The Experts below are selected from a list of 54711 Experts worldwide ranked by ideXlab platform
I. Burhan Turksen - One of the best experts on this subject based on the ideXlab platform.
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EUSFLAT Conf. - A Type 2 Fuzzy System modelling algorithm.
2020Co-Authors: Kemal Kilic, Ozge Uncu, I. Burhan TurksenAbstract:In this paper, a modified Fuzzy System modelling algorithm that incorporates Type 2 Fuzzy sets, which is based on intervalvalued membership degrees rather than singleton membership degrees, is proposed. The proposed algorithm is evaluated in terms of predictive performance and determination of the significance degrees and compared with other algorithms in the literature, namely Stepwise Multiple Linear Regression (SMLR) and Sugeno-Yasukawa [4] based Fuzzy System modelling algorithm, i.e. Turksen-Bazoon (T-B) [3]. A nonlinear function, which is introduced as a benchmarking data set by SugenoYasukawa, is used for validating the models. The proposed algorithm outperformed the other alternatives both in terms of the root mean square error (RMSE) and in terms of the determination of the significance of the inputs. These results showed that the proposed Fuzzy System modelling algorithm could effectively approximate nonlinear functions with simple Fuzzy if-then rules without assuming a priori structure for the model.
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Discrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parameters
IEEE Transactions on Fuzzy Systems, 2007Co-Authors: Ozge Uncu, I. Burhan TurksenAbstract:Fuzzy System modeling (FSM) is one of the most prominent tools that can be used to identify the behavior of highly nonlinear Systems with uncertainty. Conventional FSM techniques utilize type 1 Fuzzy sets in order to capture the uncertainty in the System. However, since type 1 Fuzzy sets express the belongingness of a crisp value x' of a base variable x in a Fuzzy set A by a crisp membership value muA(x'), they cannot fully capture the uncertainties due to imprecision in identifying membership functions. Higher types of Fuzzy sets can be a remedy to address this issue. Since, the computational complexity of operations on Fuzzy sets are increasing with the increasing type of the Fuzzy set, the use of type 2 Fuzzy sets and linguistic logical connectives drew a considerable amount of attention in the realm of Fuzzy System modeling in the last two decades. In this paper, we propose a black-box methodology that can identify robust type 2 Takagi-Sugeno, Mizumoto and Linguistic Fuzzy System models with high predictive power. One of the essential problems of type 2 Fuzzy System models is computational complexity. In order to remedy this problem, discrete interval valued type 2 Fuzzy System models are proposed with type reduction. In the proposed Fuzzy System modeling methods, Fuzzy C-means (FCM) clustering algorithm is used in order to identify the System structure. The proposed discrete interval valued type 2 Fuzzy System models are generated by a learning parameter of FCM, known as the level of membership, and its variation over a specific set of values which generate the uncertainty associated with the System structure
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Intelligent Fuzzy System Modeling
Computational Intelligence: Soft Computing and Fuzzy-Neuro Integration with Applications, 1998Co-Authors: I. Burhan TurksenAbstract:A Systems modeling is proposed with a unification of Fuzzy methodologies. Three knowledge representation schemas are presented together with the corresponding approximate reasoning methods. Unsupervised learning of Fuzzy sets and rules is reviewed with recent developments in Fuzzy cluster analysis techniques. The resultant Fuzzy sets are determined with a Euclidian distance-based similarity view of membership functions. Finally, an intelligent Fuzzy System model development is proposed with proper learning in order to adapt to an actual System performance output. In this approach, connectives are not chosen a priori but learned with an iterative training depending on a given data set.
Prodromos D. Chatzoglou - One of the best experts on this subject based on the ideXlab platform.
