Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277075
Yih-Guang Leu, Chun-Yao Chen, Chin-Ming Hong
In this paper, a nonlinear parameter fuzzy control scheme is proposed for a class of uncertain systems without all states measurement. In the control scheme, a fuzzy identifier without prior knowledge on membership functions is merged into direct adaptive control by means of a linear state estimator. Since the structure of the fuzzy identifier is nonlinear in the adjusted parameters, the fuzzy identifier uses a mean method to develop adaptive laws. Finally, an example is provided to demonstrate the effectiveness of the proposed control scheme.
{"title":"Nonlinear parameter fuzzy control for uncertain systemswith only system output measurement","authors":"Yih-Guang Leu, Chun-Yao Chen, Chin-Ming Hong","doi":"10.1109/FUZZY.2009.5277075","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277075","url":null,"abstract":"In this paper, a nonlinear parameter fuzzy control scheme is proposed for a class of uncertain systems without all states measurement. In the control scheme, a fuzzy identifier without prior knowledge on membership functions is merged into direct adaptive control by means of a linear state estimator. Since the structure of the fuzzy identifier is nonlinear in the adjusted parameters, the fuzzy identifier uses a mean method to develop adaptive laws. Finally, an example is provided to demonstrate the effectiveness of the proposed control scheme.","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":"128822025","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.2009.5277182
Katsuhiro Honda, Tomonari Nomaguchi, A. Notsu, H. Ichihashi
Cluster validation is an important issue in cluster analysis. In this paper, a comparative study on validity criteria is performed with linear fuzzy clustering that can be identified with a local PCA technique. Besides the standard fuzzification approach, the entropy regularization approach is responsible for fuzzification of data partition and the approach implies a close relation between FCM-type linear fuzzy clustering and probabilistic PCA models. This comparative study reveals mutual differences between two fuzzification approaches from the view point of cluster validation using several cluster validity criteria. Additional characteristics are shown in a pareto analysis, in which the effect of noise sensitivity is also discussed.
{"title":"A comparative study on cluster validity criteria in linear fuzzy clustering and pareto optimality analysis","authors":"Katsuhiro Honda, Tomonari Nomaguchi, A. Notsu, H. Ichihashi","doi":"10.1109/FUZZY.2009.5277182","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277182","url":null,"abstract":"Cluster validation is an important issue in cluster analysis. In this paper, a comparative study on validity criteria is performed with linear fuzzy clustering that can be identified with a local PCA technique. Besides the standard fuzzification approach, the entropy regularization approach is responsible for fuzzification of data partition and the approach implies a close relation between FCM-type linear fuzzy clustering and probabilistic PCA models. This comparative study reveals mutual differences between two fuzzification approaches from the view point of cluster validation using several cluster validity criteria. Additional characteristics are shown in a pareto analysis, in which the effect of noise sensitivity is also discussed.","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":"122049210","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.2009.5277401
R. Pallikonda, Praveen Abbaraju, Vikas Chandra Chinthala, Rashmi Reddy Pabhati Reddy, Karthik Ravi Teja Machiraju
Electrical Energy is a vital feature for any developing nation. To meet the growing demand, power generating plants of all types are being installed; even then the gap between the supply and demand is continuously increasing due to the depletion of natural resources. Hence, the way to over come the problem is optimal utilization of available energy sources. In this paper, a methodology is shown to solve to design a model for load management during peak hours in case of domestic loads in both peak hours and off peak hours aiming to reduce the gap between the demand and the supply of electrical energy. Such that consumers and supplier both get beneficial at the same time. The paper also presents the application of fuzzy logic and DSM techniques to the domestic loads, where in the power consumption can be limited during the peak hours there by achieving power conservation. The current method developed is the extension and the part of the Demand Side Management. Simulation results are presented to show effectiveness of the proposed fuzzy logic and Demand Side Management strategy for load management.
{"title":"An approach of DSM techniques for domestic load management using fuzzy logic","authors":"R. Pallikonda, Praveen Abbaraju, Vikas Chandra Chinthala, Rashmi Reddy Pabhati Reddy, Karthik Ravi Teja Machiraju","doi":"10.1109/FUZZY.2009.5277401","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277401","url":null,"abstract":"Electrical Energy is a vital feature for any developing nation. To meet the growing demand, power generating plants of all types are being installed; even then the gap between the supply and demand is continuously increasing due to the depletion of natural resources. Hence, the way to over come the problem is optimal utilization of available energy sources. In this paper, a methodology is shown to solve to design a model for load management during peak hours in case of domestic loads in both peak hours and off peak hours aiming to reduce the gap between the demand and the supply of electrical energy. Such that consumers and supplier both get beneficial at the same time. The paper also presents the application of fuzzy logic and DSM techniques to the domestic loads, where in the power consumption can be limited during the peak hours there by achieving power conservation. The current method developed is the extension and the part of the Demand Side Management. Simulation results are presented to show effectiveness of the proposed fuzzy logic and Demand Side Management strategy for load management.","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":"122602876","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.2009.5277057
Hong-Gi Lee, J. Hong, Hoon Kang, K. Sim
Even though the genetic algorithm is known to be a very effective method to solve the global minimization problem, it needs much time (a large population size and a large number of generations) for a reliable answer and thus it seems to be inadequate for on-line performance. We propose a population feedback GA scheme. we show the effectiveness of our scheme by finding an observer for the discrete-time nonlinear autonomous systems with simulations.
