Statistical analysis of the output results of fuel cell prediction model
Due to the randomness,fuel cell lifetime prediction models based on neural network algorithms have uncertainty in their output results,meaning that the output is different with each prediction.To address this issue,a fuel cell lifetime prediction model based on the long short-term memory(LSTM)neural network algorithm is established.This model is run multiple times on the experimental sample data,statistical methods are used to analyze the statistical characteristics of the distribution of output results.It is found that the output results of the LSTM neural network-based lifetime prediction model follow a normal distribution pattern.Based on this conclusion,the average of multiple results can be used as the output of the fuel cell lifetime prediction model,thereby improving the prediction accuracy and stability of the output results.
fuel celllifetime prediction modelnormal distribution testlong short-term memory(LSTM)neural networkstatistical characterization
国家科技期刊平台
NSTL
万方数据
