基于BP-ANN的热电偶信息处理方法

为了提高传感器的准确度,提出一种基于BP网络和递推预报误差算法对热电偶进行信息处理的方法,经过仿真试验证明该方法可以提高传感器在较大范围内的测量准确度。该方法用软件容易实现,可以很方便地应用到其它传感器中。

基于人工神经网络的零件特征加工方法获取及其优先权选择

为了获取零件特征的所有可行加工方法,并定量化描述其选择优先权,提出了一种基于人工神经网络的决策方法。在该决策方法中,基于对问题特性的分析,采用均匀设计法选取有代表性的样本集合;为避免常规反向传播算法训练网络时易陷入局部极小值的缺陷,采用可同时调整动量因子和学习率的改进反向传播算法,并用线性回归分析方法加以测试。实例验证了该方法可以有效地获取零件特征的所有可行加工方法及其选择优先权。

Quantitative determination of n-heptane and n-octane using terahertz time-domain spectroscopy with chemometrics methods

This paper introduces the terahertz time- domain spectroscopy （THz-TDS） used for the quantitative detection of n-heptane volume ratios in 41 n-heptane and n-octane mixtures with the concentration range of 0-100% at the intervals of 2.5%. Among 41 samples, 33 were used for calibration and the remaining 8 tbr validation. Models of chemometrics methods, including partial least squares （PLS） and back propagation-artificial neural network （BP-ANN）, were built between the THz- TDS and the n-heptane percentage. To evaluate the quality of the built models, we calculated the correlation coefficient （R） and root-mean-square errors （RMSE） of calibration and validation models. R and RMSE of two methods were close to 1 and 0 within acceptable levels, respectively, demonstrating that the combination of THz- TDS and chemometrics methods is a potential and promising tool for further quantitative detection of n- alkanes.