基于BP-ANN的光伏发电功率超短期预测方法

针对光伏发电的间歇性和波动性问题,采用基于BP-ANN建立光伏发电系统输出功率超短期预测模型,利用输出功率的历史值、过往及预测日气象信息,对输出功率进行预测,并提出适用的预测流程及预测误差评估方法。实际应用以及与实时监测数据对比,表明该方法方法误差较小,合格率较高,能够满足应用的要求。     

Comparative analyses of apple aroma by a tin-oxide gas sensor array device and GC/MS

The apple cultivars “fuji”, “jina” and “huaniu” aroma volatiles were collected and analyzed using a tin-oxide gas sensor array device and the gas chromatography combined with mass spectrometry (GC-MS). Twenty two of the most abundant volatile compounds were taken into account for further study. Eight compounds were found in every cultivar. The principal components analysis (PCA), partial least squares (PLS) and back-propagation feed-forward artificial neural network (BP-ANN) were used to analyze the sensor array and SPME-GC-MS measurements. From the plots of the first two PCs by PCA, different apple cultivars could be clearly distinguished by SPME-GC-MS measurements, while there was slight overlap by sensor array measurements. BP-ANN was used to distinguish different cultivars based on gas sensor array responses, and the accuracy was 87%. Due to the composition of gas sensors in the array, results of PLS models showed that the correlation between fourteen gas sensor array responses and the two PCs of twenty-two compounds were better than the correlation between those and each volatile compound. Furthermore, an ANN was used to build the relationship between the two predicted PCs by PLS model and the three cultivars. The recognition probability was increased to 97%.     

基于人工神经网络的大坝变形分析与预报研究

本文在分析大坝位移影响因子的基础上,给出了基于BP-ANN(人工神经网络)分析大坝变形时的七种输入模式。探讨了在建立BP-ANN模型时几个关键问题的处理方法。结合某一混凝土拱坝的垂线观测数据,应用BP-ANN的不同输入模式,对大坝的变形值进行了预报和分析,得出了一些有益的结论。     

西藏拉萨河径流预测方法研究

本文介绍了人工神经网络模型、分期平稳自回归模型、一阶季节性自回归模型这三种径流预测模型的基本原理,并且利用西藏拉萨河拉萨站1956至1968年,1973年至2000年41年的月平均流量资料对月径流进行预测和比较,得出BP-人工神经网络模型是相对于其它两种方法更适合对拉萨河径流进行预测的方法。     

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。     

基于可见-近红外光谱技术与BP-ANN算法的污水类型鉴定

提出了一种基于可见-近红外光谱技术与BP人工神经网络(BP-ANN)算法快速进行污水类型鉴定的新方法.以FieldSpec(R)3地物光谱仪采集了4种污水样品的光谱数据,共168份,随机将其分成校正集(132份)和检验集(36份).分别采取全波段(400～2450 nm)与择取波段(400～1800 nm)两种方法建立模型进行分析.光谱经S.Golay平滑和标准归一化(SNV)处理后,以主成分分析法(PCA)降维.将降维所得的前9个主成分数据作为BP-ANN的输入变量,污水类型作为输出变量,建立3层BP-ANN鉴别模型.利用36个未知样对模型进行检验.结果表明:两类模型预测准确率均高达100%,且择取波段模型比全波段模型具有更高的预测精度.说明利用可见-近红外技术结合BP-ANN算法进行污水类型的快速、无污染鉴定是可行的,且波段筛选是优化模型的有效方法之一.     

香油精电子鼻快速检测系统的设计

为实现香油精的快速检测,研制了一套能够实时、准确地进行香油精检测的系统。该系统主要由数据采集部分和数据处理部分组成。数据采集部分采用了气体传感器阵列和上位机结合的方式,数据处理部分用Matlab训练了一个3层BP神经网络。在实验过程中,配制了8个不同比例的香油精样本,用标准样本进行了神经网络的训练,测试样本进行了验证。该系统为香油精的快速检测方法的研究提供了依据,据有一定的实用价值。     

Near-Infrared Spectroscopy for Classification of Oranges and Prediction of the Sugar Content

A nondestructive method for the classification of orange samples according to their growing conditions and geographic areas was developed using Vis/Near infrared spectroscopy. The results showed that the NIR spectra of the samples were moderately clustered in the principle component space and pattern recognition wavelet transform (WT) combined artificial neural network (BP-ANN) provided satisfactory classification results. Additionally, a partial least square (PLS) method was constructed to predict the sugar content of certain oranges. It showed excellent predictions of the sugar content of oranges, with standard error of prediction (SEP) values of 0.290 and 0.301 for Shatangju and Huangyanbendizao, respectively.     

土壤锰污染的高光谱分级评价

提出一种利用高光谱技术进行土壤锰污染分级评价的方法。以FieldSpec3地物光谱仪采集矿区土壤光谱反射率150份,随机分成校正集(115份)和检验集(35份)。光谱经小波去噪和多元散射校正(MSC)处理后,以主成分分析法(PCA)降维。将降维所得的前5个主成分数据为输入变量,分别采用Fisher线性判别、Byes逐步判别、模糊模式识别以及BP-ANN判别四种方法建立了土壤锰污染分级评价模型,并利用35个未知样对模型进行检验。结果表明:Fisher线性判别与模糊模式识别预测准确率为80%,Byes逐步判别为82.86%,BP-ANN模型预测精度最高,达85.71%。说明以高光谱技术进行土壤锰污染分级评价是可行,且BP-ANN是建模的优选算法。     

基于BP-ANN优化提取HPLC特征图谱测定3种黄精辐照前后黄酮含量变化

目的:设计反向传播人工神经网络(BP-ANN)优化鸡头黄精、竹节黄精和马鞭黄精中7 种黄酮的得率,明确各黄精电子束辐照前后黄酮的含量变化,建立指纹图谱.方法:检测方法使用高效液相色谱法(HPLC),采用Inertsil ODS-3,C18 色谱柱(250 mm×4.6 mm,5 μm),流动相0.02％磷酸溶液(A)-...