Abstract
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.
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References
J.C. Bezdek, "Computing with uncertainty," IEEE Communications Magazine, vol. 30, pp. 24–36, 1992.
L.A. Zadeh, "Outline of a new approach to analysis of complex systems and decision processes," IEEE Transactions on Systems, Man, and Cybernetics, vol. 3, pp. 28–44, 1973.
L.A. Zadeh, "Fuzzy logic, neural networks, and soft computing," Communications of the ACM, vol. 37, pp. 77–84, 1994.
J.J. Buckley and Y. Hayashi, "Fuzzy neural networks," in Fuzzy Sets, Neural Networks, and Soft Computing, edited by R.R. Yager and A. Zadeh, Van Nostrand: New York, pp. 233–249, 1994.
M.M. Gupta, "Fuzzy neural networks: Theory and applications," Proceedings of SPIE, vol. 2353, pp. 303–325, 1994.
J.S.R. Jang, "ANFIS adaptive network based fuzzy inference systems,"IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, pp. 665–685, 1993.
M.I. Jordan and R.A. Jacobs, "Hierarchical mixture of experts and EM algorithm," Neural Computation, vol. 6, pp. 181–214, 1994.
C.-T. Lin and G.C.S. Lee, "Neural network based fuzzy logic control and decision system," IEEE Transactions on Computers, vol. 40, pp. 1320–1336, 1991.
S. Mitra and S.K. Pal, "Logical operation based fuzzy logicMLP for classification and rule generation," Neural Networks, vol. 7, pp. 353–373, 1994.
S.K. Pal and S. Mitra, "Multi-layer perceptron, fuzzy sets, and classification," IEEE Transactions on Neural Networks, vol. 3, pp. 683–697, 1992.
H.Takagi and I. Hayashi, "NNDriven fuzzy reasoning,"International Journal of Approximate Reasoning, vol. 5, pp. 191–212, 1991.
L.X. Wang and J.M. Mendel, "Fuzzy basis function, universal approximation, and orthogonal least squares learning," IEEE Transaction on Neural Networks, vol. 3, pp. 807–814, 1992.
J.S.R. Jang, C.T. Sun, and E. Miztani, Neuro-Fuzzy and Self-Computing, Prentice Hall: Upper Saddle River, NJ, 1997.
H.R. Berenji and P. Khedkar, "Learning and tuning fuzzy logic controllers through reinforcements,"IEEE Transactions on Neural Networks, vol. 3, pp. 724–740, 1992.
A.D. Kulkarni, "Neural-fuzzy models for multispectral image analysis," International Journal of Applied Intelligence, vol. 8, pp. 173–187, 1998.
A.D. Kulkarni and C.D. Cavanaugh, "Fuzzy neural network models for classification," in Proceedings of the Tenth International Conference on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, Atlanta, GA, 1997, pp. 357–366.
A.D. Kulkarni, Artificial Neural Networks for Image Understanding, Van Nostrand Reinhold: New York, NY, 1994.
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Kulkarni, A.D., Cavanaugh, C.D. Fuzzy Neural Network Models for Classification. Applied Intelligence 12, 207–215 (2000). https://doi.org/10.1023/A:1008367007808
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DOI: https://doi.org/10.1023/A:1008367007808