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     Research Journal of Applied Sciences, Engineering and Technology


A Tumor Growth Model with Unmolded Dynamics Based on an Online Feedback Neural Network Model

1ArashPourhashemi, 2Sara Haghighatnia, 3Nafiseh Mollaei, 4Reihaneh Kardehi Moghaddam and 5Hamid-Reza Kobravi
1, 5Department of Medical Engineering
2, 3, 4Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
Research Journal of Applied Sciences, Engineering and Technology  2014  1:169-173
http://dx.doi.org/10.19026/rjaset.7.236  |  © The Author(s) 2014
Received: May 16, 2013  |  Accepted: June 12, 2013  |  Published: January 01, 2014

Abstract

In this study, we identify tumor growth system by an online feedback neural network model based on back-propagation method. The modeling and identification of nonlinear dynamic systems is the process of developing and improving a mathematical representation of a system using experimental data. So, it is a problem of considerable importance through the use of measured experimental data in biomedical modeling. As is obvious, in biomedical researches it is really difficult and in some cases impossible to implement research on real patient or such a system which is not possible to empirical tests. To deal with, we need sometime a model close to real system in order to forecast dynamic systems so as to perform researches on models and design controller for control of system.

Keywords:

Back-propagation, multi-layer perceptron, online feedback neural network, system identification, tumor growth model,


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Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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