The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Shyiming Chen - One of the best experts on this subject based on the ideXlab platform.
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MULTIPLE Fuzzy Rules INTERPOLATION WITH WEIGHTED ANTECEDENT VARIABLES IN SPARSE Fuzzy RULE-BASED SYSTEMS
International Journal of Pattern Recognition and Artificial Intelligence, 2013Co-Authors: Shyiming Chen, Yu-chuan Chang, Ze-jin Chen, Chia-ling ChenAbstract:This paper presents a new method for multiple Fuzzy Rules interpolation with weighted antecedent variables in sparse Fuzzy rule-based systems based on polygonal membership functions. First, the proposed method calculates the normalized weighting vector of each closest Fuzzy rule. Then, it calculates the composite weight of each closest Fuzzy rule. Then, it calculates the left normal point and the right normal point of the Fuzzy interpolative reasoning result , respectively. Finally, it calculates the characteristic points and of the Fuzzy interpolative reasoning result B*, respectively. The experimental results show that the proposed method can generate more reasonable Fuzzy interpolative reasoning results than the existing methods for sparse Fuzzy rule-based systems. The proposed method can overcome the drawbacks of Chang etal.'s method (IEEE Trans. Fuzzy Syst.16(5) (2008) 1285–1301), Chen and Ko's method (IEEE Trans. Fuzzy Syst.16(6) (2008) 1626–1648) and Huang and Shen's method (IEEE Trans. Fuzzy Syst.14(2) (2006) 340–359) for multiple Fuzzy Rules interpolation. It provides us with a useful way for dealing with multiple Fuzzy Rules interpolation in sparse Fuzzy rule-based systems.
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Fuzzy Rules Interpolation for Sparse Fuzzy Rule-Based Systems Based on Interval Type-2 Gaussian Fuzzy Sets and Genetic Algorithms
IEEE Transactions on Fuzzy Systems, 2013Co-Authors: Shyiming Chen, Yu-chuan Chang, Jeng-shyang PanAbstract:In this paper, we present a new method for Fuzzy Rules interpolation for sparse Fuzzy rule-based systems based on interval type-2 Gaussian Fuzzy sets and genetic algorithms. First, we present a method to deal with the interpolation of Fuzzy Rules based on interval type-2 Gaussian Fuzzy sets. We also prove that the proposed method guarantees to produce normal interval type-2 Gaussian Fuzzy sets. Then, we present a method to learn optimal interval type-2 Gaussian Fuzzy sets for sparse Fuzzy rule-based systems based on genetic algorithms. We also apply the proposed Fuzzy Rules interpolation method and the proposed learning method to deal with multivariate regression problems and time series prediction problems. The experimental results show that the proposed Fuzzy Rules interpolation method using the optimally learned interval type-2 Gaussian Fuzzy sets gets higher average accuracy rates than the existing methods.
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weighted Fuzzy interpolative reasoning for sparse Fuzzy rule based systems
Expert Systems With Applications, 2011Co-Authors: Shyiming Chen, Yu-chuan ChangAbstract:In this paper, we present a weighted Fuzzy interpolative reasoning method for sparse Fuzzy rule-based systems, where the antecedent variables appearing in the Fuzzy Rules have different weights. We also present a weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of the Fuzzy Rules for the proposed weighted Fuzzy interpolative reasoning method. We also apply the proposed weighted Fuzzy interpolative reasoning method and the proposed weights-learning algorithm to handle the truck backer-upper control problem. The experimental results show that the proposed Fuzzy interpolative reasoning method using the optimally learned weights by the proposed weights-learning algorithm gets better truck backer-upper control results than the ones by the traditional Fuzzy inference system and the existing Fuzzy interpolative reasoning methods. The proposed method provides us with a useful way for Fuzzy Rules interpolation in sparse Fuzzy rule-based systems.
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automatically constructing grade membership functions of Fuzzy Rules for students evaluation
Expert Systems With Applications, 2008Co-Authors: Shihming Bai, Shyiming ChenAbstract:Some methods have been presented for applying the Fuzzy set theory in education grading systems. In this paper, we present a method to automatically construct the grade membership functions of lenient-type grades, strict-type grades and normal-type grades of Fuzzy Rules, respectively, for students' evaluation. Based on the constructed grade membership functions, the system performs Fuzzy reasoning to infer the scores of students. It provides a useful way to evaluate students' answerscripts in a smarter and fairer manner.
