Three important factors influencing directly the dissolved oxygen (DO) of river including the outflow, the water temperature and the pH, were used as input parameters to set up a BP neural network based on Levenberg-M...Three important factors influencing directly the dissolved oxygen (DO) of river including the outflow, the water temperature and the pH, were used as input parameters to set up a BP neural network based on Levenberg-Marquant algorithm. The neural network model was proposed to evaluate DO in water. The model contains two parts: firstly, the learning sample is unified; secondly, the neural network is used to train the unified samples to ensure the best node number of hidden layer. The proposed model is applied to assessing the DO concentration of the Yellow River in Lanzhou city. The evaluation result is compared with that by the neural network method and the reported result in Lanzhou city. The comparison result indicates that the performance of the neural network model is practically feasible in the assessment of DO. At the same time, the linear interpolation method can add the number of network's learning sample to improve the prediction precision of the network.展开更多
Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial ...Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.展开更多
This paper first describes the basic theory of BP neural network algorithm, defects and improved methods, establishes a computer network security evaluation index system, explores the computer network security evaluat...This paper first describes the basic theory of BP neural network algorithm, defects and improved methods, establishes a computer network security evaluation index system, explores the computer network security evaluation method based on BP neural network, and has designed to build the evaluation model, and shows that the method is feasible through the MATLAB simulation experiments.展开更多
Having a universal, fair, democratic and practical higher education system plays a particularly important role in the future development of the country. However, the higher education system in various countries is une...Having a universal, fair, democratic and practical higher education system plays a particularly important role in the future development of the country. However, the higher education system in various countries is uneven. It is of great significance to establish a general evaluation system for the development of global education. In this paper, 23 indicators are preliminarily selected from the education data of Universitas 21 and Global Statistical Yearbook. After the gray correlation analysis, 12 indicators were selected. On the one hand, principal component analysis is used to reduce the dimension of these 12 indicators in 50 countries, and the first four principal components with cumulative contribution rate of 99% are finally selected as the input parameters of BP neural network. On the other hand, 12 indicators are divided into four aspects as the standard of scheme decision-making. Finally, a higher education quality evaluation and decision-making model based on BP neural network and analytic hierarchy process are established. Then eight countries are selected to use the model to evaluate their current higher education quality. Based on the input and evaluation results of the four aspects of higher education in various countries, the analytic hierarchy process is used to make program decision, and several improvement suggestions are put forward for the current education policies of various countries.展开更多
