Optimization of Neural Networks by SIMPLEX Method Performed on Environmental Data

The aim of the optimization is to find out the optimal parameters for complex system such as synthesis of the compounds, chemical reactions, analytical methods, property of the products or chemical processes. The parameters that we want to determine are the values, which describe the system. The SIMPLEX is one of the most simple and general optimization method. It is used to predict the experiments that in quickest way lead to an optimum. In this work the SIMPLEX method was used to optimize the parameters of the counter-propagation neural network model constructed for the prediction of the ozone concentration as one of the most outstanding air pollution parameters in the Buenos Aires region. The network was trained with the data available for 980 collected samples; each of them was described by the concentrations of 7 pollutants: CO, SO2, O3, NOx, NO, NO2, and PM10, and 8 weather related variables: cloudiness, rainfall, insolation factor, temperature, pressure at two locations, and wind intensity with direction. The evaluation function as the optimization criterion of the model was thus the correlation coefficient between the experimental and predicted ozone concentrations.