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  • Research Article
  • Open Access

Validity-Guided Fuzzy Clustering Evaluation for Neural Network-Based Time-Frequency Reassignment

  • 1Email author,
  • 1,
  • 1,
  • 1,
  • 2 and
  • 3
EURASIP Journal on Advances in Signal Processing20102010:636858

https://doi.org/10.1155/2010/636858

  • Received: 1 March 2010
  • Accepted: 15 July 2010
  • Published:

Abstract

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.

Keywords

  • Neural Network
  • Information Technology
  • Quantum Information
  • Processing Technique
  • Hybrid Approach

Publisher note

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Authors’ Affiliations

(1)
Information and Computing Department, Iqra University, Islamabad Campus, Sector H-9, Islamabad, 44000, Pakistan
(2)
Electrical 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

Copyright

© Imran Shafi et al. 2010

This article is published under license to BioMed Central Ltd. 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.

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