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Evolutionary fuzzy clustering and functional modular neural network-based human recognition

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Abstract

Computational intelligence shows its ability for solving many real-world problems efficiently. Synergism of fuzzy logic, evolutionary computation, and neural network can lead to development of a computational efficient and performance-rich system. In this paper, we propose a new approach for solving the human recognition problem that is the fusion of evolutionary fuzzy clustering and functional modular neural networks (FMNN). Evolutionary searching technique is applied for finding the optimal number of clusters that are generated through fuzzy clustering. The functional modular neural network has been used for recognition process that is evaluated with the help of integration based on combining the outcomes of FMNN. Performance of the proposed technique has been empirically evaluated and analyzed with the help of different parameters.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Harcourt Butler Technological Institute, Kanpur, India

    Vivek Srivastava, Bipin K. Tripathi & Vinay K. Pathak

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  1. Vivek Srivastava
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  2. Bipin K. Tripathi
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  3. Vinay K. Pathak
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Correspondence to Vivek Srivastava.

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Srivastava, V., Tripathi, B.K. & Pathak, V.K. Evolutionary fuzzy clustering and functional modular neural network-based human recognition. Neural Comput & Applic 22 (Suppl 1), 411–419 (2013). https://doi.org/10.1007/s00521-012-0973-7

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  • DOI: https://doi.org/10.1007/s00521-012-0973-7

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