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automatically constructing concept maps based on Fuzzy Rules for adapting learning systems
Expert Systems With Applications, 2008Co-Authors: Shihming Bai, Shyiming ChenAbstract:In recent years, some methods have been presented for dealing with concept maps construction for providing the adaptive learning guidance to students. In this paper, we present a new method to automatically construct concept maps based on Fuzzy Rules and students' testing records. We apply Fuzzy Rules and Fuzzy reasoning techniques to automatically construct concept maps and evaluate the relevance degrees between concepts. The proposed method provides a useful way to automatically construct concept maps in adaptive learning systems.
Francisco Herrera - One of the best experts on this subject based on the ideXlab platform.
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nmeef sd non dominated multiobjective evolutionary algorithm for extracting Fuzzy Rules in subgroup discovery
IEEE Transactions on Fuzzy Systems, 2010Co-Authors: C J Carmona, Patricia Gonzalez Gonzalez, M J Del Jesus, Francisco HerreraAbstract:A non-dominated multiobjective evolutionary algorithm for extracting Fuzzy Rules in subgroup discovery (NMEEF-SD) is described and analyzed in this paper. This algorithm, which is based on the hybridization between Fuzzy logic and genetic algorithms, deals with subgroup-discovery problems in order to extract novel and interpretable Fuzzy Rules of interest, and the evolutionary Fuzzy system NMEEF-SD is based on the well-known Non-dominated Sorting Genetic Algorithm II (NSGA-II) model but is oriented toward the subgroup-discovery task using specific operators to promote the extraction of interpretable and high-quality subgroup-discovery Rules. The proposal includes different mechanisms to improve diversity in the population and permits the use of different combinations of quality measures in the evolutionary process. An elaborate experimental study, which was reinforced by the use of nonparametric tests, was performed to verify the validity of the proposal, and the proposal was compared with other subgroup discovery methods. The results show that NMEEF-SD obtains the best results among several algorithms studied.
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increasing Fuzzy Rules cooperation based on evolutionary adaptive inference systems research articles
Journal of intelligent systems, 2007Co-Authors: Jesus Alcalafdez, Francisco Herrera, Francisco Alfredo Marquez, Antonio PeregrinAbstract:This article presents a study on the use of parametrized operators in the Inference System of linguistic Fuzzy systems adapted by evolutionary algorithms, for achieving better cooperation among Fuzzy Rules. This approach produces a kind of rule cooperation by means of the inference system, increasing the accuracy of the Fuzzy system without losing its interpretability. We study the different alternatives for introducing parameters in the Inference System and analyze their interpretation and how they affect the rest of the components of the Fuzzy system. We take into account three applications in order to analyze their accuracy in practice. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 1035–1064, 2007.
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learning cooperative linguistic Fuzzy Rules using the best worst ant system algorithm
International Journal of Intelligent Systems, 2005Co-Authors: Jorge Casillas, Oscar Cordon, Inaki Fernandez De Viana, Francisco HerreraAbstract:Within the field of linguistic Fuzzy modeling with Fuzzy rule-based systems, the automatic derivation of the linguistic Fuzzy Rules from numerical data is an important task. In the last few years, a large number of contributions based on techniques such as neural networks and genetic algorithms have been proposed to face this problem. In this article, we introduce a novel approach to the Fuzzy rule learning problem with ant colony optimization (ACO) algorithms. To do so, this learning task is formulated as a combinatorial optimization problem. Our learning process is based on the COR methodology proposed in previous works, which provides a search space that allows us to obtain Fuzzy models with a good interpretability–accuracy trade-off. A specific ACO-based algorithm, the Best–Worst Ant System, is used for this purpose due to the good performance shown when solving other optimization problems. We analyze the behavior of the proposed method and compare it to other learning methods and search techniques when solving two real-world applications. The obtained results lead us to remark the good performance of our proposal in terms of interpretability, accuracy, and efficiency. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 433–452, 2005.
