Volume 7 Issue 2 - July 2016

  • 1. First and second order training algorithms for artificial neural networks to detect the cardiac state

    Authors : Sanjit Dash, G.sasibhushana Rao

    Pages : 530-537

    DOI : http://dx.doi.org/10.21172/1.72.581

    Keywords : Backpropagation algorithm, Line search, conjugate gradient algorithm, ECG arrhythmia

    Abstract :

    In this paper two minimization methods for training feedforward networks with backpropagation are discussed. Feedforward network training is a special case of functional minimization, where no explicit model of the data is assumed. Due to the high dimensionality of the data, linearization of the training problem using orthogonal basis function is one of the options. The focus is functional minimization on different basis. Two different methods, one based on local gradient and the other on Hessian matrix are discussed. MIT-BIH arrhythmia datasets are used to detect six different beats to know the cardiac state. The beats are Normal (N) beat, Left Bundle Branch Block (L) Beat, Right Bundle Branch Block(R) Beat, premature ventricular contraction (V) beat, paced (PA) beat and fusion of paced and normal (f) beat.

    Citing this Journal Article :

    Sanjit Dash, G.sasibhushana Rao, "First and second order training algorithms for artificial neural networks to detect the cardiac state", https://www.ijltet.org/journal_details.php?id=906&j_id=3287, Volume 7 Issue 2 - July 2016, 530-537, #ijltetorg