Learning Algorithm for Fuzzy Perceptron with Max-Product Composition

Xin Yu; Mian Xie; Li Xia Tang; Chen Yu Li

doi:10.4028/www.scientific.net/AMM.687-691.1359

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 687-691Learning Algorithm for Fuzzy Perceptron with...

Learning Algorithm for Fuzzy Perceptron with Max-Product Composition

Abstract:

Fuzzy neural networks is a powerful computational model, which integrates fuzzy systems with neural networks, and fuzzy perceptron is a kind of this neural networks. In this paper, a learning algorithm is proposed for a fuzzy perceptron with max-product composition, and the topological structure of this fuzzy perceptron is the same as conventional linear perceptrons. The inner operations involved in the working process of this fuzzy perceptron are based on the max-product logical operations rather than conventional multiplication and summation etc. To illustrate the finite convergence of proposed algorithm, some numerical experiments are provided.

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Periodical:

Applied Mechanics and Materials (Volumes 687-691)

Pages:

1359-1362

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.687-691.1359

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Online since:

November 2014

Authors:

Xin Yu*, Mian Xie, Li Xia Tang, Chen Yu Li

Keywords:

Finite Convergence, Fuzzy Perceptron, Learning Algorithm, Max-Product Composition

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References

[1] Y. Hayashi, J. J. Buckley, E. Czogala.: Fuzzy neural networks with fuzzy signals and weights. In: IEEE/INNS, IJCNN, Baltimore, Maryland, 2, 1992, pp.696-701.

DOI: 10.1109/ijcnn.1992.226906

Google Scholar

[2] P. RoyChowdhury, K. K. Shukla.: Incorparating fuzzy concepts along with dynamic tunneling for fast and robust training of multilayer perceptrons. Neurocomputing, 50, 2003, 319-340.

DOI: 10.1016/s0925-2312(02)00570-2

Google Scholar

[3] M. Pandit, L. Srivastava, J. Sharma.: Voltage contingency ranking using fuzzified multilayer perceptron. Electric Power Systems Research, 59, 2001, pp.65-73.

DOI: 10.1016/s0378-7796(01)00142-0

Google Scholar

[4] J. L. Chen, J. Y. Chang.: Fuzzy perceptron learning and its application to classifiers with numerical data and linguistic knowledge. IEEE Transactions on Fuzzy Systems, 8(6), 2000, pp.730-745.

DOI: 10.1109/91.890331

Google Scholar

[5] J. Yang, W. Wu.: Is bias dispensable for fuzzy neural networks. Fuzzy Sets and Systems, 158(24), 2007, pp.2757-2762.

DOI: 10.1016/j.fss.2007.07.013

Google Scholar

[6] M. Bourke, D. Fisher: A predictive fuzzy relational controller. In: Proceedings of the Fifth International Conference on Fuzzy Systems, New Orleans, LA, 1996, pp.1464-1470.

Google Scholar

[7] K. B. Roelof.: A proposed max-product threshold unit for classification of pattern vectors. International Journal of Neural Systems, 11(3), 2001, pp.271-279.

DOI: 10.1142/s0129065701000710

Google Scholar