Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm

Although traditional fuzzy models have proven to have high capacity of approximating the real-world systems, they have some challenges, such as computational complexity, optimization problems, subjectivity, etc. In order to solve some of these problems, this paper proposes a new fuzzy system modelin...

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Published in:	IEEE transactions on fuzzy systems Vol. 16; no. 3; pp. 779 - 794
Main Authors:	Celikyilmaz, A., Burhan Turksen, I.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.06.2008 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Algorithms Clustering Clustering algorithms Computational complexity Engines Fuzzy Fuzzy functions (FFs) Fuzzy logic Fuzzy set theory fuzzy system modeling (FSM) Fuzzy systems improved fuzzy clustering (IFC) Inference algorithms inference mechanisms Input variables Least squares approximation Mathematical analysis Mathematical models Parameter estimation Partitioning algorithms Studies Yield estimation
ISSN:	1063-6706, 1941-0034
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Abstract	Although traditional fuzzy models have proven to have high capacity of approximating the real-world systems, they have some challenges, such as computational complexity, optimization problems, subjectivity, etc. In order to solve some of these problems, this paper proposes a new fuzzy system modeling approach based on improved fuzzy functions to model systems with continuous output variable. The new modeling approach introduces three features: i) an improved fuzzy clustering (IFC) algorithm, ii) a new structure identification algorithm, and iii) a nonparametric inference engine. The IFC algorithm yields simultaneous estimates of parameters of c-regression models, together with fuzzy c-partitioning of the data, to calculate improved membership values with a new membership function. The structure identification of the new approach utilizes IFC, instead of standard fuzzy c-means clustering algorithm, to fuzzy partition the data, and it uses improved membership values as additional input variables along with the original scalar input variables for two different choices of regression methods: least squares estimation or support vector regression, to determine ldquofuzzy functionsrdquo for each cluster. With novel IFC, one could learn the system behavior more accurately compared to other FSM models. The nonparametric inference engine is a new approach, which uses the alike -nearest neighbor method for reasoning. Empirical comparisons indicate that the proposed approach yields comparable or better accuracy than fuzzy or neuro-fuzzy models based on fuzzy rules bases, as well as other soft computing methods.
AbstractList	Although traditional fuzzy models have proven to have high capacity of approximating the real-world systems, they have some challenges, such as computational complexity, optimization problems, subjectivity, etc. In order to solve some of these problems, this paper proposes a new fuzzy system modeling approach based on improved fuzzy functions to model systems with continuous output variable. The new modeling approach introduces three features: i) an improved fuzzy clustering (IFC) algorithm II) a new structure identification algorithm, and III) a nonparametric inference engine. The IFC algorithm yields simultaneous estimates of parameters of c-regression models, together with fuzzy c-partitioning of the data, to calculate improved membership values with a new membership function. The structure identification of the new approach utilizes IFC, instead of standard fuzzy c-means clustering algorithm, to fuzzy partition the data, and it uses improved membership values as additional input variables along with the original scalar input variables for two different choices of regression methods: least squares estimation or support vector regression, to determine ldquofuzzy functionsrdquo for each cluster. With novel IFC, one could learn the system behavior more accurately compared to other FSM models. The nonparametric inference engine is a new approach, which uses the alike -nearest neighbor method for reasoning. Empirical comparisons indicate that the proposed approach yields comparable or better accuracy than fuzzy or neuro-fuzzy models based on fuzzy rules bases, as well as other soft computing methods. The structure identification of the new approach utilizes IFC, instead of standard fuzzy c-means clustering algorithm, to fuzzy partition the data, and it uses improved membership values as additional input variables along with the original scalar input variables for two different choices of regression methods: least squares estimation or support vector regression, to determine ldquofuzzy functionsrdquo for each cluster. Although traditional fuzzy models have proven to have high capacity of approximating the real-world systems, they have some challenges, such as computational complexity, optimization problems, subjectivity, etc. In order to solve some of these problems, this paper proposes a new fuzzy system modeling approach based on improved fuzzy functions to model systems with continuous output variable. The new modeling approach introduces three features: i) an improved fuzzy clustering (IFC) algorithm, ii) a new structure identification algorithm, and iii) a nonparametric inference engine. The IFC algorithm yields simultaneous estimates of parameters of c-regression models, together with fuzzy c-partitioning of the data, to calculate improved membership values with a new membership function. The structure identification of the new approach utilizes IFC, instead of standard fuzzy c-means clustering algorithm, to fuzzy partition the data, and it uses improved membership values as additional input variables along with the original scalar input variables for two different choices of regression methods: least squares estimation or support vector regression, to determine "fuzzy functions" for each cluster. With novel IFC, one could learn the system behavior more accurately compared to other FSM models. The nonparametric inference engine is a new approach, which uses the alike -nearest neighbor method for reasoning. Empirical comparisons indicate that the proposed approach yields comparable or better accuracy than fuzzy or neuro-fuzzy models based on fuzzy rules bases, as well as other soft computing methods.
Author	Celikyilmaz, A. Burhan Turksen, I.
