基于无线传感网络隧道亮度信号采集系统的研究

亮度信号是高速公路隧道节能照明控制系统必须检测的物理量。本文基于zigbee的技术特点，设计了一款基于无线传感网络的隧道亮度信号采集系统。系统的传感器节点负责亮度信号的采集，经CC2530处理发送，通过逐级跳转的方式把数据传输到协调器。协调器通过RS232串口与灯具控制器通信，以此实现对亮度信号的实时采集和可靠性传输。通过实验表明，该系统达到了设计的目的，并适用于高速公路隧道节能照明控制系统。     

基于PSO-BP神经网络的外贸发展状况的评价

以我国对外贸易的实际情况为基础，建立了评价外贸发展状况的一套指标体系.使用主成分分析方法对所有评价指标进行预处理之后，借助粒子群优化算法改进了BP神经网络算法，构建了外贸发展状况的评价模型.评价模型在具体应用中，评价结果较为准确，具有一定的科学性和合理性.     

基于LM算法的BP神经网络的电力负荷短期预测

清代书院课艺总集多为连续出版物，或具有连续出版物的刊行初衷。刊期短则一季，多则一年或数年。经费充足与否，会影响刊期。发表周期多为一年至五年，也有十余年的。用稿率以10%~20%居多，偶见“关系稿”。时文的用稿标准是“清真雅正”。题目多为官师所拟。一般全文刊登，也偶有“论点摘编”。多经润色，并附录评点。有的以袖珍本刊行，有的宣称“翻刻必究”，标出定价，附载广告。稿费已在膏火费中预支，优秀作品可被转载。从本质属性和诸多要素来看，书院课艺总集实开今日“大学学报”、“学术集刊”之先河。     

基于模糊评判法的长安区滑坡崩塌灾害易发性分区评价

在对长安区滑坡崩塌地质灾害发育现状及形成条件分析的基础上,结合地质灾害易发性分区评价方法,采用模糊综合评判法对长安区滑坡崩塌地质灾害进行了易发性区划评价。结果表明,长安区滑坡崩塌易发性分区分为6个高易发区域、3个中易发区、1个低易发区(C)和1个不易发区(D),高易发区总面积为190.94 km2,中易发区为382.93 km2,低易发区为299.21 km2,不易发区为707.56km2。     

物流企业的社会网络特征与企业间信任的构建

第三方物流企业较之其他企业显示出较强的网络特征，企业之间的社会关系网络及其信任构建对企业的生存和发展具有重要意义。本文通过实证研究发现，物流企业信任的形成与企业社会网络的形成过程密切相关，企业组织社会网络的连接对组织信任的构建产生着重要影响。信任加速了企业获取资源的速度和质量，资源获取是企业网际互动的目的和信任的结果。     

LM-BP神经网络水质评价模型应用研究——以新乡市卫河为例

介绍了LM-BP神经网络模型的原理及算法和模型的优点。针对实际水质评价问题,利用随机内插方法在地表水环境质量分级标准阈值间生成训练样本和检验样本,建立了新乡市卫河地面水环境质量综合评价的LM-BP神经网络模型,将模型应用于卫河2011年3月份、9月份的水质评价,并与单因子评价法、模糊综合评价法进行了比较分析。实验结果表明该模型设计合理,泛化能力强,收敛速度快,算法稳定,推导严谨,有较充分的理论依据,应用于水质评价具有其合理性、实用性和有效性,适用于作深入的水环境质量分析。     

Artificial Neural Networks Models for Flow Stress during High Temperature Plastic Deformation of Al-Li Alloy

Using gained experimental data to develop the models of stable flow stresses at high temperature plastic deformation by statistical methods for alloy materials, precision of the models is poor and at the same time the processes of modeling are complicated with great workload. On the basis of the data obtained on Gleeble-1500 Thermal Simulator,the predicting models for the relation between stable flow stress during high temperature plastic deformation and deformation strain, strain rate and temperature for 1420 Al-Li alloy have been developed with BP Artificial Neural Network method. The results show that the model on basis of BPNN is practical and it reflects the real feature of the deforming process. It states that the difference between the real value and the output of the model is in order of 5 percent.     

Multiple Layer BP Neural Network for Fuzzy Calculating

The FCBP(Fuzzy calculating BP) algorithm which is proposed by this paper hasovercome the sensitivity for samples,reduced the number of input layer's samples,lightened the burden of input layer.it is suitable to fuzzy inference and pattern recognition.     

Using BP Neural Network to Determine the Membership Function of the Input of Fuzzy Control System

While design the fuzzy controller, it is very important to determine the membership function of fuzzy variables.The data can be broadly classified as fuzzy sets by using the classification property of the BP neural network. The author selects a BP neural network with one hide layer and uses S function to the input and hide layer, and linear function to the output layer.Advanced BP algorithm isused to train the BP neural network in the environment of MATLAB . The nearer to the target values is the better the last output is.With the trained BP network , the membership values of the inputs can be got ten. This method has high rate and low error.