Liu Bo-ping, Qin Hua-jun, Luo Xiang, Cao Shu-wen, Wang Jun-de
College of Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Guang Pu Xue Yu Guang Pu Fen Xi. 2007 Oct;27(10):2005-9.
Partial least squares (PLS) and artificial neural networks (ANN) prediction model for four components of feedstuff has been established with good veracity and recurrence. The spectra put into the model should be processed by second derivative and standard normal variate (SNV). Ten principal components compressed from original data by PLS and two peak values were taken as the inputs of Back-Propagation Network (BP), while four predictive targets as outputs, according to Kolmogorov theorem and experiment, and twenty three nerve cells were taken as hidden nodes. Its training iteration times was supposed to be 10,000. Prediction deciding coefficient of four components by the model are 0.9950, 0.9980, 0.9990 and 0.9670, while the standard deviation of an unknown sample scanned parallelly are 0.02774, 0.04853, 0.03292 and 0.02204.
建立了饲料中四种成分的偏最小二乘法（PLS）和人工神经网络（ANN）预测模型，具有良好的准确性和重现性。输入模型的光谱需经过二阶导数和标准正态变量变换（SNV）处理。根据Kolmogorov定理和实验，由PLS从原始数据中压缩得到的10个主成分和两个峰值作为反向传播网络（BP）的输入，四个预测目标作为输出，隐藏层节点数取23个神经细胞。设定其训练迭代次数为10000次。该模型对四种成分的预测决定系数分别为0.9950、0.9980、0.9990和0.9670，同时平行扫描未知样品的标准差分别为0.02774、0.04853、0.03292和0.02204。
