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Chemical Technology, Control and Management

Abstract

Methods and simplified computational schemes for optimizing data processing for systems operating in conditions of limited a priori information, changes in the characteristics of external influences, uncertainty of parameters have been developed. To describe a non-stationary object, non-linear identification models are considered, constraints, input conditions for obtaining possible values of output variables are defined. An approach aimed at using identification technologies based on generalization of capabilities of dynamic models, neural networks (NN), mechanisms for regulating variable computing schemes of structural network components, as well as learning algorithms of the NN is proposed. A generalized algorithm for learning NN based on the synthesis of the mechanisms of probabilistic, heuristic and stochastic search, the formation of a set of training data, regulation of the values of variables of linear and nonlinear dependences of inputs and outputs is developed. The efficiency of the implemented data processing software complex is analyzed by various methods, algorithms, speed, graphs and requirements, and also estimates of the stability of the generalized learning algorithm of NN and the minimum mean-square error are obtained.

First Page

54

Last Page

61

DOI

https://doi.org/10.34920/2018.6.54-61

References

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