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learning cooperative linguistic Fuzzy Rules using the best worst ant system algorithm research articles
International Journal of Intelligent Systems, 2005Co-Authors: Jorge Casillas, Oscar Cordon, Inaki Fernandez De Viana, Francisco HerreraAbstract:Within the field of linguistic Fuzzy modeling with Fuzzy rule-based systems, the automatic derivation of the linguistic Fuzzy Rules from numerical data is an important task. In the last few years, a large number of contributions based on techniques such as neural networks and genetic algorithms have been proposed to face this problem. In this article, we introduce a novel approach to the Fuzzy rule learning problem with ant colony optimization (ACO) algorithms. To do so, this learning task is formulated as a combinatorial optimization problem. Our learning process is based on the COR methodology proposed in previous works, which provides a search space that allows us to obtain Fuzzy models with a good interpretability–accuracy trade-off. A specific ACO-based algorithm, the Best–Worst Ant System, is used for this purpose due to the good performance shown when solving other optimization problems. We analyze the behavior of the proposed method and compare it to other learning methods and search techniques when solving two real-world applications. The obtained results lead us to remark the good performance of our proposal in terms of interpretability, accuracy, and efficiency. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 433–452, 2005.
Jorge Casillas - One of the best experts on this subject based on the ideXlab platform.
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learning cooperative linguistic Fuzzy Rules using the best worst ant system algorithm
International Journal of Intelligent Systems, 2005Co-Authors: Jorge Casillas, Oscar Cordon, Inaki Fernandez De Viana, Francisco HerreraAbstract:Within the field of linguistic Fuzzy modeling with Fuzzy rule-based systems, the automatic derivation of the linguistic Fuzzy Rules from numerical data is an important task. In the last few years, a large number of contributions based on techniques such as neural networks and genetic algorithms have been proposed to face this problem. In this article, we introduce a novel approach to the Fuzzy rule learning problem with ant colony optimization (ACO) algorithms. To do so, this learning task is formulated as a combinatorial optimization problem. Our learning process is based on the COR methodology proposed in previous works, which provides a search space that allows us to obtain Fuzzy models with a good interpretability–accuracy trade-off. A specific ACO-based algorithm, the Best–Worst Ant System, is used for this purpose due to the good performance shown when solving other optimization problems. We analyze the behavior of the proposed method and compare it to other learning methods and search techniques when solving two real-world applications. The obtained results lead us to remark the good performance of our proposal in terms of interpretability, accuracy, and efficiency. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 433–452, 2005.
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learning cooperative linguistic Fuzzy Rules using the best worst ant system algorithm research articles
International Journal of Intelligent Systems, 2005Co-Authors: Jorge Casillas, Oscar Cordon, Inaki Fernandez De Viana, Francisco HerreraAbstract:Within the field of linguistic Fuzzy modeling with Fuzzy rule-based systems, the automatic derivation of the linguistic Fuzzy Rules from numerical data is an important task. In the last few years, a large number of contributions based on techniques such as neural networks and genetic algorithms have been proposed to face this problem. In this article, we introduce a novel approach to the Fuzzy rule learning problem with ant colony optimization (ACO) algorithms. To do so, this learning task is formulated as a combinatorial optimization problem. Our learning process is based on the COR methodology proposed in previous works, which provides a search space that allows us to obtain Fuzzy models with a good interpretability–accuracy trade-off. A specific ACO-based algorithm, the Best–Worst Ant System, is used for this purpose due to the good performance shown when solving other optimization problems. We analyze the behavior of the proposed method and compare it to other learning methods and search techniques when solving two real-world applications. The obtained results lead us to remark the good performance of our proposal in terms of interpretability, accuracy, and efficiency. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 433–452, 2005.
Nandini Mukherjee - One of the best experts on this subject based on the ideXlab platform.
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multi criteria decision analysis assisted routing in wireless sensor network using Fuzzy Rules
International Conference of Distributed Computing and Networking, 2015Co-Authors: Suman Bhunia, Bijoy Das, Nandini MukherjeeAbstract:Wireless sensor network is a self-organizing wireless network system which has enabled densely deployment of nodes. These wireless sensors gather and forward data. But finding an efficient route is a challenge while the nodes communicate for data transmission. A routing algorithm, FMCR, is proposed in this paper. A well-known operations research technique, multi-criteria decision analysis, is used in this proposed scheme. Here multiple criteria, such as residual energy, packet transmission frequency and hop count are taken into account. In order to assign the weighted values on each criterion, Fuzzy Rules are applied on heuristic properties like node density, dead nodes and delay. The best route is selected using Weighted Product Model (WPM). This scheme has been implemented using TinyOS, an event-driven operating system designed for wireless sensor network.