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An intelligent short term stock trading Fuzzy System for assisting investors in portfolio management
Expert Systems with Applications, 2016Co-Authors: Konstandinos Chourmouziadis, Prodromos D. ChatzoglouAbstract:Financial markets are complex Systems influenced by many interrelated economic, political and psychological factors and characterised by inherent nonlinearities. Recently, there have been many efforts towards stock market prediction, applying various Fuzzy logic techniques and using technical analysis methods. This paper presents a short term trading Fuzzy System using a novel trading strategy and an "amalgam" between altered commonly used technical indicators and rarely used ones, in order to assist investors in their portfolio management. The sample consists of daily data from the general index of the Athens Stock Exchange over a period of more than 15 years (15/11/1996 to 5/6/2012), which was also divided into distinctive groups of bull and bear market periods. The results suggest that, with or without taking into consideration transaction costs, the return of the proposed Fuzzy model is superior to the returns of the buy and hold strategy. Τhe proposed System can be characterised as conservative, since it produces smaller losses during bear market periods and smaller gains during bull market periods compared with the buy and hold strategy.
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an intelligent short term stock trading Fuzzy System for assisting investors in portfolio management
Expert Systems With Applications, 2016Co-Authors: Konstandinos Chourmouziadis, Prodromos D. ChatzoglouAbstract:The proposed short-term Fuzzy System uses a set of appropriate technical indicators.The returns of the proposed System are higher than those of the B&H strategy.The proposed System avoids big losses during bear markets.During bull markets the System produces lower returns than the B&H strategy.Transaction costs significantly affect the performance of the proposed System. Financial markets are complex Systems influenced by many interrelated economic, political and psychological factors and characterised by inherent nonlinearities. Recently, there have been many efforts towards stock market prediction, applying various Fuzzy logic techniques and using technical analysis methods.This paper presents a short term trading Fuzzy System using a novel trading strategy and an "amalgam" between altered commonly used technical indicators and rarely used ones, in order to assist investors in their portfolio management. The sample consists of daily data from the general index of the Athens Stock Exchange over a period of more than 15 years (15/11/1996 to 5/6/2012), which was also divided into distinctive groups of bull and bear market periods.The results suggest that, with or without taking into consideration transaction costs, the return of the proposed Fuzzy model is superior to the returns of the buy and hold strategy. ?he proposed System can be characterised as conservative, since it produces smaller losses during bear market periods and smaller gains during bull market periods compared with the buy and hold strategy.
Xiaolei Wang - One of the best experts on this subject based on the ideXlab platform.
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A simplified linguistic information feedback-based dynamical Fuzzy System
Neural Computing and Applications, 2010Co-Authors: Seppo J. Ovaska, Xiaolei WangAbstract:Inspired by the linguistic information feedback-based dynamical Fuzzy System (LIFDFS) recently proposed by the authors, we present a simplified LIFDFS (S-LIFDFS) model in this paper, which has a simpler linguistic information feedback structure. Compared with the LIFDFS, the S-LIFDFS can offer us with a considerably reduced computational complexity. We first give a detailed description of its underlying principle. Based on the gradient descent method, an adaptive learning algorithm for the feedback parameters is next derived. We also discuss the application of this S-LIFDFS in time series prediction. Three evaluation examples including prediction of two artificial time sequences and the well-known Box–Jenkins gas furnace data are demonstrated here. Simulation results illustrate that with a compact structure, our S-LIFDFS can still retain the advantage of inherent dynamics of linguistic information feedback and is, therefore, well suited for handling temporal problems like prediction, modeling, and control.
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A linguistic information feed-back-based dynamical Fuzzy System (LIFBDFS) with learning algorithm
Neural Computing and Applications, 2009Co-Authors: Seppo J. Ovaska, Xiaolei WangAbstract:In this study, the linguistic information feed-back-based dynamical Fuzzy System (LIFBDFS) proposed earlier by the authors is first introduced. The principles of α-level sets and backpropagation through time approach are also briefly discussed. We next employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFBDFS. With this training algorithm, our LIFBDFS indeed becomes a potential candidate in solving real-time modeling and prediction problems.
