人工神经网络的性病艾滋病预测模型研究

目的用BP-人工神经网络(Back-propagation neural network,BP-ANN)建立性病艾滋病预测模型。方法对330例感染性病艾滋病的出入境人员和330例非性病出入境人员进行统计分析,以15个自变量(包括年龄、性别、国籍、职业、文化程度、国内外劳务史、性伴侣数、生殖系统异常症状史、不洁性生活史、明确性伴侣是否有性病、合法性伴侣是否是性病的高危人群、输血史、吸毒史、是否有同性性伴侣和是否拒绝流行病学调查)建立对患病状态的BP-ANN。结果性病患者与非性病者的年龄、性别、国籍、职业、文化程度、国内外劳务史、性伴侣数、病史、不洁性生活史、性伴侣情况的差异有统计学意义(P<0.01)。隐含层神经元个数为7个BP-ANN的预测效果最好,网络结构为15-7-1,其训练准确率、校验准确率和测试准确率分别达到93.94%、88.48%和89.60%。结论BP-ANN建立了良好的性病艾滋病预测模型,为性病艾滋病监测和风险预警提供了一种新的方法。

作物蒸发蒸腾量的人工神经网络模型研究

采用盆栽试验,利用BP-人工神经网络模拟作物的蒸发蒸腾量,分别构建ET1(气象因子)、ET2(气象因子与播种天数)、ET3(气象因子、播种天数和含水率)3种人工神经网络模型,并将预测结果与称重法得到的实际值ET进行比较,结果表明,所构建的ET3模型的计算精度较高,是一种最优的计算作物蒸发蒸腾量的BP-人工神经网络模型。

考虑时滞效应的龙爪槐树干液流人工神经网络模型研究

以鲁东大学校内5年生龙爪槐为例,分别利用美国产AZ-M茎流系统和澳大利亚产AXWG03自动气象站对植株的树干液流和微环境气象因子进行了观测,探讨了BP-人工神经网络模型在植物液流与环境因子定量分析中的适用性,并比较了考虑液流相对于微环境气象因子滞后效应前后人工神经网络模型拟合精度的变化。结果表明,树干液流速率相对于太阳总辐射、光合有效辐射和风速分别存在10 min、10 min和20 min等间隔不等的时滞。与传统的多元线性回归和不考虑滞后效应的BP-人工神经网络模型相比,考虑滞后效应的人工神经网络模型的拟合精度显著提高,考虑、不考虑滞后效应的人工神经网络模型和多元线性回归模型得到的液流速率拟合值与观测值回归方程的决定系数分别为0.94 4、0.888和0.853;液流速率拟合值与观测值相对误差处于±5%和±10%范围内的分别为41.177%、35.849%、30.189%和70.588%、62.264%、31.527%。由分析结果可知,液流的时滞是模型建立中一个不可忽略的现象。

用人工神经网络方法建立出入境人员性病艾滋病预测模型

〔目的〕研究用人工神经网络建立出入境人员性病艾滋病预测模型。〔方法〕以330例患有性病、艾滋病的出入境人员和330例非性病出入境人员组成研究样本。在对研究对象问询资料整理后进行统计分析和统计推断,再运用BP神经网络(BP-ANN)拟合,以15个自变量(包括年龄、性别、国籍、职业、文化程度、国内外劳务史、性伴侣数、生殖系统异常症状史、不洁性生活史、明确性伴侣是否是有性病、合法性伴侣是否性病的高危人群、输血史、吸毒史、是否有同性性伴侣和是否拒绝流行病学调查)建立对患病的预测模型。〔结果〕性病、艾滋病患者与非性病者间在年龄、性别、国籍、职业、文化程度、国内外劳务史、性伴侣数、病史、不洁性生活史、性伴方面的差异都具有显著性(P<0.01)。以隐含层神经元个数为7个BP-ANN模型中的预测效果最好,网络的结构为15-7-1。该模型的准确率较高,训练准确率、校验准确率和测试准确率分别达到93.94%、88.48%和89.60%,同时稳定性高、复杂性低。〔结论〕BP-ANN较好的建立了性病、艾滋病预测模型,为口岸和国内外性病、艾滋病监测和风险预警提供了一种新的方法。

基于可见/近红外反射光谱的稻米品种与真伪鉴别

利用可见/近红外光谱技术对市场上5种稻米进行了鉴别.以ASD FieldSpec3地物光谱仪采集了5种稻米的光谱数据,各获取35个样本,随机分成训练集(150份)和检验集(25份),并分别采取全波段与特征波段(400～500nm、910～1400nm与1940～2300nm)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人工神经网络(BP-ANN)的输入变量,稻米品种作为输出变量,建立3层BP-ANN鉴别模型.利用25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%,其中特征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PCA-BP神经网络分析法进行稻米品种与真伪的快速、无损鉴别是可行的,且提取特征波段是优化模型的有效方法之一.

