Artificial Neural Network Fault Diagnosis System Based on Rough Set Theory

On the basis of fault diagnosis neural network model, in this paper, knowledge representation system of rough set theory is taken as a major tool to delaminate the complex neural network and in which unnecessary properties are eliminated. This method overcomes some shortcomings, such as network scale is too large and the rate of classification is slow. The good effect that reduces the matching quantity of pattern search in classification course is gotten. The structure and algorithm of layered-mining neural network model based on rough set theory are also given. The example shows that this system has higher reference value in practical application.     

基于LM-BP神经网络的耕地土壤养分等级划分模型──以皖南山区为例

研究旨在通过BP神经网络方法,构建起LM-BP网络结构(5-M-1)模型,达到对土壤养分等级划分的目的,为合理的土壤养分管理提供可靠依据。采用Levenberg-Marquardt (LM)训练算法,构建3层网络模型：一个输入层、一个隐含层、一个输出层,利用3层网络作为耕地土壤养分等级划分模型。利用土壤养分各级评价标准作为模型的训练样本和测试样本,以此来对BP神经网络进行训练和测试,并对歙县土壤养分进行综合评价。结果表明：LM-BP网络结构对测试样本输出的预测值和实际参考值是一致的。最终通过灰色关联模型和主成分分析方法对歙县土壤养分的综合评价结果与BP神经网络的模拟结果相对比,发现也是基本一致的。LM-BP网络结构应用于土壤养分等级划分中,得到了很好的预测效果,为智能算法应用于农业领域奠定了良好的基础。     

Study on Network Learning System Based on Fuzzy Mathsmatic

Evaluation of network study result is key job that network teaching and network teaching system developments. Fuzzy comprehensive evaluation method is introduced to analyze the network study based on the network study evaluation system index system combining with the characteristics of the combinative network study. The paper puts forward the model of the fuzzy comprehensive evaluation of network study, it proves the fuzzy comprehensive evaluation method is a kind of valid method than other traditional method in network learning through an experiment.     

Application of fuzzy comprehensive evaluation in mine geological environment

Based on the complex geological environment and hydro-geological conditions in mountainous areas the fuzzy comprehensive evaluation index system of the mine geological environment is constructed. It uses the weak fuzzy consistent matrix and analytic hierarchy process to calculate the weight, which solves the question that traditional AHP is difficult to meet the consistency test. According to the special geological conditions of the mine, it divides the impact of the geological environment into three levels, builts the fuzzy comprehensive evaluation table and determines the membership degree of the influencing factors. Combined with fuzzy comprehensive evaluation method, it developes a second-order fuzzy comprehensive evaluation model of the impact of the mine geological environment. By means of the application of this model to the actual mining, it shows that the evaluation results of the model are highly similar to the actual results of the impacts of landslides, land subsidence and other geological disasters.     

基于BP神经网络的农业电子商务网站评价研究

提出了适合农业电子商务网站的评价指标体系。建立农业电子商务网站的BP神经网络评价模型,通过BP神经网络对数据进行训练、测试,对提出的评价指标体系进行了验证。通过对结果的分析发现,建立的基于BP神经网络的评价模型在农业电子商务网站评价中具有可行性,在网站建设方面具有较强的参考价值。     

基于BP人工神经网络的区域土地生态安全评价研究——以安徽省为例

为了进行区域土地生态安全评价研究,在界定土地生态安全概念的基础上,建立了由15个评价指标组成的区域土地生态安全评价指标体系,运用BP人工神经网络方法,构建起区域土地生态安全评价模型,并根据此模型对1998—2012年安徽省土地生态安全程度进行评价。研究表明：（1）安徽省土地生态安全等级在1998—2002年是“非常不安全”,2002—2007年是“较安全”,2008—2012年是“较不安全”;（2）BP人工神经网络通过对历史数据的学习,能够实现区域土地生态安全评价,对探索区域土地生态安全评价方法具有重要意义。     

