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

A technology for improving particle swarm and transfer function, applied in the field of model parameter identification, can solve problems such as inapplicability of multi-level model structure and complicated implementation process
CN111428849AInactive Publication Date: 2020-07-17NARI TECH CO LTD +4
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
NARI TECH CO LTD
Publication Date
2020-07-17
Estimated Expiration
Not applicable · inactive patent

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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.
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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...

Claims

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