Research Article | OPEN ACCESS
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
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,
References
-
Alexander, I.G., 2010. Neural Networks Theory. Springer, New York.
-
Blumberg, N., C. Chuang-Stein and J.M. Heal, 1990. The relationship of blood transfusion, tumor staging and cancerrecurrence. Transfusion, 30(4): 291-4.
CrossRef PMid:2349625
-
Cappuccio, A., F. Castiglione and B. Piccoli, 2007. Determination of the optimal therapeutic protocols in cancer immunotherapy. Math. Biosci., 209(1): 1-13.
CrossRef PMid:17416392
-
David, M.S., 1996. Building Neural Networks. ACM Press, Boston.
-
Eberhard, R.C. and R.W. Dobbins, 1990. Neural Network PC Tools: A Practical Guide. Academic Press, San Diego, pp: 414.
-
Elfelly, E., J.Y. Dieulot, P. Bornne and M. Benrejeb, 2010. A multi-model identification of complex systems based on both neural and fuzzy clustering algorithms. Proceedings of the 9th International Conference on Machine Learning and Applications (ICMLA '10), pp: 93-98.
-
Farrell, J.A. and M.M. Polycarpou, 2006. Adaptive Approximation Based Control: Unifying Neural, Fuzzy and Traditional Adaptive Approximation Approaches. John Wiley and Sons, New York, USA.
CrossRef
-
Fu, Y.Y., C.J. Wu, J.T. Jeng and C.N. Ko, 2009. Identification of MIMO systems using radial basis function networks with hybrid learning algorithm. Appl. Math. Comput., 213(1): 184-196.
CrossRef
-
Gatti, R.R., W.A. Robinson, A.S. Deinard, M. Nesbit, J.J. McCullough M. Ballow, R.A. Good, 1973. Cyclic leukocytosis in chronic myelogenous leukemia: New perspectives on pathogenesis and therapy. Blood, 41(6): 771-782.
PMid:4514501
-
Hirao, Y., E. Okajima, S. Ozono, S. Samma, K. Sasaki and T. Hiramatsu, 1992. A prospective randomized study of prophylaxis of tumor recurrence following transurethral resection of superficial bladder cancer-intravesical thio-TEPA versus oral UFT. Cancer Chem. Pharmacol., 30: S26-30.
CrossRef PMid:1394812
-
Johnson, C., G.K. Venayagamoorthy and P. Mitra, 2009. Comparison of a spiking neural network and an MLP for robust identification of generator dynamics in a multimachine power system. Neural Netw., 22(5-6): 833-841.
CrossRef PMid:19616408
-
Kennedy, B.J., 1970. Cyclic leukocyte oscillations in chronic mylegenous leukemia during hydroxyurea therapy. Blood, 35(6): 751-760.
PMid:5269413
-
Kirschner, D. and J.C. Panetta, 1998. Modeling immunotherapy of tumor-immune interaction. J. Math. Biol., 37: 235-252.
CrossRef PMid:9785481
-
Kosmatopoulos, E.B., M.M. Polycarpou, M.A. Christodoulou and P.A. Ioannou, 1995. High-order neural network structures for identification of dynamical systems. IEEE T. Neural Networ., 6(2): 422-431.
CrossRef PMid:18263324
-
Lendaris, G.G., 2009. Adaptive dynamic programming approach to experience-based systems identification and control. Neural Networks, 22(5-6): 822-832.
CrossRef PMid:19632087
-
Luitel, B. and G.K. Venayagamoorthy, 2010. Particle swarm optimization with quan-tum infusion for system identification. Eng. Appl. Artif. Intell., 23(5): 635-649.
CrossRef
-
Manel, M.R., R.A. Jose Luis, G. Camps-Valls and J. Munoz-Mari, 2006. Support vector machines for nonlinear kernel ARMA system identification. IEEE T. Neural Networ., 17(6): 1617-1622.
CrossRef PMid:17131673
-
Norgaard, M., 2000. Neural Networks for Modelling and Control of Dynamic Systems. Springer, London.
CrossRef
-
Parlos, A.G., S.K. Menon and Amir F. Atiya, 2001. An algorithm approach to adaptive state filtering using recurrent neural network. IEEE T. Neural Networ., 12(6): 1411-1432.
-
Rouss, V. and W. Charon, 2008. Multi-input and multi-output neural model of themechanical nonlinear behaviour of a PEM fuel cell system. J. Power Sourc., 175: 1-17.
CrossRef
-
Rumelhart, D.E. and J.L. McClelland, 1986. Parallel Distributed Processing: Explorations in the Microstructure of Cog-nition. MIT Press, Cambridge, MA, USA, pp: 318-362.
-
Shi, D. and Y. Gao, 2012. A new method for identifying electromagnetic radiation sources using back propagation neural network. IEEE T. Electromagn. C., PP(99): 1-7.
-
Vieira, W.G., V.M.L. Santos, F.R. Carvalho, J.A.F.R. Pereira and A.M.F. Fileti, 2005. Identification and predictive control of a FCC unit using an MIMO neural model. Chem. Eng. Process., 44(8): 8558-68.
CrossRef
-
Yuan, J. and S. Yu, 2012. Privacy preserving back-propagation neural network learning made practical with cloud computing. IEEE T. Parall. Distr., ISSN: 1045-9219.
Competing interests
The authors have no competing interests.
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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.
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The authors have no competing interests.
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