基于空间相关性的分布式光伏超短期预测技术研究

因历史出力数据的缺失及精确数值天气预报的无法获取,很大比例的分布武光伏电站不能利用现有成熟技术对其出力进行超短期预测。提出了一种基于空间相关性的分布式光伏超短期功率预测技术。基于分层聚类算法对光伏电站间空间相关关系进行判断及匹配,从而得到目标光伏电站到参考光伏电站的空间映射关系。基于BP~人工神经网络算法对参考光伏电站的超短期功率进行预测,并将预测结果作为空间映射关系的输入,计算出目标光伏电站的超短期功率预测。仿真算例结果表明:基于空间相关性的预测方法利用最近48 h内的历史出力,达到了精度较高的超短期预测效果。

改进GA算法结合ANN用于酚类化合物的QSAR研究

采用改进的遗传算法(IGA)和BP人工神经网络相结合的方法,研究了50个酚类化合物的麻醉毒性和分子结构之间的相关性,并与单纯用BP人工神经网络建立的模型进行比较.结果表明,该方法克服了人工神经网络训练中的局部最优问题,采用最优交叉和变异等遗传策略,有效地解决了收敛过程中的振荡问题,所得模型的训练精度和预测精度均优于单纯的BP人工神经网络QSAR模型.

ANN Model and Learning Algorithm in Fault Diagnosis for FMS

The fault diagnosis model for FMS based on multi layer feedforward neural networks was discussed An improved BP algorithm,the tactic of initial value selection based on genetic algorithm and the method of network structure optimization were presented for training this model ANN(artificial neural network)fault diagnosis model for the robot in FMS was made by the new algorithm The result is superior to the rtaditional algorithm

The pH control optimization in the crop fertigation system using ANN

pH regulation is a complicated and comprehensive technique in the crop fertigation system. In this paper, a method is put forward to improve the quality of pH regulation, using artificial neural network to map a nonlinear relationship between pH interfering factor and the switching frequency of pH control valve, which achieves the dynamic feedforward compensation to the main control system.

Ecosystem Health Assessment of Honghu Lake Wetland of China Using Artificial Neural Network Approach

Honghu Lake,located in the southeast of Hubei Province,China,has suffered a severe disturbance during the past few decades.To restore the ecosystem,the Honghu Lake Wetland Protection and Restoration Demonstration Project(HLWPRDP) has been implemented since 2004.A back propagation(BP) artificial neural network(ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland.And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project.Particularly,12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs.The output is one layer of ecosystem health index.After training and testing the BP ANNs,an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland.The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP(in 2002) to middle health after the implementation of the HLWPRDP(in 2005).It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.

Prediction of oxygen concentration and temperature distribution in loose coal based on BP neural network

An effective method for preventing spontaneous combustion of coal stockpiles on the ground is to control the air-flow in loose coal. In order to determine and predict accurately oxygen concentrations and temperatures within coal stockpiles, it is vital to obtain information of self-heating conditions and tendencies of spontaneous coal combustion. For laboratory conditions, we designed our own experimental equipment composed of a control-heating system, a coal column and an oxygen concentration and temperature monitoring system, for simulation of spontaneous combustion of block coal （13-25 mm） covered with fine coal （0-3 mm）. A BP artificial neural network （ANN） with 150 training samples was gradually established over the course of our experiment. Heating time, relative position of measuring points, the ratio of fine coal thickness, artificial density, voidage and activation energy were selected as input variables and oxygen concentration and temperature of coal column as output variables. Then our trained network was applied to predict the trend on the untried experimental data. The results show that the oxygen concentration in the coal column could be reduced below the minimum still able to induce spontaneous combustion of coal - 6% by covering the coal pile with fine coal, which would meet the requirement to prevent spontaneous combustion of coal stockpiles. Based on the prediction of this ANN, the average errors of oxygen concentration and temperature were respectively 0.5% and 7 ℃, which meet actual tolerances. The implementation of the method would provide a practical guide in understanding the course of self-heating and spontaneous combustion of coal stockpiles.

Development of a spontaneous combustion TARPs system based on BP neural network

Spontaneous combustion of coal is a major cause of coal mine fires.It not only poses a severe hazard to the safe extraction of coal resources,but also jeopardizes the safety of mine workers.The development of a scientific management system of coal spontaneous combustion is of vital importance to the safe production of coal mine.This paper provides a comparative analysis of a range of worldwide prediction techniques and methods for coal spontaneous combustion,and systematically introduces the trigger action response plans(TARPs)system used in Australian coal mines for managing the spontaneous heating of coal.An artificial neural network model has been established on the basis of real coal mine operational conditions.Through studying and training the neural network model,prediction errors can be controlled within the allowable range.The trained model is then applied to the conditions of Nos.1 and 3 coal seams located in Weijiadi Coal Mine to demonstrate its feasibility for spontaneous combustion assessment.Based upon the TARPs system which is commonly used in Australian longwall mines,a TARPs system has been developed for Weijiadi Coal Mine to assist the management of spontaneous combustion hazard and ensure the safe operation of its mining activities.

