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 Validity-Guided Fuzzy Clustering Evaluation for Neural Network-Based Time-Frequency Reassignment | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2010 Article ID 636858 14 pages doi 2010 636858 Research Article Validity-Guided Fuzzy Clustering Evaluation for Neural Network-Based Time-Frequency Reassignment Imran Shafi 1 Jamil Ahmad 1 Syed Ismail Shah 1 Ataul Aziz Ikram 1 Adnan Ahmad Khan 2 and Sajid Bashir3 1 Information and Computing Department Iqra University Islamabad Campus Sector H-9 Islamabad 44000 Pakistan 2Electrical Engineering Department College of Telecommunication Engineering National University of Sciences and Technology Islamabad 44000 Pakistan 3 Computer Engineering Department Centre for Advanced Studies in Engineering Islamabad 44000 Pakistan Correspondence should be addressed to Imran Shafi Received 1 March 2010 Revised 21 May 2010 Accepted 15 July 2010 Academic Editor Srdjan Stankovic Copyright 2010 Imran Shafi et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. This paper describes the validity-guided fuzzy clustering evaluation for optimal training of localized neural networks LNNs used for reassigning time-frequency representations TFRs . Our experiments show that the validity-guided fuzzy approach ameliorates the difficulty of choosing correct number of clusters and in conjunction with neural network-based processing technique utilizing a hybrid approach can effectively reduce the blur in the spectrograms. In the course of every partitioning problem the number of subsets must be given before the calculation but it is rarely known apriori in this case it must be searched also with using validity measures. Experimental results demonstrate the effectiveness of the approach. 1. Introduction Clustering is important for pattern recognition classification model reduction and optimization. Cluster analysis plays a .
