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

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.

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Correspondence to Imran Shafi.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Shafi, I., Ahmad, J., Shah, S. et al. Validity-Guided Fuzzy Clustering Evaluation for Neural Network-Based Time-Frequency Reassignment. EURASIP J. Adv. Signal Process. 2010, 636858 (2010). https://doi.org/10.1155/2010/636858

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Keywords

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