西安f7地裂缝发展变化的预测研究

论文首先分析和总结了影响西安地裂缝发展变化的主要人为因素。随后,利用三维非稳定渗流分析方法研究了人类活动对地下水位的影响;根据西安市新增固定资产投资的变化,预测了地面荷载的变化;以时间、地下水位和地面荷载为基本变量,建立了预测f 7地裂缝活动趋势的BP神经网络模型;最后,借助MATLAB语言进行编程,利用训练稳定的网络模型,预测了f 7地裂缝的活动趋势以及年平均的垂直位移沉降量,为进一步研究西安地铁2号线安全运行和防护提供参考依据。     

边坡变形时序非线性判定及混沌预测研究

以探讨边坡变形性质及混沌预测可行性为目的,基于混沌理论利用相空间重构技术对其变形时间序列进行混沌特征判定,试验显示变形系统具有混沌特性,可用混沌相关理论进行研究;基于混沌相空间重构技术,笔者构建了多种混沌预测模型进行混沌预计研究,分析各类模型的工程实际应用效果;针对单次监测时序预测精度较低的问题,提出累加时序预测方案,训练结果显示,短期预测精度变形累计值基本控制在5%以内,高程值预测相对误差均低于1%,预测精度较高,可以用于工程实际。     

B—P网络逐步聚类法在环境测点优选中的应用

将Ｂ－Ｐ网络原理与逐步聚类分析思想相结合，用于环境测点聚类优选。该方法用于水清河几个监测断面的优选结果是符合客观实际的。     

基于BP神经网络的苯储罐泄漏事故风险评价模型研究

为了在事故发生之前对苯储罐进行风险评价，提出1种基于BP神经网络的泄漏事故风险评价方法，利用该方法构建了苯储罐的风险评价模型，并对模型进行了训练及验证。研究结果表明:BP神经网络成功完成了建模任务，且模型训练结果较好，可利用基于BP神经网络所构建的苯泄漏事故风险评价模型对苯储罐发生泄漏事故的风险进行评价。     

基于BP神经网络的垃圾焚烧过程故障诊断

应用BP神经网络方法,建立了垃圾焚烧过程故障诊断的模型.该方法采用梯度下降法训练网络权值,利用最优停止法对网络进行了优化,避免了过拟合现象.提高了BP神经网络的训练速度和泛化能力.并以实际焚烧过程工况数据进行性能检验,检验结果表明了该BP网络具有较高的诊断精度.     

Social Networks and Community-Based Natural Resource Management

We conducted case studies of three successful examples of collaborative, community-based natural resource conservation and development. Our purpose was to: (1) identify the functions served by interactions within the social networks of involved stakeholders; (2) describe key structural properties of these social networks; and (3) determine how these structural properties varied when the networks were serving different functions. The case studies relied on semi-structured, in-depth interviews of 8 to 11 key stakeholders at each site who had played a significant role in the collaborative projects. Interview questions focused on the roles played by key stakeholders and the functions of interactions between them. Interactions allowed the exchange of ideas, provided access to funding, and enabled some stakeholders to influence others. The exchange of ideas involved the largest number of stakeholders, the highest percentage of local stakeholders, and the highest density of interactions. Our findings demonstrated the value of tailoring strategies for involving stakeholders to meet different needs during a collaborative, community-based natural resource management project. Widespread involvement of local stakeholders may be most appropriate when ideas for a project are being developed. During efforts to exert influence to secure project approvals or funding, however, involving specific individuals with political connections or influence on possible sources of funds may be critical. Our findings are consistent with past work that has postulated that social networks may require specific characteristics to meet different needs in community-based environmental management.     

Artificial neural network based carbon monoxide persistence models for episodic urban air quality management

This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.     

Stochastic Transfer Function Model and Neural Networks to Estimate Soil Moisture1

Abstract: A practical methodology is proposed to estimate the three‐dimensional variability of soil moisture based on a stochastic transfer function model, which is an approximation of the Richard’s equation. Satellite, radar and in situ observations are the major sources of information to develop a model that represents the dynamic water content in the soil. The soil‐moisture observations were collected from 17 stations located in Puerto Rico (PR), and a sequential quadratic programming algorithm was used to estimate the parameters of the transfer function (TF) at each station. Soil texture information, terrain elevation, vegetation index, surface temperature, and accumulated rainfall for every grid cell were input into a self‐organized artificial neural network to identify similarities on terrain spatial variability and to determine the TF that best resembles the properties of a particular grid point. Soil moisture observed at 20 cm depth, soil texture, and cumulative rainfall were also used to train a feedforward artificial neural network to estimate soil moisture at 5, 10, 50, and 100 cm depth. A validation procedure was implemented to measure the horizontal and vertical estimation accuracy of soil moisture. Validation results from spatial and temporal variation of volumetric water content (vwc) showed that the proposed algorithm estimated soil moisture with a root mean squared error (RMSE) of 2.31% vwc, and the vertical profile shows a RMSE of 2.50% vwc. The algorithm estimates soil moisture in an hourly basis at 1 km spatial resolution, and up to 1 m depth, and was successfully applied under PR climate conditions.     

基于BP模型对城市交通噪声的数据处理和预测

城市交通噪声的预测和评价技术是城市交通可持续发展的重要研究内容，直接为城市交通规划中环境容量分析和环境影响评价服务。本文通过实测的大量数据，运用神经网络中的BP模型及其算法建立车辆数、道路宽度和交通噪声之间的关系，对城市道路交通噪声数据进行处理和预测。     

基于人工神经网络建模的PTC功率智能检测技术

PTC是家用空调器中主要器件之一,具有耗电量大、非线性的特征。目前PTC常用的功率检测方法是通过"专用电量计量芯片结合电压、电流采样电路"来实现,存在成本高和可靠性低问题。提出一种基于人工神经网络的PTC功率纯软件计算方法,无需增加任何硬件成本。通过实验验证,精度达±3%,满足产品化需求,成熟可靠。