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A simplified linguistic information feedback-based dynamical Fuzzy System (S-LIFDFS) - Part I. Theory
Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications 2005. SMCia 05., 2005Co-Authors: Seppo J. Ovaska, Xiaolei WangAbstract:For pt.2 see ibid., p.51-6 (2005). This work consists of two parts: theory and evaluation. In Part I, inspired by the linguistic information feedback-based dynamical Fuzzy System (LIFDFS) recently proposed by the authors, we present a simplified LIFDFS (S-LIFDFS) model, which has a simpler linguistic information feedback structure. Compared with the LIFDFS, the S-LIFDFS can offer us with the considerably reduced computational complexity. We first give a detailed description of its underlying principle. Based on the gradient descent method, an adaptive learning algorithm for the feedback parameters is next derived. Part II of this work discusses applying this S-LIFDFS in time series prediction. Three evaluation examples including prediction of two artificial time sequences and the well-known Box-Jenkins gas furnace data are demonstrated here. Simulation results illustrate that with a compact structure, our S-LIFDFS can still retain the advantage of inherent dynamics of linguistic information feedback, and is, therefore, well suited for handling temporal problems like prediction, modeling, and control.
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A simplified linguistic information feedback-based dynamical Fuzzy System (S-LIFDFS)-Part II. Evaluation
Proceedings of the 2005 IEEE Midnight-Summer Workshop on Soft Computing in Industrial Applications 2005. SMCia 05., 2005Co-Authors: Seppo J. Ovaska, Xiaolei WangAbstract:For pt.1 see ibid., p.41-50 (2005). Part I of this paper introduces the simplified linguistic information feedback-based dynamical Fuzzy System (S-LIFDFS) as well as adaptive training algorithms for the feedback parameters. In Part II, we are going to evaluate the effectiveness of the proposed S-LIFDFS and its learning algorithms in time series prediction. Two artificial time sequences and the well-known Box-Jenkins gas furnace data are used.
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Learning algorithm for linguistic information feedback-based dynamical Fuzzy System (LIFDFS)
2004 IEEE International Conference on Systems Man and Cybernetics (IEEE Cat. No.04CH37583), 2004Co-Authors: Seppo J. Ovaska, Xiaolei WangAbstract:In this paper, the linguistic information feedback-based dynamical Fuzzy System (LIFDFS) proposed earlier by the authors is first introduced. The principles of a level sets and backpropagation through time (BTT) are also briefly discussed. We employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFDFS. With this training algorithm, our LIFDFS becomes a potential candidate in solving real-time modeling and prediction problems.
Konstandinos Chourmouziadis - One of the best experts on this subject based on the ideXlab platform.
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An intelligent short term stock trading Fuzzy System for assisting investors in portfolio management
Expert Systems with Applications, 2016Co-Authors: Konstandinos Chourmouziadis, Prodromos D. ChatzoglouAbstract:Financial markets are complex Systems influenced by many interrelated economic, political and psychological factors and characterised by inherent nonlinearities. Recently, there have been many efforts towards stock market prediction, applying various Fuzzy logic techniques and using technical analysis methods. This paper presents a short term trading Fuzzy System using a novel trading strategy and an "amalgam" between altered commonly used technical indicators and rarely used ones, in order to assist investors in their portfolio management. The sample consists of daily data from the general index of the Athens Stock Exchange over a period of more than 15 years (15/11/1996 to 5/6/2012), which was also divided into distinctive groups of bull and bear market periods. The results suggest that, with or without taking into consideration transaction costs, the return of the proposed Fuzzy model is superior to the returns of the buy and hold strategy. Τhe proposed System can be characterised as conservative, since it produces smaller losses during bear market periods and smaller gains during bull market periods compared with the buy and hold strategy.
