Application of an improved adaptive chaos prediction model in aero-engine performance parameters

Based on the research of complexity and non-linearity of aero-engine exhaust gas temperature (EGT) system, a regularization adaptive chaotic prediction model applied in short time forecasting of EGT was proposed. In this research, we develop a new hybrid particle swarm optimization (HPSO) arithmetic in order to improve the accuracy of the forecasting model. This arithmetic enhanced the ability of dealing with integer variables and constraints by adding and changing some manipulations to fit in with optimizing continuous and integer variables. The test results are based on QAR data supplied by a civil airline company, and show that the proposed framework performs better than the traditional chaotic forecasting model on prediction accuracy. Therefore, this arithmetic is efficient and feasible for a short-term prediction of aero-engine exhaust gas temperature. Key–Words: Exhaust gas temperature (EGT); Regularization; Adaptive chaos prediction ; Hybrid particle swarm optimization(HPSO); Principal component regression(PCR);Aero-engine.