ANN-based software analyzer design and implementation in processes of microbial ecology

Present work is oriented to study of processes of microbial ecology by developing and implementing artificial neural networks (ANN)-based software analyzers. Microbial ecology investigates the function of microorganisms in the environment. Towards understanding the role of microorganisms in important ecosystem processes, one example is considered. The presented example is focused on continuous yeasts growth prediction by the developed software analyzer, which helps to discover the influence of initial formaldehyde concentrations on the continuous cell growth, correlated with the protein-synthesizing ability (A,[h(-1)]) of yeasts strain Candida didensii 74-10 at two different dilution rates (D,[h(-1)]). The Levenberg-Marquardt optimization technique has been used to upgrade the network by minimizing the sum square error (SSE). The performance of the network with two hidden layers for predicting cell mass and protein synthesizing ability is found to be very effective.