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":"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":"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.2009.5277184
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":"77 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":"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.2009.5277326
K. Kwong, Max H. Y. Wong, Raymond S. T. Lee, J. Liu, J. You
This paper describes a methodology for financial prediction by using an advanced paradigm from computational intelligence - Chaotic Oscillatory-based Neural Networks (CONN) and aid with fuzzy membership function. The method uses financial market data to predict market trends over a certain period of time. This approach may have a wide variety of applications but from financial forecasting perspective, it can be used to identify and forecast market patterns for providing valuable and useful advices to investors for making investment decisions.
{"title":"Financial trend forecasting with fuzzy chaotic oscillatory-based neural networks (CONN)","authors":"K. Kwong, Max H. Y. Wong, Raymond S. T. Lee, J. Liu, J. You","doi":"10.1109/FUZZY.2009.5277326","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277326","url":null,"abstract":"This paper describes a methodology for financial prediction by using an advanced paradigm from computational intelligence - Chaotic Oscillatory-based Neural Networks (CONN) and aid with fuzzy membership function. The method uses financial market data to predict market trends over a certain period of time. This approach may have a wide variety of applications but from financial forecasting perspective, it can be used to identify and forecast market patterns for providing valuable and useful advices to investors for making investment decisions.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":"1 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":"129000079","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":"1 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":"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.2009.5277329
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":"48 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":"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.2009.5277076
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":"66 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":"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.2009.5277405
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":"1 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":"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.2009.5277373
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":"26 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":"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}
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":"41 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":"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.2009.5277185
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":"23 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":"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}
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