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References	ref12 ref15 ref14 ref10 chang (ref7) 2001 gunn (ref13) 1998 ref17 ref19 ref18 hill (ref16) 0 cristianini (ref9) 2000 ref50 uncu (ref43) 2003 ref47 ref41 kosko (ref25) 1992 ref44 ref49 turksen (ref40) 2006; 8 ref8 uncu (ref42) 2001 lin (ref27) 1996 ref4 ref3 weiss (ref48) 1991 montgomery (ref29) 1992 ref35 ref34 ref37 ref36 celikyilmaz (ref6) 2007 vergara (ref46) 1997 ref30 ref33 ref2 ref1 ref39 ref38 vapnik (ref45) 1998 kilic (ref22) 2001 ref23 ref26 ref20 ref21 (ref11) 0 ref28 (ref32) 0 newman (ref31) 1998 celikyilmaz (ref5) 0 kohavi (ref24) 1995
References_xml	– ident: ref23 doi: 10.1109/91.728458 – ident: ref50 doi: 10.1109/NAFIPS.2004.1337399 – year: 1992 ident: ref29 publication-title: Introduction to Linear Regression Analysis – ident: ref44 doi: 10.1109/ICSMC.2004.1400667 – ident: ref19 doi: 10.1109/IJCNN.2004.1380933 – ident: ref33 doi: 10.1016/j.ins.2007.06.028 – year: 1998 ident: ref31 publication-title: UCI repository of machine learning databases – ident: ref1 doi: 10.1007/978-3-642-60767-7_2 – ident: ref49 doi: 10.1109/ICIMA.2004.1384212 – start-page: 252 year: 1997 ident: ref46 publication-title: Fuzzy Model Identification Selected Approaches – ident: ref20 doi: 10.1109/21.256541 – year: 2000 ident: ref9 publication-title: An Introduction to Support Vector Machines – year: 0 ident: ref11 publication-title: DENFIS from ECOS Toolbox for Matlab – year: 0 ident: ref5 article-title: fuzzy functions approximation with support vector machines for fuzzy system modeling publication-title: Inf Sci – year: 1992 ident: ref25 publication-title: Neural Networks and Fuzzy Systems – ident: ref18 doi: 10.1016/S0888-613X(02)00078-6 – start-page: 1137 year: 1995 ident: ref24 article-title: a study of cross-validation and bootstrap for accuracy estimation and model selection publication-title: Proc 14th Int Joint Conf Artificial Intelligence – ident: ref21 doi: 10.1109/91.995117 – year: 0 ident: ref16 publication-title: Indicator Analysis – ident: ref35 doi: 10.1109/TFUZZ.2003.817853 – year: 1998 ident: ref13 publication-title: Support vector machines for classification and regression – year: 1991 ident: ref48 publication-title: Computer Systems That Learn – ident: ref37 doi: 10.1109/TFUZZ.1993.390281 – ident: ref3 doi: 10.1016/j.ejor.2005.11.017 – year: 0 ident: ref32 publication-title: NIST/SEMATECH e-Handbook of Statistical Methods – start-page: 236 year: 2001 ident: ref22 article-title: a -nearest neighborhood based fuzzy reasoning schema publication-title: Proc 10th IEEE Int Conf Fuzzy Systems – start-page: 136 year: 2007 ident: ref6 article-title: new classifier design with fuzzy functions publication-title: Proc 11th Int Conf Rough Sets – ident: ref26 doi: 10.1109/ICSMC.2005.1571584 – volume: 8 start-page: 137 year: 2006 ident: ref40 article-title: comparison of fuzzy functions with fuzzy rule base approaches publication-title: Int J Fuzzy Syst – ident: ref12 doi: 10.1109/91.705501 – year: 2001 ident: ref7 publication-title: LIBSVM A library for support vector machines – ident: ref17 doi: 10.1109/KES.2000.885783 – ident: ref38 doi: 10.1109/TSMC.1985.6313399 – start-page: 191 year: 2003 ident: ref43 article-title: localmfsm: a new fuzzy system modeling approach using a two-step fuzzy inference mechanism based on local fuzziness level selection publication-title: Proc 8th Int Fuzzy Systems Association World Congr – ident: ref47 doi: 10.1109/72.159070 – ident: ref10 doi: 10.1016/S0165-0114(97)00280-7 – ident: ref30 doi: 10.1016/0165-0114(93)90024-C – ident: ref34 doi: 10.1109/21.376490 – ident: ref8 doi: 10.1016/S0165-0114(96)00384-3 – ident: ref39 doi: 10.1016/j.asoc.2007.12.004 – start-page: 381 year: 2001 ident: ref42 article-title: prediction of stock prices with multidimensional fuzzy structure identification and inference publication-title: Sig Proc – year: 1998 ident: ref45 publication-title: Statistical Learning Theory – ident: ref14 doi: 10.1109/91.236552 – ident: ref28 doi: 10.1016/S0165-0114(00)00052-X – ident: ref4 doi: 10.1109/FUZZY.2006.1682003 – ident: ref2 doi: 10.1016/0098-3004(84)90020-7 – year: 1996 ident: ref27 publication-title: Neural Fuzzy Systems/A Neuro-Fuzzy Synergism to Intelligent Systems – ident: ref15 doi: 10.1016/j.fss.2005.01.012 – ident: ref41 doi: 10.1109/FUZZ.2001.1009015 – ident: ref36 doi: 10.1109/CEC.2005.1554965
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SubjectTerms	Algorithms Clustering Clustering algorithms Computational complexity Engines Fuzzy Fuzzy functions (FFs) Fuzzy logic Fuzzy set theory fuzzy system modeling (FSM) Fuzzy systems improved fuzzy clustering (IFC) Inference algorithms inference mechanisms Input variables Least squares approximation Mathematical analysis Mathematical models Parameter estimation Partitioning algorithms Studies Yield estimation
Title	Enhanced Fuzzy System Models With Improved Fuzzy Clustering Algorithm
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