Identification of hydraulic resistance parameters in hydraulic network model

Abstract

The main purpose of the work is to develop and analyze new identification algorithm for multivariate multi-connected implicit system. Computational experiments and nonparametric statistics are implemented. The result of the work is a qualitatively new identification and output forecasting algorithm for multivariate systems. Based on the proposed algorithm a new class of generalized multivariate implicit nonparametric models is suggested. The significance of the research is subject to the fact that the majority of complex technical objects is usually described by large-scale nonlinear systems of equations up to input and output variables. Even if random effects in such models could be neglected a procedure of numerical solving the equation systems were either unstable or it would last too long to be implemented in forecasting of continuous process in the controlled object. Any delay in model calculation can also impact negatively on real-time control procedure. Using the proposed approach one can substitute solving the equation by estimation of the solution based on sample including measurements of input and output variables. Moreover, compared to counterparts, using the proposed approach it is possible to develop a single model for a range of input effects as it is presented in the research. Modelling and identification of multivariate systems is one of the most urgent tasks. Nowadays the development modelling methods does not keep up with the continuous and unlimited increase in the volume of information. To solve this problem, the authors proposed a new approach to constructing models of multidimensional systems. The scientific novelty of the article is as follows. A new modification of the nonparametric algorithm for identification of multidimensional systems has been proposed and tested. It makes possible to improve the accuracy and speed of computations.