Transfer function model parameter recognition method and device based on improved particle swarm algorithm

Abstract

The invention discloses a transfer function model parameter recognition method and device based on an improved particle swarm algorithm. A coevolution idea and a Gaussian disturbance strategy are introduced into a basic particle swarm optimization algorithm, a hybrid algorithm is formed with a bat algorithm under a coevolution framework, and a Gaussian disturbance term is added in the optimizationprocess to form a hybrid coevolution Gaussian particle swarm optimization algorithm; sampling the input and output of the to-be-identified object model and the estimation model, and solving the standard deviation between the actual output value of the system and the output value of the estimation model at the moment k; feeding back the standard deviation to an HCGPSO algorithm to obtain an optimal result of the current parameter; and replacing the original value with the optimal value of the current model parameter, updating the estimation model, and sequentially iterating until the requirement of an output recognition criterion is met, thereby realizing parameter recognition of the transfer function model.

Description

technical field [0001] The invention relates to the technical field of model parameter identification, in particular to a transfer function model parameter identification method and device based on an improved particle swarm algorithm. Background technique [0002] In practical applications, many actual processes and application systems, such as thermal processes, biochemical reaction processes, etc., have time delays, and most of the models are nonlinear, multivariable, strongly coupled, high-order, and large-delay complex system, it is difficult to determine the specific model through experiments, and the universal transfer function model cannot be established uniformly. The establishment of process and system models is the basis for subsequent prediction, control, evaluation and analysis of process and system, and has important practical significance. Therefore, the identification of model parameters for the corresponding process and model has always been one of the focu...

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Problems solved by technology

[0004] The patent application number CN201610919907.7 is a thermal process model parameter identification method using an improved hybrid particle swarm algorithm. This patent application discloses a thermal object model identification method, but there are two problems as follows: 1. The improved particle swarm algorithm adopts artificial immune strategy and simulated annealing idea, and the implementation process is relatively complicated; 2. The object of application is the thermal process model, and the constructed model structure is second-order, which is not applicable to multi-order model structures

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