Pub Date : 2006-11-30DOI: 10.1109/ISEFS.2006.251131
N. Kishor, S. Singh, A. S. Raghuvanshi, P. Sharma
In this paper, the hydro power plant dynamics is identified using fuzzy models. The plant data is generated from Pade and H-infinity approximated first, second, third and fourth-order rational transfer function models. The models are simulated as (i) gate-servo motor position and turbine speed with random load disturbance and (ii) gate position and developed turbine power. Takagi-Sugeno fuzzy model structures are identified with smooth stepped wave signal input and the identified model is generalized on its validation data set and with random stepped wave signal as input. The fuzzy rules are extracted from data by means of Gustafson-Kessel clustering with antecedents determined using product-space and point-wise projection techniques
{"title":"Fuzzy Models for the Study of Hydro Power Plant Dynamics","authors":"N. Kishor, S. Singh, A. S. Raghuvanshi, P. Sharma","doi":"10.1109/ISEFS.2006.251131","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251131","url":null,"abstract":"In this paper, the hydro power plant dynamics is identified using fuzzy models. The plant data is generated from Pade and H-infinity approximated first, second, third and fourth-order rational transfer function models. The models are simulated as (i) gate-servo motor position and turbine speed with random load disturbance and (ii) gate position and developed turbine power. Takagi-Sugeno fuzzy model structures are identified with smooth stepped wave signal input and the identified model is generalized on its validation data set and with random stepped wave signal as input. The fuzzy rules are extracted from data by means of Gustafson-Kessel clustering with antecedents determined using product-space and point-wise projection techniques","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132719245","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251139
A. Banakar, M. Azeem
The advantages of wavelets when used in neural networks and fuzzy are well known. The new notion is to combine wavelet networks and neuro-fuzzy models. In this paper two models namely summation wavelet neural network (SWNN) and multiplication wavelet neural network (MWNN) are proposed. These two generalized wavelet neural network (WNN) models are used in neuro-fuzzy model are tested by using time series prediction
{"title":"Generalized Wavelet Neuro-Fuzzy Model and its Application in Time Series Forecasting","authors":"A. Banakar, M. Azeem","doi":"10.1109/ISEFS.2006.251139","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251139","url":null,"abstract":"The advantages of wavelets when used in neural networks and fuzzy are well known. The new notion is to combine wavelet networks and neuro-fuzzy models. In this paper two models namely summation wavelet neural network (SWNN) and multiplication wavelet neural network (MWNN) are proposed. These two generalized wavelet neural network (WNN) models are used in neuro-fuzzy model are tested by using time series prediction","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133494766","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251140
M. Tabakov
The main objective of medical image segmentation is to extract and characterise anatomical structures with respect to some input features or expert knowledge. This paper describes a way of medical image segmentation using an appropriately defined fuzzy clustering method based on a fuzzy similarity relation. The considered relation is defined in terms of the Euclidean metric. A fuzzy similarity relation-based image segmentation algorithm is also introduced. To illustrate the obtained segmentation process some examples of computed tomography imaging are considered. Some results, using the classical fuzzy c-means clustering algorithm are also presented, for a comparison purpose
{"title":"A Fuzzy Clustering Technique for Medical Image Segmentation","authors":"M. Tabakov","doi":"10.1109/ISEFS.2006.251140","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251140","url":null,"abstract":"The main objective of medical image segmentation is to extract and characterise anatomical structures with respect to some input features or expert knowledge. This paper describes a way of medical image segmentation using an appropriately defined fuzzy clustering method based on a fuzzy similarity relation. The considered relation is defined in terms of the Euclidean metric. A fuzzy similarity relation-based image segmentation algorithm is also introduced. To illustrate the obtained segmentation process some examples of computed tomography imaging are considered. Some results, using the classical fuzzy c-means clustering algorithm are also presented, for a comparison purpose","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128993068","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251169
M. Asif, P. Angelov, H. Ahmed
Object tracking is of great interest in different areas of industry, security and defense. Tracking moving objects based on color information is more robust than systems utilizing motion cues. In order to maintain the lock on the object as the surrounding conditions vary, the color model needs to be adapted in real-time. In this paper an on-line learning method for the color model is implemented using fuzzy adaptive resonance theory (ART). Fuzzy ART is a type of neural network that is trained based on competitive learning principle. The color model of the target region is regularly updated based on the vigilance criteria (which is a threshold) applied to the pixel color information. The target location in the next frame is predicted using evolving extended Takagi-Sugeno (exTS) model to improve the tracking performance. The results of applying exTS for prediction of the position of the moving target were compared with the usually used solution based on Kalman filter. The experiments with real footage demonstrate over a variety of scenarios the superiority of the exTS as a predictor comparing to the Kalman filter. Further investigation concentrates on using evolving clustering for realizing computationally efficient simultaneous tracking of different segments in the object
