Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Incremental Local Linear Fuzzy Classifier in Fisher Space | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 360834 9 pages doi 2009 360834 Research Article Incremental Local Linear Fuzzy Classifier in Fisher Space Armin Eftekhari 1 Hamid Abrishami Moghaddam 1 2 Mohamad Forouzanfar 3 and Javad Alirezaie1 4 1 Faculty of Electrical Engineering . Toosi University of Technology P. O. Box 16315-1355 Tehran Iran 2 Unite de Genie Biophysique et Medical Groupe de Recherche sur l Analyse Multimodale de la Fonction Cerebrale GRAMFC Faculte de Medecine 3 rue des Louvels 80036 AMIENS cedex France 3 School of Information Technology and Engineering University of Ottawa 800 King Edward Avenue Ottawa ON Canada K1N 6N5 4 Department of Electrical and Computer Engineering Ryerson University 350 Victoria Street Toronto ON Canada M5B 2K3 Correspondence should be addressed to Hamid Abrishami Moghaddam moghadam@ Received 4 August 2008 Revised 4 March 2009 Accepted 25 March 2009 Recommended by Christophoros Nikou Optimizing the antecedent part of neurofuzzy system is an active research topic for which different approaches have been developed. However current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classifier is then built in the transformed space starting from the simplest form a global linear classifier . If the overall performance of the classifier was not satisfactory it would be iteratively refined by incorporating additional local classifiers. In addition rule consequent parameters are optimized using a local least square approach. Our refinement strategy is .