基于主成分分析的BP神经网络估算藏北高寒草地覆盖度变化

藏北高寒牧区草地是中国高寒草地分布面积最大的地区。为了及时准确地获得该区域草地覆盖度的变化趋势,本研究利用多年气象数据、社会统计数据、GIMMS、MODIS两种归一化植被指数（NDVI）数据作为参数,构建 BP神经网络模型,估算2010—2014年藏北高寒草地年际变化趋势,并用主成分分析方法优化参数来改进模型。结果表明,① BP神经网络模型及其改进模型对藏北高寒草地覆盖度年际变化趋势与遥感值的相关系数为0.16、0.47,表明通过主成分分析优化参数后的BP神经网络模型具有较好的模拟效果。 ②两种BP神经网络估算的植被指数值与NDVI值平均误差率分别为2.36%、2.20%。均有较高的模拟精度。③从神经网络训练步数上看,BP神经网络结果训练收敛步长为5000,基于主成分分析的BP神经网络模型训练收敛步长为454,表明后者提高了计算效率,体现出良好的收敛性。因此,无论从年际变化趋势拟合程度、植被指数估算值精度、还是从计算效率来看,改进的BP神经网络模型对于估算藏北高寒草地覆盖度变化更加行之有效。     

基于BP神经网络的土壤重金属污染评价方法——以包头土壤环境质量评价为例

为了克服国标(GBl5618—1995)内梅罗综合污染指数评价土壤环境质量时存在的缺点,借助BP神经网络模型,并结合GIS技术对包头土壤重金属污染的空间分布进行研究。实地调研获得221个土壤样,利用原子荧光光谱法和等离子体质谱法测试得到8种重金属含量数据。统计结果表明,研究区主要为Pb、Zn污染,考虑研究区特异性构造神经网络学习样本,建立基于特征模式的BP神经网络土壤环境质量评价模型。根据采样点评价结果,利用Kriging插值法绘制包头土壤环境质量专题图,分析得出包头土壤环境呈沿昆都伦河被污染的条带特点。结果表明,根据采样统计量信息利用BP神经网络模型,能够有效建立特殊研究区土壤中各种重金属含量与环境质量之间的非线性映射关系,为土壤污染的来源、分布、累积效应和主要影响因素以及污染链的阻断提供依据。     

基于大数据的食品安全标准体系评价模型研究

基于大数据的食品安全评价模型,依照社会监管机制,围绕食品安全问题的责任主体展开研究。基于大数据技术,建立了食品安全三维空间模型,并构建其标准化体系。建立研究食品安全问题评价模型,按照食品安全的前中后期构建评价指标,全面涵盖了食品安全问题的所有阶段。利用网络层次法对食品监管情况进行评价,通过数据调研,构建全面的监管评价数据模型。试验结果表明,本研究具有极高的可靠性和有效性,并可用于指导食品安全问题的体系建设,具有重大的现实意义。     

Study of Intelligent Fault Diagnostic System for a Kind of Power Plant

Based on the neural networks, object oriented technique, artificial intelligence technique and computer networks, an intelligent fault diagnosis system of the power plant whose need of the operation is presented. The architecture of software system , hardware system and corresponding databases is designed. The software of collecting data is established. The intelligent fault diagnosis system based on neural networks and object oriented technique is also developed. The physical system operation has been shown that the method mentioned above is feasible and effective in practice.     

粒子群优化的神经网络在夏季降水预测中的应用

降水短期气候预测是一个非常复杂、重要的研究课题。为了提高其预测能力,拟采用1959—2011年逐月74项大气环流特征量序列、月平均500 hPa高度场和月平均海温场,选取预测因子;用主分量分析方法提取样本数据中主要信息为综合因子。用粒子群优化人工神经网络方法,建立宣城市夏季降水短期气候预测模型。对2007—2011年宣城市夏季降水预报检验结果表明,粒子群优化人工神经网络收敛速度快,迭代次数少;试报平均绝对误差是66.5 mm,绝对值平均相对误差10.5%,预测精度高,具有很好的应用推广前景。     

Predictive Control Approach for Active Power Filter Based on Neural Networks

In this paper, a predictive control approach using neural networks for active power filter is proposed. The control system of active power filter using this method make using of the internal model control technology based on neural networks, meanwhile, to solve its questions such as lag because of calculating using neural networks, a predictive model based on neural networks is introduced. Simulation analysis shows that this control approach can compensate the lag of system and take advantage of self-adaptive characteristic of neural networks. Good result can be obtained.     