{"title":"An Approach to Real-time Color-based Object Tracking","authors":"M. Asif, P. Angelov, H. Ahmed","doi":"10.1109/ISEFS.2006.251169","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251169","url":null,"abstract":"Object tracking is of great interest in different areas of industry, security and defense. Tracking moving objects based on color information is more robust than systems utilizing motion cues. In order to maintain the lock on the object as the surrounding conditions vary, the color model needs to be adapted in real-time. In this paper an on-line learning method for the color model is implemented using fuzzy adaptive resonance theory (ART). Fuzzy ART is a type of neural network that is trained based on competitive learning principle. The color model of the target region is regularly updated based on the vigilance criteria (which is a threshold) applied to the pixel color information. The target location in the next frame is predicted using evolving extended Takagi-Sugeno (exTS) model to improve the tracking performance. The results of applying exTS for prediction of the position of the moving target were compared with the usually used solution based on Kalman filter. The experiments with real footage demonstrate over a variety of scenarios the superiority of the exTS as a predictor comparing to the Kalman filter. Further investigation concentrates on using evolving clustering for realizing computationally efficient simultaneous tracking of different segments in the object","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127997325","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251176
D. Ichiba, K. Hara, H. Kanoh
We propose a method to interpolate traffic data of roads using genetic fuzzy systems (GFSs). In Japan, car navigation equipment provides drivers with real-time traffic information about principal roads. The information enables giving route guidance. In a previous study, the problem of the method lies in the following two facts because a human designs membership functions of fuzzy c-means (FCM) experientially. One fact is that the design cost is high; the other is that tuning membership functions optimally is difficult. We automatically tune membership functions using a genetic algorithm (GA). The membership functions are encoded as a chromosome of GA, and the average of mean daily errors calculated from actual traffic data is used as a fitness function. Experiments using actual traffic data and an actual road map indicate that our method is more effective than the conventional method
{"title":"Spatial Interpolation of Traffic Data by Genetic Fuzzy System","authors":"D. Ichiba, K. Hara, H. Kanoh","doi":"10.1109/ISEFS.2006.251176","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251176","url":null,"abstract":"We propose a method to interpolate traffic data of roads using genetic fuzzy systems (GFSs). In Japan, car navigation equipment provides drivers with real-time traffic information about principal roads. The information enables giving route guidance. In a previous study, the problem of the method lies in the following two facts because a human designs membership functions of fuzzy c-means (FCM) experientially. One fact is that the design cost is high; the other is that tuning membership functions optimally is difficult. We automatically tune membership functions using a genetic algorithm (GA). The membership functions are encoded as a chromosome of GA, and the average of mean daily errors calculated from actual traffic data is used as a fitness function. Experiments using actual traffic data and an actual road map indicate that our method is more effective than the conventional method","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114604699","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251160
G. Magoulas, D. Dimakopoulos
The paper discusses user attributes and their representation in user profiles for user-adaptive systems. It introduces an approach for representing multiple attributes in the user profile, and a technique that combines fuzzy programming and fuzzy relation networks to prioritize the impact of user attributes/requirements in personalizing the application through generating appropriate adaptation actions. The paper also presents a generic adaptation scenario using the proposed approach
{"title":"An Adaptive Fuzzy Model for Personalization with Evolvable User Profiles","authors":"G. Magoulas, D. Dimakopoulos","doi":"10.1109/ISEFS.2006.251160","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251160","url":null,"abstract":"The paper discusses user attributes and their representation in user profiles for user-adaptive systems. It introduces an approach for representing multiple attributes in the user profile, and a technique that combines fuzzy programming and fuzzy relation networks to prioritize the impact of user attributes/requirements in personalizing the application through generating appropriate adaptation actions. The paper also presents a generic adaptation scenario using the proposed approach","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125368452","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251173