{"title":"A genetic algorithms for on-line calculation with application to system theory","authors":"Hong-Gi Lee, J. Hong, Hoon Kang, K. Sim","doi":"10.1109/FUZZY.2009.5277057","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277057","url":null,"abstract":"Even though the genetic algorithm is known to be a very effective method to solve the global minimization problem, it needs much time (a large population size and a large number of generations) for a reliable answer and thus it seems to be inadequate for on-line performance. We propose a population feedback GA scheme. we show the effectiveness of our scheme by finding an observer for the discrete-time nonlinear autonomous systems with simulations.","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":"124241680","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.2009.5277077
Xiao-Ying Wang, J. Garibaldi, Shang-Ming Zhou, R. John
Recommending appropriate follow-up treatment options to patients after diagnosis and primary (usually surgical) treatment of breast cancer is a complex decision making problem. Often, the decision is reached by consensus from a multi-disciplinary team of oncologists, radiologists, surgeons and pathologists. Non-stationary fuzzy sets have been proposed as a mechanism to represent and reason with the knowledge of such multiple experts. In this paper, we briefly describe the creation of a non-stationary fuzzy inference system to provide decision support in this context, and examine a number of alternative methods for interpreting the output of such a non-stationary inference system. The alternative interpretation methodologies and the experiments carried out to compare these methods are detailed. Results are presented which shown that using majority voting ensemble decision making from a non-stationary fuzzy system improves accuracy of the decision making. We conclude that non-stationary systems coupled with ensemble interpretation methods are worthy of further exploration.
{"title":"Methods of interpretation of a non-stationary fuzzy system for the treatment of breast cancer","authors":"Xiao-Ying Wang, J. Garibaldi, Shang-Ming Zhou, R. John","doi":"10.1109/FUZZY.2009.5277077","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277077","url":null,"abstract":"Recommending appropriate follow-up treatment options to patients after diagnosis and primary (usually surgical) treatment of breast cancer is a complex decision making problem. Often, the decision is reached by consensus from a multi-disciplinary team of oncologists, radiologists, surgeons and pathologists. Non-stationary fuzzy sets have been proposed as a mechanism to represent and reason with the knowledge of such multiple experts. In this paper, we briefly describe the creation of a non-stationary fuzzy inference system to provide decision support in this context, and examine a number of alternative methods for interpreting the output of such a non-stationary inference system. The alternative interpretation methodologies and the experiments carried out to compare these methods are detailed. Results are presented which shown that using majority voting ensemble decision making from a non-stationary fuzzy system improves accuracy of the decision making. We conclude that non-stationary systems coupled with ensemble interpretation methods are worthy of further exploration.","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":"133586612","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.2009.5277329
M. Helmi, S. Almodarresi
This paper presents a fuzzy inference system (FIS) for recognizing human activities using a triaxial accelerometer. The accelerometer is used to collect human motion acceleration data for classifying four different activities: moving forward, jumping, going upstairs, and going downstairs. Three different features including peak to peak amplitude, standard deviation, and correlation between axes are extracted from each axis of the accelerometer as inputs to the fuzzy system. The fuzzy rules and the membership functions of this fuzzy system are defined based on the experimental values of these features. The experiments show that the proposed fuzzy inference system recognizes moving forward, jumping, going upstairs, and going downstairs with accuracy of 100%, 96.7%, 93.3%, and 93.3%, respectively.
{"title":"Human activity recognition using a fuzzy inference system","authors":"M. Helmi, S. Almodarresi","doi":"10.1109/FUZZY.2009.5277329","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277329","url":null,"abstract":"This paper presents a fuzzy inference system (FIS) for recognizing human activities using a triaxial accelerometer. The accelerometer is used to collect human motion acceleration data for classifying four different activities: moving forward, jumping, going upstairs, and going downstairs. Three different features including peak to peak amplitude, standard deviation, and correlation between axes are extracted from each axis of the accelerometer as inputs to the fuzzy system. The fuzzy rules and the membership functions of this fuzzy system are defined based on the experimental values of these features. The experiments show that the proposed fuzzy inference system recognizes moving forward, jumping, going upstairs, and going downstairs with accuracy of 100%, 96.7%, 93.3%, and 93.3%, respectively.","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":"130417972","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.2009.5277221
D. Guan, Yongkoo Han, Young-Koo Lee, Sungyoung Lee, Chongkug Park
For a learning task with a huge number of training instances, we sample some informative/important instances, which are then used for learning. Obtaining accurately labeling data is always difficult thus noise detection is required to filter out noises from sampled instances since the noises will degrade the learning performance. In this work, we propose to utilize unsampled instances to improve the performance of noise detection in sampled instances. Empirical study validates our idea that refined classifier can be achieved from noisy sampled instances by utilizing unsampled instances.
