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Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks for Image Recognition

  • Conference paper
Foundations of Fuzzy Logic and Soft Computing (IFSA 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4529))

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Abstract

The combination of Soft Computing techniques allows the improvement of intelligent systems with different hybrid approaches. In this work we consider two parts of a Modular Neural Network for image recognition, where a Type-2 Fuzzy Inference System (FIS 2) makes a great difference. The first FIS 2 is used for feature extraction in training data, and the second one to find the ideal parameters for the integration method of the modular neural network. Once again Fuzzy Logic is shown to be a tool that can help improve the results of a neural system, when facilitating the representation of the human perception.

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Patricia Melin Oscar Castillo Luis T. Aguilar Janusz Kacprzyk Witold Pedrycz

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© 2007 Springer Berlin Heidelberg

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Mendoza, O., Melin, P., Castillo, O., Licea, G. (2007). Type-2 Fuzzy Logic for Improving Training Data and Response Integration in Modular Neural Networks for Image Recognition. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds) Foundations of Fuzzy Logic and Soft Computing. IFSA 2007. Lecture Notes in Computer Science(), vol 4529. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72950-1_60

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  • DOI: https://doi.org/10.1007/978-3-540-72950-1_60

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72917-4

  • Online ISBN: 978-3-540-72950-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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