A Parallel Online Performance Optimization Method for ATR Engines Based on Ensemble Neural Network

To address the issue of lengthy optimization processes for ATR engines using swarm intelligence optimization algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), a novel Ensemble Neural Networks based Parallel Performance Optimization (ENNPPO) methodology is proposed. This strategy amalgamates several small neural networks into a comprehensive larger model for the construction of an advanced onboard ATR engine model, moreover, the independence of each small neural networks allows for the reduction of computational time through parallel processing on GPUs. The method then harnesses the PSO algorithm to refine performance. Numerical simulations carried out with embedded GPU computing chips demonstrate that the average optimization time per test case is 0.28 seconds, with an energy consumption of 1.22 Joules. The proposed method achieves an optimization precision that is 78.40% and 84.30% higher than that of traditional neural network approaches and Linear Parameter-Varying (LPV) model methods, respectively. Furthermore, the computation speed is enhanced by a factor of at least 7.8 times than CPU, directly attributable to the expedited parallel processing enabled by GPUs.