| Summary: | The thermal design parameters of space telescopes are mainly optimized through traversal and iterative attempts. These optimization techniques are time consuming, rely heavily on the experience of the engineer, bear a large computational workload, and have difficulty in achieving optimal outcomes. In this paper, we propose a design method (called SMPO) based on an improved back-propagation neural network (called GAALBP) that builds a surrogate model and uses a genetic algorithm to optimize the model parameters. The surrogate model of a space telescope that measures the atmospheric density is established using GAALBP and then compared with surrogate models established using a traditional BP neural network and radial-basis-function neural network. The results show that the regression rate of the surrogate model based on the GAALBP reaches 99.99%, a mean square error of less than 2 × 10<sup>−6</sup>, and a maximum absolute error of less than 4 × 10<sup>−3</sup>. The thermal design parameters of the surrogate model are optimized using a genetic algorithm, and the optimization results are verified in a finite element simulation. Compared with the design results of the manually determined thermal design parameters, the maximum temperature of the CMOS is reduced by 5.33 °C, the minimum temperature is increased by 0.39 °C, and the temperature fluctuation is reduced by a factor of 4. Additionally, SMPO displays versatility and can be used in various complex engineering applications to provide guidance for the better selection of appropriate parameters and optimization.
|
|---|