Simulation of phytoplankton biomass in Quanzhou Bay using a back propagation network model and sensitivity analysis for environmental variables

Prediction and sensitivity models,to elucidate the response of phytoplankton biomass to environmental factors in Quanzhou Bay,Fujian,China,were developed using a back propagation(BP) network.The environmental indicators of coastal phytoplankton biomass were determined and monitoring data for the bay from 2008 was used to train,test and build a three-layer BP artificial neural network with multi-input and single-output.Ten water quality parameters were used to forecast phytoplankton biomass(measured as chlorophyll-a concentration).Correlation coefficient between biomass values predicted by the model and those observed was 0.964,whilst the average relative error of the network was-3.46% and average absolute error was 10.53%.The model thus has high level of accuracy and is suitable for analysis of the influence of aquatic environmental factors on phytoplankton biomass.A global sensitivity analysis was performed to determine the influence of different environmental indicators on phytoplankton biomass.Indicators were classified according to the sensitivity of response and its risk degree.The results indicate that the parameters most relevant to phytoplankton biomass are estuary-related and include pH,sea surface temperature,sea surface salinity,chemical oxygen demand and ammonium.

Prediction of 2A70 aluminum alloy flow stress based on BP artificial neural network

The hot deformation behavior of 2A70 aluminum alloy was investigated by means of isothermal compression tests performed on a Gleeble-1500 thermal simulator over 360～480 ℃ with strain rates in the range of 0.01～1 s-1 and the largest deformation up to 60%. On the basis of experiments, a BP artificial neural network （ANN） model was constructed to predict 2A70 aluminum alloy flow stress. True strain, strain rates and temperatures were input to the network, and flow stress was the only output. The comparison between predicted values and experimental data showed that the relative error for the trained model was less than ±3% for the sampled data while it was less than ±6% for the non-sampled data. Furthermore, the neural network model gives better results than nonlinear regression method. It is evident that the model constructed by BP ANN can be used to accurately predict the 2A70 alloy flow stress.

Nonlinear inversion for electrical resistivity tomography based on chaotic DE-BP algorithm

Nonlinear resistivity inversion requires efficient artificial neural network(ANN)model for better inversion results.An evolutionary BP neural network(BPNN)approach based on differential evolution(DE)algorithm was presented,which was able to improve global search ability for resistivity tomography 2-D nonlinear inversion.In the proposed method,Tent equation was applied to obtain automatic parameter settings in DE and the restricted parameter Fcrit was used to enhance the ability of converging to global optimum.An implementation of proposed DE-BPNN was given,the network had one hidden layer with 52 nodes and it was trained on 36 datasets and tested on another 4 synthetic datasets.Two abnormity models were used to verify the feasibility and effectiveness of the proposed method,the results show that the proposed DE-BP algorithm has better performance than BP,conventional DE-BP and other chaotic DE-BP methods in stability and accuracy,and higher imaging quality than least square inversion.

Estimation of half-wave potential of anabolic androgenic steroids by means of QSER Approach

The quantitative structure-property relationship(QSPR) of anabolic androgenic steroids was studied on the half-wave reduction potential(E1/2) using quantum and physico-chemical molecular descriptors. The descriptors were calculated by semi-empirical calculations. Models were established using partial least square(PLS) regression and back-propagation artificial neural network(BP-ANN). The QSPR results indicate that the descriptors of these derivatives have significant relationship with half-wave reduction potential. The stability and prediction ability of these models were validated using leave-one-out cross-validation and external test set.

Research on safety assessment of gas explosion hazard in heading face based on BP neural network

According to hazard theory and the principle of selecting assessment index,combining the causes and mechanisrn of gas explosion, established assessment index system of gas explosion in heading face. Based on the method of gray clustering, principle of BP neural network and characters of gas explosion in heading face, safety assessment procedural diagram of BP neural network on gas explosion hazard in heading face is designed. Meanwhile, concrete heading face of the gas explosion hazard is assessed by safety assessment method of BP neural network and grades of comprehensive safety assessment are got. The static and dynamic safety assessment can be achieved by this method. It is practical to improve safety management and to develop safety assessment technology in coalmine.

Neural network fault diagnosis method optimization with rough set and genetic algorithms

Aiming at the disadvantages of BP model in artificial neural networks applied to intelligent fault diagnosis, neural network fault diagnosis optimization method with rough sets and genetic algorithms are presented. The neural network nodes of the input layer can be calculated and simplified through rough sets theory; The neural network nodes of the middle layer are designed through genetic algorithms training; the neural network bottom-up weights and bias are obtained finally through the combination of genetic algorithms and BP algorithms. The analysis in this paper illustrates that the optimization method can improve the performance of the neural network fault diagnosis method greatly.

A BOD-DO coupling model for water quality simulation by artificial neural network

A one-dimensional BOD-DO coupling model for water quality simulation is presented, which adopts Streeter-Phelps equations and the theory of back-propagation artificial neural network. The water quality data of Yangtze River in the Chongqing region in the year of 1989 are divided into 5 groups and used in the learning and testing courses of this model. The result shows that such model is feasible for water quality simulation and is more accurate than traditional models.

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Computer-aided diagnosis(CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging(MRI), image features from T2-weighted images(T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone(PZ) and central gland(CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features(10/12) had significant difference(P<0.01) between PCa and non-PCa in the PZ, while only five features(sum average, minimum value, standard deviation, 10 th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2 w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.