INFORMATION AND ANALYTICAL MODEL OF PRODUCT TESTING FOR COUNTERFEIT
The article considers a problem of counterfeit products and the existing methods for solving this problem. An information and analytical method for the optimization of an expert product evaluation is proposed, which uses a parameter set and allows to automate the final decision-making process. The set of parameters takes into account not only indicators for suppliers, but also possible consequences of using counterfeit products for different groups of consumers. The method is based on the application of a neural network using a certain base of expert knowledge. Network training is provided during analyzing process with the possibility of correction by an expert. Modeling of the analysis process with different sets of input parameters and various parameters of the neural network was carried out. The results are stored in the database; the neural network performance is evaluated based on these results. The database structure, input parameters structure and the visualization of experimental results are presented.