CAJ | 학술논문

针对BP神经网络模型对3C铜腐蚀速度的预测存在精度低问题，建立了基于遗传算法的3C钢神经网络预测模型。该模型采用遗传算法优化BP神经网络的初始权值和闽值。实例对比分析表明：采用遗传神经网络预测3C钢的腐蚀速度，提高了准确率．具有应用可行性。
침대BP신경망락모형대3C동부식속도적예측존재정도저문제，건립료기우유전산법적3C강신경망락예측모형。해모형채용유전산법우화BP신경망락적초시권치화민치。실례대비분석표명：채용유전신경망락예측3C강적부식속도，제고료준학솔．구유응용가행성。
Because of the low precision of the BP neural network model for the corrosion rate prediction of 3C steeL we make a neural network prediction model for 3C steel based on the genetic algorithm. This model uses the genetic algorithm to optimize the initial weights and thresholds of the BP neural network. Comparative analysis of examples shows that： it can improve accuracy and feasibility using genetic neural network to predict the corrosion rate of 3C steel.