this study, we re-model the previous method and use the signed distance method which would be effective and reliable to do aggregated assessment analysis.
{"title":"An evaluation of survey by fuzzy linguistics based on the signed distance method","authors":"Lily Lin, Huey-Ming Lee","doi":"10.1109/FUZZY.2009.5277199","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277199","url":null,"abstract":"An evaluation of survey by fuzzy linguistics has been conducted using both the signed distance and centroid method. As to both methods, the proposed approaches, different from conventional survey algorithms via questionnaire rating item by linguistic variables, possessing the vague nature, we employed fuzzy sense of sampling to express the degree of interviewee's feelings based on his own concept, the result will be closer to interviewee's real thought. In this study, we re-model the previous method and use the signed distance method which would be effective and reliable to do aggregated assessment analysis.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127260682","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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