An Improvement on Deep Time Growing Neural Network on Biological Signals: Review

Abstract

A novel method for learning the cyclic contents of stochastic time series: the deep time-growing neural network (DTGNN). The DTGNN combines supervised and unsupervised methods in different levels of learning for an enhanced performance. It was employed by a multiscale learning structure to classify cyclic time series (CTS), in which the dynamic contents of the time series are preserved in an efficient manner. This paper suggests a systematic procedure for finding the design parameter of the classification method for a one versus-multiple class application. In this paper different authors research papers are reviewed and different problems are stored and now these problems are resolved.