Development of a decision support system based on neural networks and a genetic algorithm

We examine different approaches to determining the dependence of a target variable on input data and review the most common statistical forecasting methods. The advantages of using neural networks for this purpose are described. We suggest applying interval neural networks for calculations with underdetermined (interval) data, which makes it possible to use our DSS in a wide range of complicated tasks. We developed a corresponding learning algorithm for the interval neural networks. The advantages of using a genetic algorithm (GA) to select the most significant inputs are shown. We justify the use of general-purpose computing on graphics processing units (GPGPU) to achieve high-speed calculations with the decision support system in question. A functional diagram of the system is presented and described. The results and samples of the DSS application are demonstrated.