Algorithm for estimating the parameters of a discrete-dynamic consumption function using a neural network by MATLAB

The purpose of the work is to ensure continuous monitoring of the characteristics and operational indicators of urban facilities and its transport infrastructure by assessing the parameters that affect not only the level of organization and automation of the processes of documenting current statistical information on consumer expenditures of the population, but also the state of production of necessary consumer goods and transport services as a whole. An algorithm for numerical estimation of the parameters of regression models of consumption functions built on the basis of statistics of socio-economic development of the region is proposed. Its significant difference from the known methods of estimation is the use of neural network technologies, which contribute to a significant expansion of the technical capabilities of modeling and increase the accuracy of calculations by obtaining recurrent estimates of the vector of the desired model coefficients. It is shown that for the considered class of problems of “fitting” the trajectories of the consumption function to statistical data, it is possible to apply neural models of generalized regression networks with simple training modes and high modeling accuracy. The use of neural network technologies provides the maximum approximation of the production function model of a given structure to the neural model to a given initial approximation with its subsequent use for estimating weight coefficients. The method application is demonstrated by estimating the parameters of the discrete-dynamic model of the consumption function according to the corresponding statistical series. Calculations are performed using the functions of the Neural Networks Toolbox of the MATLAB environment. The method is applicable for quantitative estimates of the parameters of production models with complex logical-probabilistic relationships, as well as for obtaining numerical values of target indicators and indicators for assessing the development of inland water transport by statistical series and monitoring.

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