Application of neural network model for parameters identification of non-linear dynamic system

Title:

Application of neural network model for parameters identification of non-linear dynamic system

Creator:

Balara, D., Timko, J., and Žilková , J.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:a5be334b-a318-4bad-bd42-1f12082ed6f1
uuid:a5be334b-a318-4bad-bd42-1f12082ed6f1
doi:10.14311/NNW.2013.23.008

Subject:

Neural networks, non-linear dynamic systems, induction machines, and ientification

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

A method for identification of parameters of a non-linear dynamic system, such as an induction motor with saturation effect taken into account, is presented in this paper. Adaptive identifier with structure similar to model of the system performs identification. This identifier can be regarded as a special neural network, therefore its adaptation is based on the gradient descent method and Back-Propagation well known in the neural networks theory. Parameters of electromagnetic subsystems were derived from the values of synaptic weights of the estimator after its adaptation. Testing was performed with simulations taking into account noise in measured quantities. Deviations of identified parameters in case of electrical parameters of the system were up to 1% of real values. Parameters of non-linear magnetizing curve were identified with deviations up to 6% of real values. Identifier was able to follow sudden changes of rotor resistance, load torque and moment of inertia.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2013 Volume:23 | Number:2

Harvested from:

CDK

Metadata only:

false