Turboshaft engine steady-state model identification method based on PSO-NARX

Abstract

The invention relates to a turboshaft engine steady-state model identification method based on PSO-NARXPSO-NARX-based turboshaft engine steady-state model identification method, and discloses a PSO-NARX network-based turboshaft engine steady-state model identification method. The invention discloses a PSO-NARX network-based turboshaft engine steady-state model identification method. The method ischaracterized in that a particle swarm optimization algorithm (PSO) is used for optimizing characteristic parameters of the NARX network, and a mean square error of model prediction output and targetoutput is used as a fitness function of particles, so that the optimization effect on the NARX network is improved. A PSO-NARX network is applied to identification of a steady-state model of a certaintype of turboshaft engine; c. Compared with a back propagation (BP) neural network and an NARX network, the method has the advantages that the precision of the steady-state model of the turboshaft engine identified by the PSO-NARX network is higher, the precision requirement of practical application can be met, and a better convergence effect is shown. According to the method, tThe problems thata turboshaft engine steady-state building model is complex, difficult and not high in precision can be well solved.

Description

technical field [0001] The invention relates to a PSO-NARX-based steady-state model identification method of a turboshaft engine, which belongs to the field of engine modeling and regression identification. Background technique [0002] At present, there are two main methods for establishing turboshaft engine models: one is the analytic method for analyzing the aerodynamic characteristics and mechanical relations of components, and the other is the system identification method that regards the engine as a "black box". The analytical method first requires sufficient engine component characteristics, but it is difficult to obtain the engine component characteristics, and then establishes a nonlinear equation system, and then obtains the engine performance data through assumptions, approximate processing, and a large number of iterative operations. The process is complex and has certain deviations . The method of system identification does not need to understand the complex ch...

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Problems solved by technology

[0004] In order to solve the problem of difficult, complex and low-precision establishment of a turboshaft engine steady-state model, the present invention discloses a turboshaft engine steady-state model identification method based on a PSO-NARX network, which is characterized in that it includes the following steps:

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