With the rapid development of China’s economy,the scale of the city has been continuously expanding,industrial enterprises have been increasing,the discharge of multiple pollutants has reached the top of the world,an...With the rapid development of China’s economy,the scale of the city has been continuously expanding,industrial enterprises have been increasing,the discharge of multiple pollutants has reached the top of the world,and the environmental problems become more and more serious.The air pollution problem is particularly prominent.Air quality has become a daily concern for people.In order to control air pollution,it is necessary to grasp the air quality situation in an all-round way.It is necessary to evaluate air quality.Accurate results of air quality evaluation can help people know more about air quality.In this paper,refers to previous research results and different evaluation methods,combined with artificial neural network,fuzzy theory,genetic algorithm,GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality.At the same time,for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application,the four-grade standard for annual air quality evaluation has been proposed,and its practicality has been verified through experiments.By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm,it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model,both in efficiency and accuracy.展开更多
有机碳含量是评价烃源岩潜力的主要参数,常用的总有机碳含量(TOC)测井反演模型难以深度剖析测井曲线之间的复杂共线性关系,制约了多维测井信息的综合评价效果。利用玛湖凹陷三叠系白碱滩组泥岩的热解实验结果和常规测井曲线资料,建立了...有机碳含量是评价烃源岩潜力的主要参数,常用的总有机碳含量(TOC)测井反演模型难以深度剖析测井曲线之间的复杂共线性关系,制约了多维测井信息的综合评价效果。利用玛湖凹陷三叠系白碱滩组泥岩的热解实验结果和常规测井曲线资料,建立了一种基于PCA-BP(Principal Component Analysis and Back Propagation)神经网络的有机碳含量智能预测方法。该方法以敏感测井曲线的加权平均值和TOC测试结果为原始数据集,首先利用方差膨胀因子检测测井曲线之间共线性,然后采用主成分分析PCA(Principal Component Analysis)技术对原始数据集进行去共线性和降维处理,确定出2个主成分,最后结合中子、自然伽马、密度、声波时差曲线值,建立出6个输入节点的3层BP(Back Propagation)神经网络预测模型,对研究区三叠系白碱滩组烃源岩潜力进行精细评价。3口取心井累积410m井段的预测结果表明,模型的决定系数高达0.879,预测结果平均绝对误差和均方误差分别为0.220和0.107,平均相对误差为16.1%。研究结果为准噶尔盆地勘探领域优选提供了可靠参考。展开更多
[目的/意义]学术论文是学者科研水平与学术贡献的重要佐证和标志。构建科学的学术论文评价模型,对人才评价、科研经费分配、评奖评优、晋升及招聘等都具有重要指导意义。[方法/过程]文章选择Web of Science数据库中“Information Scienc...[目的/意义]学术论文是学者科研水平与学术贡献的重要佐证和标志。构建科学的学术论文评价模型,对人才评价、科研经费分配、评奖评优、晋升及招聘等都具有重要指导意义。[方法/过程]文章选择Web of Science数据库中“Information Science and Library Science”学科类别下发表于2010年的论文作为研究对象。首先,基于论文多方面的关联特征构建模型特征空间;然后,采用机器学习中被广泛用于预测任务的有监督学习算法——BP神经网络训练模型,并进行十折交叉验证确保模型稳定性;最后,通过计算模型的校正决定系数(R_(adjusted)^(2))和均方根误差(RMSE),选择出最优模型。[结果/结论]本研究构建的最优BP神经网络模型的校正决定系数(R_(adjusted)^(2))达0.91,均方根误差(RMSE)约19.8,评价性能较好。展开更多
文摘Three important factors influencing directly the dissolved oxygen (DO) of river including the outflow, the water temperature and the pH, were used as input parameters to set up a BP neural network based on Levenberg-Marquant algorithm. The neural network model was proposed to evaluate DO in water. The model contains two parts: firstly, the learning sample is unified; secondly, the neural network is used to train the unified samples to ensure the best node number of hidden layer. The proposed model is applied to assessing the DO concentration of the Yellow River in Lanzhou city. The evaluation result is compared with that by the neural network method and the reported result in Lanzhou city. The comparison result indicates that the performance of the neural network model is practically feasible in the assessment of DO. At the same time, the linear interpolation method can add the number of network's learning sample to improve the prediction precision of the network.
文摘Recent years we have witnessed the rapid growth of social commerce in China, but many users are not willing to trust and use social commerce. So improving consumers’ trust and purchase intention has become a crucial factor in the success of social commerce. Business factors, environment factors and social factors including twelve secondary indexes build up a social commerce trust evaluation model. Questionnaires are handed out to collect twelve secondary indexes scores as input of BP neural network and composite score of trust as output. Model simulation shows that both training samples and test samples have low level of average error and standard deviation, which certify that the model has good stability and it is a good method for evaluating social commerce trust.
文摘This paper first describes the basic theory of BP neural network algorithm, defects and improved methods, establishes a computer network security evaluation index system, explores the computer network security evaluation method based on BP neural network, and has designed to build the evaluation model, and shows that the method is feasible through the MATLAB simulation experiments.
