CAJ | 학술논문

为自动准确测定水质pH值，采用大量的具有代表性的pH值检测数据为样本，提出了一种基于模拟退火优化BP神经网络的pH值预测方法。利用模拟退火算法优化BP网络的权值，调整优化样本的选取和隐层神经元数，训练BP神经网络预测模型得到最优解。由测试样本对网络进行了预测试验，并与非线性回归的预测结果进行了对比。结果表明，该方法对水质pH值预测具有较好的非线性拟合能力和更高的预测准确性。
위자동준학측정수질pH치，채용대량적구유대표성적pH치검측수거위양본，제출료일충기우모의퇴화우화BP신경망락적pH치예측방법。이용모의퇴화산법우화BP망락적권치，조정우화양본적선취화은층신경원수，훈련BP신경망락예측모형득도최우해。유측시양본대망락진행료예측시험，병여비선성회귀적예측결과진행료대비。결과표명，해방법대수질pH치예측구유교호적비선성의합능력화경고적예측준학성。
In order to determine accurate pH value of water automatically,sufficient and typical data of pH value measuring tests are collected as samples,and a pH value prediction method of optimized BP neural network based on simulated annealing algorithm is presented. The simulated annealing algorithm is employed to optimize the weights and thresholds of BP neural network,and selection methods of the training samples and the number of the hidden layer nodes are improved,thus yield an optimal solution of BP neural network. The obtained BP neural net-work is tested by samples,and the prediction results are compared with ones given by a nonlinear regression method. Experimental results exhibit that the proposed method provides better fitting ability and higher accuracy for pH value prediction.