GMDH and Neural Network Application for Modeling Vital Functions of Green Algae under Toxic Impact. Oleksandra Bulgakova, Volodymyr Stepashko, Tetyana Parshikova, Iryna Novikova

Abstract. This work presents the modeling results of influence of toxic bichromate potassium on the vital functions cells of green algae using intellectual computing - Group method of data handing (MGDH) and neural network (NN) with backpropagation algorithm learning, and also regressive analysis. The results of the laboratory experiments executed at the Kyiv National University.

In all experiments was used the toxic - bichromate potassium with concentration from 0,05 to 135 mg/l. In some experiments this toxic was used with algin acid.

The goal of our work is to get better result of forecasting influence of toxic bichromate potassium on the vital functions cells of green algae and compare results which were got using different methods.

Keywords. Group method of data handling, neural network, backpropagation algorithm, algae, bichromate potassium, forecasting.

References.
1. Buerge J., Hug J. Influence of Mineral Surfaces on Chromium (VI) reduction by Iron (II) // Environ. Sci. Technol. - 1999. - 33. - P. 4285 - 4291.
2. Stepashko V.S. GMDH algorithms - the basic of the automatization process of modeling on experimental data// Automatica, 1998, no.4, pp. 44-55.
3. Robert Callan. Essence of Artificial Intelligence. Prentice Hall (1997).