{"title":"Refining classifier from unsampled data","authors":"D. Guan, Yongkoo Han, Young-Koo Lee, Sungyoung Lee, Chongkug Park","doi":"10.1109/FUZZY.2009.5277221","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277221","url":null,"abstract":"For a learning task with a huge number of training instances, we sample some informative/important instances, which are then used for learning. Obtaining accurately labeling data is always difficult thus noise detection is required to filter out noises from sampled instances since the noises will degrade the learning performance. In this work, we propose to utilize unsampled instances to improve the performance of noise detection in sampled instances. Empirical study validates our idea that refined classifier can be achieved from noisy sampled instances by utilizing unsampled instances.","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":"130437101","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.2009.5277405
Shinn-Horng Chen, Wen-Hsien Ho, J. Chou
The robust completely controllability problem for the Takagi-Sugeno (TS) fuzzy descriptor systems is studied in this paper. The proposed sufficient condition can provide the explicit relationship of the bounds on parameter uncertainties for preserving the assumed properties.
{"title":"Robust controllability of TS fuzzy descriptor systems with structured parametric uncertainties","authors":"Shinn-Horng Chen, Wen-Hsien Ho, J. Chou","doi":"10.1109/FUZZY.2009.5277405","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277405","url":null,"abstract":"The robust completely controllability problem for the Takagi-Sugeno (TS) fuzzy descriptor systems is studied in this paper. The proposed sufficient condition can provide the explicit relationship of the bounds on parameter uncertainties for preserving the assumed properties.","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":"131203695","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.2009.5277360
Balaji Parasumanna Gokulan, D. Srinivasan
Rapid advances made in vehicle technology and increased level of urbanization have caused an exponential increase in road traffic congestion levels. This has necessitated the implementation of intelligent traffic responsive signal controllers capable of maintaining the saturation levels in each link thereby reducing congestion and increasing utilization of existing infrastructure. This paper presents one such distributed multi-agent architecture based on weighted type-2 fuzzy inference engine for the urban traffic signal control. Agents have been programmed in PARAMICS microscopic traffic simulator and tested on a simulated section of Central Business District in Singapore with twenty five interconnected intersections. A comparative analysis of the proposed architecture with the existing traffic signal controller HMS - Hierarchical multi-agent system, was performed for two different traffic scenarios. The results clearly indicates better performance of the proposed agent architecture over the benchmark controller and offers scope for improvement in the future.
{"title":"Distributed multi-agent type-2 fuzzy architecture for urban traffic signal control","authors":"Balaji Parasumanna Gokulan, D. Srinivasan","doi":"10.1109/FUZZY.2009.5277360","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277360","url":null,"abstract":"Rapid advances made in vehicle technology and increased level of urbanization have caused an exponential increase in road traffic congestion levels. This has necessitated the implementation of intelligent traffic responsive signal controllers capable of maintaining the saturation levels in each link thereby reducing congestion and increasing utilization of existing infrastructure. This paper presents one such distributed multi-agent architecture based on weighted type-2 fuzzy inference engine for the urban traffic signal control. Agents have been programmed in PARAMICS microscopic traffic simulator and tested on a simulated section of Central Business District in Singapore with twenty five interconnected intersections. A comparative analysis of the proposed architecture with the existing traffic signal controller HMS - Hierarchical multi-agent system, was performed for two different traffic scenarios. The results clearly indicates better performance of the proposed agent architecture over the benchmark controller and offers scope for improvement in the future.","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":"134127343","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.2009.5277088
M. Umano, M. Okamura, Kazuhisa Seta
We have various kinds of time series such as stock prices. We understand them via their linguistic expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a linguistic expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of Multimodal Summarization for Trend Information (MuST).
{"title":"Improved method for linguistic expression of time series with global trend and local features","authors":"M. Umano, M. Okamura, Kazuhisa Seta","doi":"10.1109/FUZZY.2009.5277088","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277088","url":null,"abstract":"We have various kinds of time series such as stock prices. We understand them via their linguistic expressions in a natural language rather than conventional stochastic models. We propose an improved method to have a linguistic expression with a global trend and local features of time series. A global trend is extracted via aggregated values on the fuzzy intervals in the temporal axis and local features are specified as the positions of locally large differences between the original data and the data representing the global trend. We apply the method to the data of Multimodal Summarization for Trend Information (MuST).","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":"134552345","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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