Method for automatic processing of audit content based on bidirectional neural network mapping

Abstract

Currently, the analytical procedures used during the audit are based on data mining techniques. The work solves the problem of increasing the efficiency and effectiveness of analytical audit procedures by automating data comparison by bidirectional neural network mapping. The object of the research is the process of the content auditing of the receipt of raw materials for production and the manufactured products. The aim of the work is to increase the effectiveness and efficiency of audit due to mapping by full (bidirectional) counterpropagating neural network of content of the receipt of raw materials for production and the manufactured products while automating procedures for checking their compliance. The vectors of feature for the objects of the sequences of the receipt of raw materials for production and the manufactured products are generated, which are then used in the proposed method. The created method, in contrast to the traditional one, provides for a batch mode, which allows the method to increase the learning rate by an amount equal to the product of the number of neurons in the hidden layer and the power of the training set, which is critically important in the audit system for the implementation of multivariate intelligent analysis, which involves enumerating various methods of forming subsets analysis. The urgent task of increasing the audit efficiency was solved by automating the mapping of audit indicators by full (bidirectional) counterpropagating neural network. A learning algorithm based on 𝑘-means has been created, intended for implementation on a GPU using CUDA technology, which increases the speed of identifying parameters of a neural network model. The neural network with the proposed training method based on the 𝑘-means rule can be used to intellectualize the DSS audit. The prospects for further research are the application of the proposed method by neural network mapping for a wide class of artificial intelligence tasks, in particular, for creating a method for bidirectional mapping indicators of audit tasks