E. Lughofer
In this paper several improvements towards a safer processing of incremental learning techniques for fuzzy models are demonstrated. The first group of improvements include stability issues for making the evolving scheme more robust against faults, steady state situations and extrapolation occurrence. In the case of steady states or constant system behaviors a concept of overcoming the so-called 'unlearning' effect is proposed by which the forgetting of previously learned relationships can be prevented. A discussion on the convergence of the incremental learning scheme to the optimum in the least squares sense is included as well. The concepts regarding fault omittance are demonstrated, as usually faults in the training data lead to problems in learning underlying dependencies. An improvement of extrapolation behavior in the case of fuzzy models when using fuzzy sets with infinite support is also highlighted. The second group of improvements deals with interpretability and quality aspects of the models obtained during the evolving process. An online strategy for obtaining better interpretable models is presented. This strategy is feasible for online monitoring tasks, as it can be applied after each incremental learning step, that is without using prior data. Interpretability is important, whenever the model itself or the model decisions should be linguistically understandable. The quality aspects include an online calculation of local error bars for Takagi-Sugeno fuzzy models, which can be seen as a kind of confidence intervals. In this sense, the error bars can be exploited in order to give feedback to the operator, regarding fuzzy model reliability and prediction quality. Evaluation results based on experimental results are included, showing clearly the impact on the improvement of robustness of the learning procedure
{"title":"Process Safety Enhancements for Data-Driven Evolving Fuzzy Models","authors":"E. Lughofer","doi":"10.1109/ISEFS.2006.251173","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251173","url":null,"abstract":"In this paper several improvements towards a safer processing of incremental learning techniques for fuzzy models are demonstrated. The first group of improvements include stability issues for making the evolving scheme more robust against faults, steady state situations and extrapolation occurrence. In the case of steady states or constant system behaviors a concept of overcoming the so-called 'unlearning' effect is proposed by which the forgetting of previously learned relationships can be prevented. A discussion on the convergence of the incremental learning scheme to the optimum in the least squares sense is included as well. The concepts regarding fault omittance are demonstrated, as usually faults in the training data lead to problems in learning underlying dependencies. An improvement of extrapolation behavior in the case of fuzzy models when using fuzzy sets with infinite support is also highlighted. The second group of improvements deals with interpretability and quality aspects of the models obtained during the evolving process. An online strategy for obtaining better interpretable models is presented. This strategy is feasible for online monitoring tasks, as it can be applied after each incremental learning step, that is without using prior data. Interpretability is important, whenever the model itself or the model decisions should be linguistically understandable. The quality aspects include an online calculation of local error bars for Takagi-Sugeno fuzzy models, which can be seen as a kind of confidence intervals. In this sense, the error bars can be exploited in order to give feedback to the operator, regarding fuzzy model reliability and prediction quality. Evaluation results based on experimental results are included, showing clearly the impact on the improvement of robustness of the learning procedure","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123331522","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251185
N. Kasabov, Dimitar Filev
The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of Evolving Intelligent Systems are summarized in this short communication. Intelligent systems can be defined as systems that incorporate some form of reasoning that is typical for humans. Fuzzy Systems are well known for being able to formalize human knowledge that still separates humans from machines. Artificial Neural Networks have proven to be a useful form of parallel processing of information that employs principles from the organization of the brain. Finally, the evolution is a phenomenon that was initially used to solve optimization problems inspired by the progress in Genetic Algorithms, Evolutionary Computing, and Genetic Programming. These types of evolutionary algorithms are mimicking the natural selection that takes place in populations of living creatures over generations. More recently, the evolution of individual systems within their life-span (self-organization, learning through experience, and self-developing) has attracted attention. These systems called `evolving' came as a result of the research on practical intelligent systems and on-line learning algorithms that are capable of extracting knowledge from data and performing a higher level adaptation of model structure as well as model parameters. Evolving systems can also be considered an extension of the multi-model concept known from the control theory, and of the on-line identification of fuzzy rule-based models. They can also be regarded as an extension of the methods for on-line learning neural networks with flexible structure that can grow and shrink.