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an intelligent short term stock trading Fuzzy System for assisting investors in portfolio management
Expert Systems With Applications, 2016Co-Authors: Konstandinos Chourmouziadis, Prodromos D. ChatzoglouAbstract:The proposed short-term Fuzzy System uses a set of appropriate technical indicators.The returns of the proposed System are higher than those of the B&H strategy.The proposed System avoids big losses during bear markets.During bull markets the System produces lower returns than the B&H strategy.Transaction costs significantly affect the performance of the proposed System. Financial markets are complex Systems influenced by many interrelated economic, political and psychological factors and characterised by inherent nonlinearities. Recently, there have been many efforts towards stock market prediction, applying various Fuzzy logic techniques and using technical analysis methods.This paper presents a short term trading Fuzzy System using a novel trading strategy and an "amalgam" between altered commonly used technical indicators and rarely used ones, in order to assist investors in their portfolio management. The sample consists of daily data from the general index of the Athens Stock Exchange over a period of more than 15 years (15/11/1996 to 5/6/2012), which was also divided into distinctive groups of bull and bear market periods.The results suggest that, with or without taking into consideration transaction costs, the return of the proposed Fuzzy model is superior to the returns of the buy and hold strategy. ?he proposed System can be characterised as conservative, since it produces smaller losses during bear market periods and smaller gains during bull market periods compared with the buy and hold strategy.
Ozge Uncu - One of the best experts on this subject based on the ideXlab platform.
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EUSFLAT Conf. - A Type 2 Fuzzy System modelling algorithm.
2020Co-Authors: Kemal Kilic, Ozge Uncu, I. Burhan TurksenAbstract:In this paper, a modified Fuzzy System modelling algorithm that incorporates Type 2 Fuzzy sets, which is based on intervalvalued membership degrees rather than singleton membership degrees, is proposed. The proposed algorithm is evaluated in terms of predictive performance and determination of the significance degrees and compared with other algorithms in the literature, namely Stepwise Multiple Linear Regression (SMLR) and Sugeno-Yasukawa [4] based Fuzzy System modelling algorithm, i.e. Turksen-Bazoon (T-B) [3]. A nonlinear function, which is introduced as a benchmarking data set by SugenoYasukawa, is used for validating the models. The proposed algorithm outperformed the other alternatives both in terms of the root mean square error (RMSE) and in terms of the determination of the significance of the inputs. These results showed that the proposed Fuzzy System modelling algorithm could effectively approximate nonlinear functions with simple Fuzzy if-then rules without assuming a priori structure for the model.
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Discrete Interval Type 2 Fuzzy System Models Using Uncertainty in Learning Parameters
IEEE Transactions on Fuzzy Systems, 2007Co-Authors: Ozge Uncu, I. Burhan TurksenAbstract:Fuzzy System modeling (FSM) is one of the most prominent tools that can be used to identify the behavior of highly nonlinear Systems with uncertainty. Conventional FSM techniques utilize type 1 Fuzzy sets in order to capture the uncertainty in the System. However, since type 1 Fuzzy sets express the belongingness of a crisp value x' of a base variable x in a Fuzzy set A by a crisp membership value muA(x'), they cannot fully capture the uncertainties due to imprecision in identifying membership functions. Higher types of Fuzzy sets can be a remedy to address this issue. Since, the computational complexity of operations on Fuzzy sets are increasing with the increasing type of the Fuzzy set, the use of type 2 Fuzzy sets and linguistic logical connectives drew a considerable amount of attention in the realm of Fuzzy System modeling in the last two decades. In this paper, we propose a black-box methodology that can identify robust type 2 Takagi-Sugeno, Mizumoto and Linguistic Fuzzy System models with high predictive power. One of the essential problems of type 2 Fuzzy System models is computational complexity. In order to remedy this problem, discrete interval valued type 2 Fuzzy System models are proposed with type reduction. In the proposed Fuzzy System modeling methods, Fuzzy C-means (FCM) clustering algorithm is used in order to identify the System structure. The proposed discrete interval valued type 2 Fuzzy System models are generated by a learning parameter of FCM, known as the level of membership, and its variation over a specific set of values which generate the uncertainty associated with the System structure