Yu-chuan Chang - One of the best experts on this subject based on the ideXlab platform.
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MULTIPLE Fuzzy Rules INTERPOLATION WITH WEIGHTED ANTECEDENT VARIABLES IN SPARSE Fuzzy RULE-BASED SYSTEMS
International Journal of Pattern Recognition and Artificial Intelligence, 2013Co-Authors: Shyiming Chen, Yu-chuan Chang, Ze-jin Chen, Chia-ling ChenAbstract:This paper presents a new method for multiple Fuzzy Rules interpolation with weighted antecedent variables in sparse Fuzzy rule-based systems based on polygonal membership functions. First, the proposed method calculates the normalized weighting vector of each closest Fuzzy rule. Then, it calculates the composite weight of each closest Fuzzy rule. Then, it calculates the left normal point and the right normal point of the Fuzzy interpolative reasoning result , respectively. Finally, it calculates the characteristic points and of the Fuzzy interpolative reasoning result B*, respectively. The experimental results show that the proposed method can generate more reasonable Fuzzy interpolative reasoning results than the existing methods for sparse Fuzzy rule-based systems. The proposed method can overcome the drawbacks of Chang etal.'s method (IEEE Trans. Fuzzy Syst.16(5) (2008) 1285–1301), Chen and Ko's method (IEEE Trans. Fuzzy Syst.16(6) (2008) 1626–1648) and Huang and Shen's method (IEEE Trans. Fuzzy Syst.14(2) (2006) 340–359) for multiple Fuzzy Rules interpolation. It provides us with a useful way for dealing with multiple Fuzzy Rules interpolation in sparse Fuzzy rule-based systems.
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Fuzzy Rules Interpolation for Sparse Fuzzy Rule-Based Systems Based on Interval Type-2 Gaussian Fuzzy Sets and Genetic Algorithms
IEEE Transactions on Fuzzy Systems, 2013Co-Authors: Shyiming Chen, Yu-chuan Chang, Jeng-shyang PanAbstract:In this paper, we present a new method for Fuzzy Rules interpolation for sparse Fuzzy rule-based systems based on interval type-2 Gaussian Fuzzy sets and genetic algorithms. First, we present a method to deal with the interpolation of Fuzzy Rules based on interval type-2 Gaussian Fuzzy sets. We also prove that the proposed method guarantees to produce normal interval type-2 Gaussian Fuzzy sets. Then, we present a method to learn optimal interval type-2 Gaussian Fuzzy sets for sparse Fuzzy rule-based systems based on genetic algorithms. We also apply the proposed Fuzzy Rules interpolation method and the proposed learning method to deal with multivariate regression problems and time series prediction problems. The experimental results show that the proposed Fuzzy Rules interpolation method using the optimally learned interval type-2 Gaussian Fuzzy sets gets higher average accuracy rates than the existing methods.
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weighted Fuzzy interpolative reasoning for sparse Fuzzy rule based systems
Expert Systems With Applications, 2011Co-Authors: Shyiming Chen, Yu-chuan ChangAbstract:In this paper, we present a weighted Fuzzy interpolative reasoning method for sparse Fuzzy rule-based systems, where the antecedent variables appearing in the Fuzzy Rules have different weights. We also present a weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of the Fuzzy Rules for the proposed weighted Fuzzy interpolative reasoning method. We also apply the proposed weighted Fuzzy interpolative reasoning method and the proposed weights-learning algorithm to handle the truck backer-upper control problem. The experimental results show that the proposed Fuzzy interpolative reasoning method using the optimally learned weights by the proposed weights-learning algorithm gets better truck backer-upper control results than the ones by the traditional Fuzzy inference system and the existing Fuzzy interpolative reasoning methods. The proposed method provides us with a useful way for Fuzzy Rules interpolation in sparse Fuzzy rule-based systems.