{"title":"Evolving Intelligent Systems: Methods, Learning, & Applications","authors":"N. Kasabov, Dimitar Filev","doi":"10.1109/ISEFS.2006.251185","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251185","url":null,"abstract":"The basic concept, formulation, background, and a panoramic view over the recent research results and open problems in the newly emerging area of Evolving Intelligent Systems are summarized in this short communication. Intelligent systems can be defined as systems that incorporate some form of reasoning that is typical for humans. Fuzzy Systems are well known for being able to formalize human knowledge that still separates humans from machines. Artificial Neural Networks have proven to be a useful form of parallel processing of information that employs principles from the organization of the brain. Finally, the evolution is a phenomenon that was initially used to solve optimization problems inspired by the progress in Genetic Algorithms, Evolutionary Computing, and Genetic Programming. These types of evolutionary algorithms are mimicking the natural selection that takes place in populations of living creatures over generations. More recently, the evolution of individual systems within their life-span (self-organization, learning through experience, and self-developing) has attracted attention. These systems called `evolving' came as a result of the research on practical intelligent systems and on-line learning algorithms that are capable of extracting knowledge from data and performing a higher level adaptation of model structure as well as model parameters. Evolving systems can also be considered an extension of the multi-model concept known from the control theory, and of the on-line identification of fuzzy rule-based models. They can also be regarded as an extension of the methods for on-line learning neural networks with flexible structure that can grow and shrink.","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130514038","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251141
Y. Gorshkov, I. Kokshenev, Y. Bodyanskiy, V. Kolodyazhniy, O. Shylo
The problem of adaptive segmentation of time series changing their properties at a priori unknown moments is considered. The proposed approach is based on the idea of indirect sequence clustering which is realized with a novel robust recursive fuzzy clustering algorithm that can process incoming observations online, and is stable with respect to outliers that are often present in real data. An application to the segmentation of a biological time series confirms the efficiency of the proposed algorithm
{"title":"Robust Recursive Fuzzy Clustering-Based Segmentation of Biological Time Series","authors":"Y. Gorshkov, I. Kokshenev, Y. Bodyanskiy, V. Kolodyazhniy, O. Shylo","doi":"10.1109/ISEFS.2006.251141","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251141","url":null,"abstract":"The problem of adaptive segmentation of time series changing their properties at a priori unknown moments is considered. The proposed approach is based on the idea of indirect sequence clustering which is realized with a novel robust recursive fuzzy clustering algorithm that can process incoming observations online, and is stable with respect to outliers that are often present in real data. An application to the segmentation of a biological time series confirms the efficiency of the proposed algorithm","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132639522","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 : 2006-11-30DOI: 10.1109/ISEFS.2006.251164
Rodney A. Gomez, Katherine Lugo, Eric Vallejo
The design of fuzzy controllers for mobile robots navigation in different environments has been considered a difficult task for a long time. In this paper, an evolutionary strategy is developed in which a genetic algorithm builds the rules base of a fuzzy controller during training sessions. The potential of the scheme has been shown in the simulated and real Khepera robot
{"title":"Evolutionary Design of Fuzzy Controllers Based on Messy Coding for a Miniature Mobile Robot","authors":"Rodney A. Gomez, Katherine Lugo, Eric Vallejo","doi":"10.1109/ISEFS.2006.251164","DOIUrl":"https://doi.org/10.1109/ISEFS.2006.251164","url":null,"abstract":"The design of fuzzy controllers for mobile robots navigation in different environments has been considered a difficult task for a long time. In this paper, an evolutionary strategy is developed in which a genetic algorithm builds the rules base of a fuzzy controller during training sessions. The potential of the scheme has been shown in the simulated and real Khepera robot","PeriodicalId":269492,"journal":{"name":"2006 International Symposium on Evolving Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2006-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132211048","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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