Neural Network Modeling Method of Transformations Data of Audit Production with Returnable Waste

Abstract

Currently, the analytical procedures used during the audit are based on data mining techniques. The object of the research is the process of the content auditing of the production with returnable waste and intermediate products. The aim of the work is to reduce the risk of incorrect display of the dataset in the DSS of the audit of the method of neural network modeling of transformations of audit data of production with recyclable waste and intermediates. This will reduce the risk of the validated data misclassification. Audit data set transformations of a prerequisite "Completeness" are presented the sequences of sets data mappings of consecutive operations. Reached further development a method of parametrical identification of the MRMLP model which considers number of iterations of training and combines Gaussian distributions and Cauchy that increases the forecast accuracy as on initial iterations all search space is investigated, and on final iterations the search becomes directed. The software implementing the offered methods in MATLAB package was developed and investigated on the data of the release of raw materials into production and the posting of finished products of a with a two-year depth of sampling with daily time intervals. The made experiments confirmed operability of the developed software and allow to recommend it for use in practice in a subsystem of the automated analysis of DSS of audit for check of maps of sets of data of the raw materials release into production and the products output.