Exploring The Direct Influence Of Control Parameters Of Experimental Facility For Fuel Cells Based On Improved Generalized Regression Neural Network

The exploration of the control parameters for experimental facility in terms of their coupling effects can provide direct guidance for experimental research of fuel cells. In this study, a model based on an improved generalized regression neural network (GRNN) is proposed to study the coupling effect of control parameters that can be set and adjusted directly during the experiment. In addition, the expectation-maximization algorithm and the ensemble filter algorithm are introduced to preprocess data; the adaptive learning factor and the differential evolution strategy are introduced to improve the GRNN. Moreover, a moment-independent global sensitivity analysis is introduced to apply for sensitivity analysis by the dimensional reduction technique and principle of maximum entropy. The results indicated that the improved model can accurately predict performance in a dynamic environment; the relative error was <1.7%, the prediction errors were, respectively, reduced by 35.61%, 19.78%, 60.99%, and 37.55% as compared with the GRNN model, demonstrating the effectiveness of the improvement strategy; the back pressure and inlet temperatures, dew point, and cell temperatures were classified as sensitive factors; when the anodic back pressure was approximately 1 atm, the cathodic back pressure had little influence, the performance conforms to the maximum level.