文摘Having a universal, fair, democratic and practical higher education system plays a particularly important role in the future development of the country. However, the higher education system in various countries is uneven. It is of great significance to establish a general evaluation system for the development of global education. In this paper, 23 indicators are preliminarily selected from the education data of Universitas 21 and Global Statistical Yearbook. After the gray correlation analysis, 12 indicators were selected. On the one hand, principal component analysis is used to reduce the dimension of these 12 indicators in 50 countries, and the first four principal components with cumulative contribution rate of 99% are finally selected as the input parameters of BP neural network. On the other hand, 12 indicators are divided into four aspects as the standard of scheme decision-making. Finally, a higher education quality evaluation and decision-making model based on BP neural network and analytic hierarchy process are established. Then eight countries are selected to use the model to evaluate their current higher education quality. Based on the input and evaluation results of the four aspects of higher education in various countries, the analytic hierarchy process is used to make program decision, and several improvement suggestions are put forward for the current education policies of various countries.
文摘With the rapid development of China’s economy,the scale of the city has been continuously expanding,industrial enterprises have been increasing,the discharge of multiple pollutants has reached the top of the world,and the environmental problems become more and more serious.The air pollution problem is particularly prominent.Air quality has become a daily concern for people.In order to control air pollution,it is necessary to grasp the air quality situation in an all-round way.It is necessary to evaluate air quality.Accurate results of air quality evaluation can help people know more about air quality.In this paper,refers to previous research results and different evaluation methods,combined with artificial neural network,fuzzy theory,genetic algorithm,GA-BP hybrid algorithm based on fuzzy theory is proposed to evaluate air quality.At the same time,for the problem that the two-grade standard of air quality annual evaluation is not suitable for practical application,the four-grade standard for annual air quality evaluation has been proposed,and its practicality has been verified through experiments.By setting contrast experiments and comparing the air quality evaluation model based on standard BP algorithm,it is proved that the fuzzy GA-BP evaluation model is better than the standard BP model,both in efficiency and accuracy.
文摘有机碳含量是评价烃源岩潜力的主要参数,常用的总有机碳含量(TOC)测井反演模型难以深度剖析测井曲线之间的复杂共线性关系,制约了多维测井信息的综合评价效果。利用玛湖凹陷三叠系白碱滩组泥岩的热解实验结果和常规测井曲线资料,建立了一种基于PCA-BP(Principal Component Analysis and Back Propagation)神经网络的有机碳含量智能预测方法。该方法以敏感测井曲线的加权平均值和TOC测试结果为原始数据集,首先利用方差膨胀因子检测测井曲线之间共线性,然后采用主成分分析PCA(Principal Component Analysis)技术对原始数据集进行去共线性和降维处理,确定出2个主成分,最后结合中子、自然伽马、密度、声波时差曲线值,建立出6个输入节点的3层BP(Back Propagation)神经网络预测模型,对研究区三叠系白碱滩组烃源岩潜力进行精细评价。3口取心井累积410m井段的预测结果表明,模型的决定系数高达0.879,预测结果平均绝对误差和均方误差分别为0.220和0.107,平均相对误差为16.1%。研究结果为准噶尔盆地勘探领域优选提供了可靠参考。
文摘[目的/意义]学术论文是学者科研水平与学术贡献的重要佐证和标志。构建科学的学术论文评价模型,对人才评价、科研经费分配、评奖评优、晋升及招聘等都具有重要指导意义。[方法/过程]文章选择Web of Science数据库中“Information Science and Library Science”学科类别下发表于2010年的论文作为研究对象。首先,基于论文多方面的关联特征构建模型特征空间;然后,采用机器学习中被广泛用于预测任务的有监督学习算法——BP神经网络训练模型,并进行十折交叉验证确保模型稳定性;最后,通过计算模型的校正决定系数(R_(adjusted)^(2))和均方根误差(RMSE),选择出最优模型。[结果/结论]本研究构建的最优BP神经网络模型的校正决定系数(R_(adjusted)^(2))达0.91,均方根误差(RMSE)约19.8,评价性能较好。