Research on Load Prediction Model of Air Condition System Based on Elman Neural Network

The load of air condition system is influenced by many factors, and they are variable and nonlinear, The relation between them is dynamic,It is impossible to forecaste the load of air condition syestem accurately by traditional method. But Recurrent Neural Network is able to reflect the dynamic lively and directly. Elman is one of the typical RNN. Based on the analysis as above, prediction model of air-condition system based on Elman neural network is established, and some prediction is done. The prediction accuracy of Elman neural network and BP neural network is compared, and the experiments show that the Elman neural network is efficiency and accuracy , so Elman neural network is a new and reliable method for predicting the load of air-condition system.     

多目标决策灰色投影法在水土保持综合效益评价中的应用

运用灰色系统理论和矢量投影原理,建立了水土保持综合效益评价多目标决策灰色关联投影模型,旨在探讨多目标决策灰色关联投影法在水土保持综合效益评价中的应用。研究表明,利用多目标决策灰色关联投影法进行水土保持综合效益评价是有效的,具有应用和推广价值。     

Evaluation of Academic Degrees and Graduate Education Based on Neural Network

With the development of the academic degrees and graduate education, evaluation is the important measure for guaranteeing the quality in the area. With increasing complication of the target of the evaluation of academic degrees and graduate education , it is necessary to introduce nonlinear method of the evaluation, which has great significance for the validity of the result of the evaluation. Neural networks have been developed greatly in recent years,they have the advantages of nonlinear mapping,parallelity ,adaption, etc. Therefore , they are good tools of nonlinear modeling. On the basis of setting up the model of the evaluation based on neural network ,this paper presets the practical value of the method.based them.     

乡镇政府网站主客观综合评价指标体系及方法研究

基于对乡镇政府网站和城市政府网站的区别总结,分析了乡镇政府网站评估和研究其评估体系的必要性,建立了适用于乡镇政府网站的主客观相结合的评估指标体系。同时应用层次分析法获得各指标的权重,详细列出了各指标的评估要点和计算方法,初步提出了完整的乡镇政府网站的评估模型。     

Application of Intelligent Neural Network on Prediction of Time Series Signal

The research of neural network has been maturated both in theory and practical application since 1980's, and also been employed into the prediction and analysis of nonlinear time series signal in the field of signal process system. Concerning with the problem of time series signal prediction based on traditional neural network, such as black box, poor accuracy, and facing the shortage of post knowledge, this paper presents a different neural network prediction model from the traditional ones, based on intelligent neural cell model and employing the iterative prediction method. Through the example on stock price prediction, the prediction accuracy and practical value are proved.     

Calculation Model Based on Improved BP Network of Refrigerant State Parameters

BP network is improved by using Levenberg-Marquardt optimization method. Under large swatch input circumstance, more fast network constringency speed and more highly approach precision are obtained. Calculation model based on improved BP neural network of refrigerant state parameters is established. The main defect of refrigerant hot ties of matter model in existing refrigeration and air-conditioning appliances simulation system is overcome.     

The improved geometrical constraint model of open-pit mine boundary optimum and its application

This paper develops the improved geometric constraint model (IGCM) based on the 3D block model. The variable-slope excavation cones of each block have been abstracted to collections of nodes and directed edges, and the optimization method has been introduced using the set theory. The IGCM consists of the optimized collection nodes and directed edges. The algorithm based on IGCM has been actualized in DIMINE digital mine system, and is applied to an actual open pit mine boundary optimization engineering. The results shows that IGCM can accurately simulate variable slope angles in any azimuth and elevation changes, and adapt to all open pit mine boundary optimum problems in any complex geological conditions, calculation speed and accurate. It provides a new and effective method for the variable-slope open pit mine boundary optimization problem in complex geological conditions.     

Analysis on cooling load on hot mine

Air stream of hot mines is taken as research object, and energy balance equation is established. Cooling load expressions under five different conditions are derived through strict theoretical derivation, i.e. unsteady multi-stream, steady flow, steady flow ignoring kinetic energy change, steady flow ignoring potential energy change and steady flow ignoring fan power change. Then the enthalpy variable expression of the subsurface moist air and heat calculating process of surrounding rock are deduced. Finally, Yong Chuan coal mine illustrates and summarizes the process of calculation cooling load quantitatively. It is conducive to scientificly manage hot mine and design mine ventilation.