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Fuzzy Neural Network Models for Classification
International Journal of Applied Intelligence (2000)
  • Dr. Arun D Kulkarni, University of Texas at Tyler
  • Charles D. Cavanaugh
In this paper, we combine neural networks with fuzzy logic techniques. We propose a fuzzy-neural network model for pattern recognition. The model consists of three layers. The first layer is an input layer. The second layer maps input features to the corresponding fuzzy membership values, and the third layer implements the inference engine. The learning process consists of two phases. During the first phase weights between the last two layers are updated using the gradient descent procedure, and during the second phase membership functions are updated or tuned. As an illustration the model is used to classify samples from a multispectral satellite image, a data set representing fruits, and Iris data set.
  • fuzzy logic,
  • neural networks,
  • decision systems,
  • classification
Publication Date
May, 2000
Citation Information
Kulkarni, A. D., & Cavanaugh, C. D. (2000). Fuzzy neural network models for classification. International Journal of Applied Intelligence, 12, 207–215.