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2019 IEEE International Conference on Information Technologies

New Methods of Training Two-Layer Sigmoidal Neural Networks with Regularization

Vladimir Nikolaevich Krutikov
Guzel Sharipzhanovna Shkaberina
Mikhail Nikolaevich Zhalnin
Lev Alexandrovich Kazakovtsev
Reshetnev University, Krasnoyarsk
Russian Federation
e-mail: levk@bk.ru
Abstract:

In this paper we present an algorithm for training two-layer sigmoidal artificial neural networks (ANN) in the presence of significant interference and low-informative variables. Mixed smooth and non- smooth regularizing functionals are used to suppress the low –informative variables and to control interference. A computational experiment was organized comparing the quality of approximation by ANN models and logistic models with various types of regularization. The proposed algorithm for ANN learning in combination with non-smooth regularization allows us to obtain efficient ANN models for classification problems.

Key words:
ANN
regularization
machine learning