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A study on the generalized approximation modeling method based on fitting sensitivity for prediction of engine performance. (English) Zbl 1368.93020

Summary: Prediction technology for aeroengine performance is significantly important in operational maintenance and safety engineering. In the prediction of engine performance, to address overfitting and underfitting problems with the approximation modeling technique, we derive a generalized approximation model that could be used to adjust fitting precision. Approximation precision is combined with fitting sensitivity to allow the model to obtain excellent fitting accuracy and generalization performance. Taking the Grey Model (GM) as an example, we discuss the modeling approach of the novel GM based on fitting sensitivity, analyze the setting methods and optimization range of model parameters, and solve the model by using a genetic algorithm. By investigating the effect of every model parameter on the prediction precision in experiments, we summarize the change regularities of the Root-Mean-Square Errors (RMSEs) varying with the model parameters in novel GM. Also, by analyzing the novel ANN and ANN with Bayesian regularization, it is concluded that the generalized approximation model based on fitting sensitivity can achieve a reasonable fitting degree and generalization ability.

MSC:

93A30 Mathematical modelling of systems (MSC2010)
93C95 Application models in control theory
93C41 Control/observation systems with incomplete information
90C59 Approximation methods and heuristics in mathematical programming
93B35 Sensitivity (robustness)
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