The Experts below are selected from a list of 72 Experts worldwide ranked by ideXlab platform
Huaping Liu - One of the best experts on this subject based on the ideXlab platform.
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hierarchical structured sparse representation for t s fuzzy systems identification
IEEE Transactions on Fuzzy Systems, 2013Co-Authors: Minnan Luo, Fuchun Sun, Huaping LiuAbstract:“The curse of dimensionality” has become a significant bottleneck for fuzzy system identification and approximation. In this paper, we cast the Takagi-Sugeno (T-S) fuzzy system identification into a hierarchical sparse representation problem, where our goal is to establish a T-S fuzzy system with a minimal number of fuzzy Rules, which simultaneously have a minimal number of nonzero consequent parameters. The proposed method, which is called hierarchical sparse fuzzy inference systems ( H-sparseFIS), explicitly takes into account the block-structured information that exists in the T-S fuzzy model and works in an intuitive way: First, initial fuzzy Rule Antecedent part is extracted automatically by an iterative vector quantization clustering method; then, with block-structured sparse representation, the main important fuzzy Rules are selected, and the redundant ones are eliminated for better model accuracy and generalization performance; moreover, we simplify the selected fuzzy Rules consequent with sparse regularization such that more consequent parameters can approximate to zero. This algorithm is very efficient and shows good performance in well-known benchmark datasets and real-world problems.
Uzay Kaymak - One of the best experts on this subject based on the ideXlab platform.
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an enhanced approach to Rule base simplification of first order takagi sugeno fuzzy inference systems
Combinatorial Pattern Matching, 2017Co-Authors: Caro Fuchs, Anna Wilbik, Saskia Van Loon, Arjenkars Boer, Uzay KaymakAbstract:Fuzzy Rule base simplification is used to reduce the complexity of fuzzy models identified from the data. In this paper, an enhanced approach is proposed for simplifying the Rule base of fuzzy inference systems when all the membership functions for a variable are highly similar to one another. In this case it is possible to remove a variable from the Rule Antecedent, but keep it in the Rule consequent. Experimental results show that simpler Rules can be obtained while barely sacrificing accuracy.
Minnan Luo - One of the best experts on this subject based on the ideXlab platform.
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hierarchical structured sparse representation for t s fuzzy systems identification
IEEE Transactions on Fuzzy Systems, 2013Co-Authors: Minnan Luo, Fuchun Sun, Huaping LiuAbstract:“The curse of dimensionality” has become a significant bottleneck for fuzzy system identification and approximation. In this paper, we cast the Takagi-Sugeno (T-S) fuzzy system identification into a hierarchical sparse representation problem, where our goal is to establish a T-S fuzzy system with a minimal number of fuzzy Rules, which simultaneously have a minimal number of nonzero consequent parameters. The proposed method, which is called hierarchical sparse fuzzy inference systems ( H-sparseFIS), explicitly takes into account the block-structured information that exists in the T-S fuzzy model and works in an intuitive way: First, initial fuzzy Rule Antecedent part is extracted automatically by an iterative vector quantization clustering method; then, with block-structured sparse representation, the main important fuzzy Rules are selected, and the redundant ones are eliminated for better model accuracy and generalization performance; moreover, we simplify the selected fuzzy Rules consequent with sparse regularization such that more consequent parameters can approximate to zero. This algorithm is very efficient and shows good performance in well-known benchmark datasets and real-world problems.
Caro Fuchs - One of the best experts on this subject based on the ideXlab platform.
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an enhanced approach to Rule base simplification of first order takagi sugeno fuzzy inference systems
Combinatorial Pattern Matching, 2017Co-Authors: Caro Fuchs, Anna Wilbik, Saskia Van Loon, Arjenkars Boer, Uzay KaymakAbstract:Fuzzy Rule base simplification is used to reduce the complexity of fuzzy models identified from the data. In this paper, an enhanced approach is proposed for simplifying the Rule base of fuzzy inference systems when all the membership functions for a variable are highly similar to one another. In this case it is possible to remove a variable from the Rule Antecedent, but keep it in the Rule consequent. Experimental results show that simpler Rules can be obtained while barely sacrificing accuracy.
Russell R Rhinehart - One of the best experts on this subject based on the ideXlab platform.
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a generalized tsk model with a novel Rule Antecedent structure structure identification and parameter estimation
Computers & Chemical Engineering, 2010Co-Authors: Russell R RhinehartAbstract:TSK fuzzy models are convenient tools for describing complex nonlinear behavior. However, the existing combinatorial Antecedent structure in TSK models makes them substantially suffer from the curse of dimensionality. In this work, a novel Rule Antecedent structure is proposed to design an efficient generalized TSK (GTSK) model by using fewer Rules. The new Rule Antecedent only uses nonlinear variables. Additionally, one more degree of freedom is introduced to design Antecedents to cover an Antecedent space more efficiently, which further reduces the number of Rules. The resultant GTSK model is identified in two stages. A novel recursive estimation based on spatially rearranged data is used to determine the consequent and Antecedent variables. Model parameter values are obtained from partitioned Antecedent space, which is the result of solving a series of splitting and regression problems.
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a novel Rule Antecedent structure and its identification for fuzzy models
American Control Conference, 2009Co-Authors: Russell R RhinehartAbstract:The existing combinatorial Antecedent structure in fuzzy models makes them suffer from “the curse of dimensionality”. In this work, a novel Rule Antecedent structure is proposed to design an efficient fuzzy model by using fewer Rules. The new Rule Antecedent only uses nonlinear variables. Additionally, the proposed Rule Antecedents are expressed as ellipsoids covering the underlying local regions, which make spatial coverage more efficient.
