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This article was downloaded by: [Memorial University of Newfoundland] On: 18 July 2014, At: 18:14 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK International Journal of Systems Science Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tsys20 A learning-automaton-based method for fuzzy inference system identification MOHAMED CHTOUROU a , MAHER BEN JEMAA b & RAOUF KETATA c a Laboratoire d'Electrotechnique et d'Automatique (LETAU), Département de Génie Electrique , ENIS, B.P.W , Sfax-Turrisie, 3038 b Département de Génie Electrique , ENIS, B.P.W , Sfax-Turrisie, 3038 c Institut National des Sciences Appliquees et Technologiques de Tunis , Tunisie Published online: 06 Apr 2007. To cite this article: MOHAMED CHTOUROU , MAHER BEN JEMAA & RAOUF KETATA (1997) A learning-automaton-based method for fuzzy inference system identification, International Journal of Systems Science, 28:9, 889-896 To link to this article: http://dx.doi.org/10.1080/00207729708929451 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

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Page 1: A learning-automaton-based method for fuzzy inference system identification

This article was downloaded by: [Memorial University of Newfoundland]On: 18 July 2014, At: 18:14Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: MortimerHouse, 37-41 Mortimer Street, London W1T 3JH, UK

International Journal of Systems SciencePublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tsys20

A learning-automaton-based method for fuzzyinference system identificationMOHAMED CHTOUROU a , MAHER BEN JEMAA b & RAOUF KETATA ca Laboratoire d'Electrotechnique et d'Automatique (LETAU), Département de GénieElectrique , ENIS, B.P.W , Sfax-Turrisie, 3038b Département de Génie Electrique , ENIS, B.P.W , Sfax-Turrisie, 3038c Institut National des Sciences Appliquees et Technologiques de Tunis , TunisiePublished online: 06 Apr 2007.

To cite this article: MOHAMED CHTOUROU , MAHER BEN JEMAA & RAOUF KETATA (1997) A learning-automaton-basedmethod for fuzzy inference system identification, International Journal of Systems Science, 28:9, 889-896

To link to this article: http://dx.doi.org/10.1080/00207729708929451

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose ofthe Content. Any opinions and views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be reliedupon and should be independently verified with primary sources of information. Taylor and Francis shallnot be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and otherliabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to orarising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

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