STRUCTURAL-PARAMETRIC SYNTHESIS OF THE FEEDFORWARD NEURAL NETWORKS WITH SIGMOID PIECEWISE-TYPE NEURONS

M. Z. Zgurovsky, O. I. Chumachenko, V. S. Gorbatiuk

Abstract


The method of structural and parametric synthesis of feedforward neural networks is considered, which includes Sigmoid Piecewise neurons, used in the process of a predictive model construction. The article describes the principles of a new developed Sigmoid Piecewise neuron constructing. It is proposed the method of structural and parametric synthesis with a single layer of  Sigmoid Piecewise neurons.The training algorithm of proposed neural networks is developed. The results of the effectiveness research of Sigmoid Piecewise neurons on real samples are presented.

Keywords


Feedforward neural networks; time series; sigmoid piecewise; training algorithm

References


Jürgen Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks, vol. 61, pp. 85–117, January 2015. doi.org/10.1016/j.neunet.2014.09.003

Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.

Olivier Delalleau, and Yoshua Bengio, “Shallow vs. deep sum-product networks,” Advances in Neural Information Processing Systems. 2011, pp. 666–674.

Stephen Boyd, and Lieven Vandenberghe, Convex optimization. Cambridge university press. 2004.

Martin Riedmiller, and Heinrich Braun, “A direct adaptive method for faster backpropagation learning: The RPROP algorithm,” IEEE International Conference on Neural Networks. 1993, pp. 586–591.

Hugo Larochelle, Yoshua Bengio, Jérôme Louradour, and Pascal Lamblin, “Exploring strategies for training deep neural networks,” Journal of machine learning research, 10, no., pp.1–40, Jan. 2009.

Donald F. Specht, “A general regression neural network,” IEEE transactions on neural networks 2, no. 6, 1991, pp. 568–576.

Sung-Kwun Oh, and Witold Pedrycz, “The design of self-organizing polynomial neural networks,” Information Sciences 141, no. 3–4, pp. 237–258, 2002.


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