A hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral

  • Authors:

  • Olivia Mendoza;Patricia Melin;Guillermo Licea

  • Affiliations:

  • Division of Research and Graduate Studies, Universidad Autonoma de Baja California, Tijuana, Mexico;Department of Research and Graduate Studies, Tijuana Institute of Technology, P.O. Box 4207, Chula Vista, CA 91909, United States;Division of Research and Graduate Studies, Universidad Autonoma de Baja California, Tijuana, Mexico

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2009

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Abstract

In this paper, a hybrid approach for image recognition combining type-2 fuzzy logic, modular neural networks and the Sugeno integral is described. Interval type-2 fuzzy inference systems are used to perform edge detection and to calculate fuzzy densities for the decision process. A type-2 fuzzy system is used for edge detection, which is a pre-processing applied to the training data for better use in the neural networks. Another type-2 fuzzy system calculates the fuzzy densities necessary for the Sugeno integral, which is used to integrate results of the neural network modules. In this case, fuzzy logic is shown to be a good methodology to improve the results of a neural system facilitating the representation of the human perception. A comparative study is also made to verify that the proposed approach is better than existing approaches and improves the performance over type-1 fuzzy logic.