6.3 基于RBF神经网络的软测量及水华短期预测方法
RBF神经网络[182]软测量的二次变量依据表6-1的检测数据进行主成分分析得到,训练数据采用前面的方法进行插值得到。
表6-1 1号检测点的检测数据
参数
序号TW/℃SD/cmDO/
(mg·L-1)EC/
(S·m-1)pHTP/
(mg·L-1)TN/
(mg·L-1)Chi-a
(mg·L-1)16.2929.712537.50.1152.62535. 827.78710.082457.80.1192.84124.5314.6559.9823980. 1283.4247.2416.2546.861898. 10.1451.90794.4522489.0817580. 1981.626107.4623.8456.711838. 10.1212.50887.2733. 1408.071959.20.1692.50687.2830.9315.4118690.1092. 838175.7924.84011.0617910.30.1192.38496.11022.33911.171888.60. 1082.07589.41113.4329.32328.50.161. 83689.4127.6538.822478.40.1341.90557137.2559.012788.30.2561. 89235. 8146.61208.9232280.0811.91214.515111309.952938.60. 1833.21261616.1809.1224380. 1392.01423. ...... (共628字) [阅读本文]>>