Application of Self-Organizing map for Time-varying biological to predicting of complexity Systems

© 2020 by IJCTT Journal
Volume-68 Issue-4
Year of Publication : 2020
Authors : Mohammad Almasi, Mahla Alimardani
DOI :  10.14445/22312803/IJCTT-V68I4P111

How to Cite?

Mohammad Almasi, Mahla Alimardani, "Application of Self-Organizing map for Time-varying biological to predicting of complexity Systems," International Journal of Computer Trends and Technology, vol. 68, no. 4, pp. 59-64, 2020. Crossref,

One of the problems in biochemistry is how to estimate the biological system`s behavior changes during the time. The types of recursive identification methods like recursive least square error (RLSE) estimation and Kalman filter is used in previous, the speed of estimation is still a limiting factor in biological systems. It is especially noticeable when the identifier has to estimate the parameters in the situation that it had estimated before but has later lost the estimated values because of the changes in the biological systems behavior. To overcome this problem and speed up the identification process, the multiple model identification using the self-organizing map neural network (MMSOM) has been introduced. In multiple modeling, there is more than one estimated model for biological systems. In each step of time, the best model is selected for the biological systems according to previously defined criteria of identifications which is adaptive during different times.

Biological Prediction . deep learning . Neural Network . SOM

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