基于模糊神经网络的机动车驾驶教练员职业适宜性研究

提出应用模糊神经网络系统,建构教练员职业适宜性的3个要素(即心理素质、驾驶技能和知识的表达阐述能力)的学习样本,分析"三要素"的8项特征参数指标——场依存性、速度估计能力、交通安全意识、简单反应、选择反应、跟踪能力、行车注意力、表达阐述能力等,使用K-均值法对实验样本进行初始分类,形成标准学习样本,使用该样本对所构建系统进行训练和调试。利用经调试训练后的系统,依据所测教练员的心理、心理参数对其职业适宜性进行评价。试验表明:建立的教练员职业适宜性仿真模型能取得很好的评价效果。     

ABR处理硫酸盐有机废水的BP神经网络建模

通过厌氧折流板反应器(ABR)处理硫酸盐有机废水的实验数据对BP神经网络进行训练,建立了ABR处理硫酸盐有机废水的BPNN模型,通过测试对比,找出了较优训练函数为traingda,较优训练次数为1 900.利用分割连接权值法(PCW)对影响出水SO42-和COD的主要因素进行分析,结果显示进水COD、SO42-、pH、COD/SO42-和HRT对出水SO42-和COD均产生一定影响,其中进水pH对出水SO42-和COD的影响最大,相对重要性(RI)指数分别为30.79％和23.44％;并通过样本试验数据分别建立了对SO42-和COD去除率的限制因子仿真模型,为预测硫酸盐有机废水的厌氧处理过程提供指导.     

响应面法和神经网络优化Acinetobactersp．DNS32发酵基质

为了提高阿特拉津降解菌Acinetobactersp．DNS32的产量，分别采用响应曲面法和基于人工神经网络的遗传算法对阿特拉津降解菌DNS32发酵培养基中3个重要基质成分（玉米粉、豆饼粉、K：HPO。）进行优化研究。响应曲面法确定3种成分的含量为玉米粉39．494g／L，豆饼粉25．638g／L和K。HPO。3．265g／L时，预测发酵活菌最大生物量为7．079×10^8CFU／mL，实测量为7．194×10^8CFU／mL；人工神经网络结合遗传算法优化确定3种主要成分含量为玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L时，预测最大值为7．199×10^8CFU／mL，实测量为7．244×10。CFU／mL；最终确定培养基配方：玉米粉为39．650g／L，豆饼粉为25．500g／L，K2HPO4为2．624g／L，CaCO3为3．000g／L，MgSO4·7H2O和NaCl均为0．200g／L；优化后阿特拉津降解菌DNS32发酵生物量比优化前提高了36．6％。结果表明，在阿特拉津降解菌DNS32发酵培养基组分优化方面，响应面法和基于人工神经网络的遗传算法都是可行的，基于人工神经网络的遗传算法具有更好的拟合度和预测准确度。     

基于粗糙集-神经网络的矿山地质环境影响评价模型及应用

采用衡山白果地区石膏矿山的11个评价指标,综合运用粗糙集和神经网络理论,构建了基于粗糙集-神经网络(RS-ANN)的矿山地质环境影响评价模型,对RSES软件约简的数据和无约简的数据采用EasyNN-plus软件进行预测评价。神经网络模型的输入属性为8个,而粗糙集-神经网络模型的输入属性为6个,训练样本均为13个,预测样本均为4个,前者的平均预测精度为1.85%～24.86%,后者为1.23%～15.28%。研究发现,粗糙集在保留关键信息的前提下可有效地对数据表进行约简,约简后的神经网络预测结果与实际情况吻合,并比无约简时总体精度有较大幅度提高。     

Neural network forecasting of air pollutants hourly concentrations using optimised temporal averages of meteorological variables and pollutant concentrations

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO₂, O₃, CO and PM₁₀). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.     

Choosing a Food Future: Differentiating Among Alternative Food Options

This article examines the diversity of food networks that fit within the alternative food system of the United States. While farmers’ markets, community supported agriculture schemes, and corporate organic food markets all fit within the alternative food system, they differ greatly in the conventions and beliefs that they represent. The alternative food system has divided into two movements: corporate, weak alternative food networks; and local, strong alternative food networks. The weak corporate version focuses on protecting the environment; however, it neglects issues concerning labor standards, animal welfare, rural communities, small-scale farmers, and human health. Local, strong alternative food networks not only assure environmental protection, but they also address the issues that weak alternatives neglect. Using three case studies from the Washington, D.C. metro area, the author explains that strong alternative food networks are better suited to create social and political change because they challenge the foundations of the conventional food system: standardized and generic products, price-based competition, consolidated power, and global scale. To affect true social and political change in the United States, the author recommends supporting strong alternative food networks by creating the requisite cultural and political space for them to succeed.     

Simultaneous determination of three herbicides by differential pulse voltammetry and chemometrics

A novel differential pulse voltammetry method (DPV) was researched and developed for the simultaneous determination of Pendimethalin, Dinoseb and sodium 5-nitroguaiacolate (5NG) with the aid of chemometrics. The voltammograms of these three compounds overlapped significantly, and to facilitate the simultaneous determination of the three analytes, chemometrics methods were applied. These included classical least squares (CLS), principal component regression (PCR), partial least squares (PLS) and radial basis function-artificial neural networks (RBF-ANN). A separately prepared verification data set was used to confirm the calibrations, which were built from the original and first derivative data matrices of the voltammograms. On the basis relative prediction errors and recoveries of the analytes, the RBF-ANN and the DPLS (D – first derivative spectra) models performed best and are particularly recommended for application. The DPLS calibration model was applied satisfactorily for the prediction of the three analytes from market vegetables and lake water samples.     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。