Deep learning classifier based on nefclass neural network

Authors

  • O. I. Chumachenko National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

DOI:

https://doi.org/10.18372/1990-5548.49.11242

Keywords:

Fuzzy classifiers, deep learning, NEFCLASS neural network Restricted Boltzman Machine

Abstract

It is proposed a new class of fuzzy classifiers. It is a deep learning classifier based onNEFCLASS neural network. The pre-learning is supplied with help of Restricted Boltzman Machine

Author Biography

O. I. Chumachenko, National Technical University of Ukraine “Ihor Sikorsky Kyiv Polytechnic Institute”

Candidate of engineering. Associate Professor. Technical Cybernetic Department

References

D. Nauck, U. Nauck, and R. Kruse, “Generating classification rules with the neurofuzzy system NEFCLASS.” Biennial Conference of the North American, Fuzzy Information Processing Society, 1996, pp. 466–470.

Y. Bengio, “Learning deep architectures for AI.” Foundations and trends® in Machine Learning, 2(1), pp. 1–127, 2009.

Y. Bengio, Deep learning of representations: Looking forward, Statistical Language and Speech Processing. Springer Berlin Heidelberg, 2013, pp. 1–37.

J. Schmidhuber, “Deep learning in neural networks: An overview.” Neural Networks, 61, pp. 85–117. 2015.

L. A. Zadeh, "Fuzzy algorithms." Information and Control. 12 (2), pp. 94–102, 1968.

G. Hinton, A Practical Guide to Training Restricted Boltzmann Machines, 2010, pp. 3–17.

A. Fischer, and C. Igel, An Introduction to Restricted Boltzmann Machines, 2011, pp. 1–23.

M. Nielsen, (viewed on September 20, 2016). Using neural nets to recognize handwritten digits [Electronic resourse] – Electronic data.– Mode of access: http://neuralnetworksanddeeplearning.com/chap1.html

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Issue

Vol. 3 No. 49 (2016)

Section

COMPUTER-AIDED DESIGN SYSTEMS

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