Biological Inspiration—Theoretical Framework Mitosis Artificial Neural Networks Unsupervised Algorithm


The modified approach to conventional Artificial Neural Networks (ANN) described in this paper represents an essential departure from the conventional techniques of structural analysis. It has four main distinguishing features: 1) it introduces a new simulation algorithm based on the biology; 2) it performs relatively simple arithmetic as massively parallel, during analysis of a structure; 3) it shows that it is possible to use the application of the modified approach to conventional ANN to solve problems of any complexity in the field of structural analysis; 4) the Neural Topologies for Structural Analysis (NTSA) system are recurrent networks and its outputs are connected to its inputs [1] and [2]. In NTSA system the DNA of the neuron mother and daughters would be defined by: 1) the same entry, from the corresponding neuron in the previous layer; 2) the same trend vector; 3) the same transfer function (purelin). The mother’s neuron and her daughter’s neuron differ only in the connection weight and its output signal.

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Mindiola, L. , Freile, G. and Bertiz, C. (2015) Biological Inspiration—Theoretical Framework Mitosis Artificial Neural Networks Unsupervised Algorithm. International Journal of Communications, Network and System Sciences, 8, 374-398. doi: 10.4236/ijcns.2015.89036.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Pinto, L.R. and Zambrano, A.R. (2014) Unsupervised Neural Network Approach to Frame Analysis of Conventional Buildings. Int. J. Communications, Network and Systems Sciences, 7, 203-211.
[2] Rivero-Angeles, F.J., Gomez-Ramirez, E., Gomez-Gonzalez, B. and Garrido, R. (2005) Fault Detection in Shear Buildings Subject to Earthquakes Using a Neural Network. Proceeding of the Eighth International Conference on the Application of Artificial Intelligence to Civil, Structural and Environmental Engineering, Edited by B. H. V. Topping, 107, ISBN 1-905088-03-05.
[3] Pinto, L. (2008) Tesis Doctoral: AETN Analisis de Estructuras Mediante Topología Neuronal.
[4] DARPA (1987-1988) Neural Network Study (U.S.). Published by AFCEA International Press, a Division of the Armed Forces Communications and Electronics Association 4406 Fair Lakes Court Fairfax Virginia 22033-3899 USA.
[5] Beale, M., Hagan, M.T. and Demuth, H.B. (1995) Neural Network Design. Thomson Learning, Boston.
[6] Eaton, L.K. (2001) Hardy Cross and the “Moment Distribution Method”. Nexus Network Journal, 3, 15-24.
[7] Hoffmann, F. Biological Therapies and Cancer. Produced through an educational grant from La Roche Ltd.
[8] Biological Foundations—Neuron Communication.
[9] Kani, G. (1955) Cálculo de Pórticos de Varios Pisos. In: Reverte, S.A., Ed., 1978-1979, Printed in Spain, ISBN-84-291-2051-6, 19-20-21-22.
[10] Boso, D., Lefik, M. and Schnefler, B. (2005) Joint Finite Element: Artificial Neural Network Numerical Analysis of Multilevel Composites Artificial Intelligence to Civil, Structural and Environmental Engineering. Edited by B. H. V. Topping, Civil Comp. Ltd., 101, ISBN 1-905088-03-05.
[11] Lu, Y., Roychowdhury, V. and Vanderberghe, L. (2007) Distributed Parallel Support Vector Machines in Strongly Connected Networks. Neural Networks. A Publication of the IEEE Computational Intelligence Society.
[12] Bebbahani, S. and Nasrabadi, A.M. (2009) Application of Som Neural Network in Clustering.

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