NEURAL NETWORK SUPPORT FOR AUTOMATION OF OBJECT CONTROL WITH MULTIFACTORIAL INPUT UNDER CONDITIONS OF UNCERTAINTY

Abstract

High dimension of the factors, their noisiness, subjectivity of the human factor breed high uncertainty in the implementation of effective monitoring and productive management in the areas of production and consumption, and hinder the optimal decisions making. In these conditions, it is appropriate to apply intelligent data analysis procedures based on artificial neural networks. Purpose of the study is to substantiate the technology for constructing of effective neural network models for automatic assessment of the object states and their control by finding the optimal values of input factors based on the analysis of the initial set of retrospective data. The desired product is set of neural network models for simultaneous assessment of the current object states and the calculation of the input factors values that ensure the achievement of the required objective function indicators. To automate the processes of recognizing the states of the study object and adapting the factors that bring the current state to the target one, a functional dependence of the states and factors is found based on forced learning of a synthesized models ensemble. The proposed technology and technical tools make it possible to automate the processes of classifying the states of the study objects, adapt the input factors to the target states, and evaluate the quality by model testing. The practical significance of the study results is in the creation of a universal toolkit for a whole class of objects in the tasks of automatic state classification and search for input factors space that is adequate to the target state space. Functionally, the ensemble of trained models can be implemented as a data analysis software unit in the format of two subsystems: for recognizing the states of the study object and adapting input factors to target states. Automation of the state classifying tasks and adapting the input set in the proposed technology that is performed on the basis of standard technical data analysis packages, makes it possible to increase the efficiency of decision-making and reduce financial costs in the implementation of industrial and commercial projects.