浅谈选煤厂建设投资估算影响因素及改进措施

本文对选煤厂建设工程投资估算中影响准确性的因素进行了分析,并结合BP神经网络理论基于Matlab软件对选煤厂带式输送机及栈桥单位工程进行了数值模拟,通过对比数值模拟结果与概算指标估算结果,详细阐述了可以提高选煤厂建设工程投资估算准确性的几点建议。     

基于 BP 神经网络的结构损伤识别研究

针对结构的损伤识别进行了研究，选取结构固有频率平方变化比作为特征参数，建立12×25×1 BP网络结构，采用均方误差函数目标误差函数，学习函数选取梯度下降动量学习函数和L-M优化算法，对四层钢框架结构的损伤进行了检测。     

基于神经网络的企业信息能力模糊评价研究--以安徽省制造型企业为例

文章构建了企业信息能力评价指标体系，确定了由初始级、系统级、优化级、战略级和持续改善级构成的企业信息能力评语等级论域。运用神经网络确定模糊综合评价中的权重值，克服了现有其它方法主观性判断的不足。提出了信息能力模糊综合评价模型。利用该模型对安徽省制造型企业进行了实际应用分析。最后，对评价结果进行了分析并给出了政策建议。     

模糊层次分析法优化BP神经网络的交通冲突预测模型

本文通过全面剖析影响交通冲突的原因,以交通流量、道路几何设计和道路环境三方面的因素建立指标层次结构体系。提出基于模糊层次分析(FAHP)法优化BP神经网络(BPNN)的预测模型,应用于交通冲突预测。     

河北省碳足迹及碳承载力的时空演变分析

[目的]碳足迹及碳承载力的时空演变分析是当前分析温室气体排放量的热点问题。[方法]文章采用2004~2014年河北省化石能源消费数据、土地利用结构数据以及经济社会数据,通过构建碳足迹模型,基于Arc GIS平台对河北省11个地级市的碳足迹、碳承载力、净碳足迹进行时空演变分析。[结果](1)2004~2014年河北省碳足迹由2.224 5亿t增长至4.792 2亿t,其中煤炭能源消费量占90%左右,唐山、邯郸和石家庄碳足迹值较高,分别占河北省碳足迹的33%、18%和16%;(2)2004~2014年河北省碳承载力由9 043万t增长至1.050 6亿t,其中林地碳承载力占河北省碳承载力的97%左右,西南地区农、林业发达,碳承载力相对较高;(3)2004~2014年河北省净碳足迹呈逐年上升趋势,由1.536 7亿t增长至4.236 5亿t,唐山、邯郸及石家庄净碳足迹较大,分别占河北省净碳足迹的40%、22%和16%;(4)除保定外,其他10个地级市的碳足迹压力指数变化强度均呈现不同程度的增强趋势。此次研究成果将为河北省未来制定温室气体排放量等相关政策的建设提供参考。[结论]整体来看,河北省碳足迹及碳承载力呈逐年增长的变化趋势,应加强温室气体的管控力度,减小碳排放给河北省带来的负面影响。     

基于PCA-BP-GA的BOT项目风险预测研究

由于BOT项目本身的长期性和复杂性,所以在BOT项目实施前需要准确科学的预测出所面临的风险大小。针对BOT项目风险影响因素众多的问题,先利用主成分分析法进行降维,然后利用遗传算法找出BP神经网络的最优全值阈值,建立了PCAGA-BP BOT项目风险预测模型。同时将以往的BOT项目数据作为学习样本,对BOT项目风险进行预测,并利用某地污水厂的例子进行验证,说明此模型对实际工程的科学指导性。     

Using deep learning and visual analytics to explore hotel reviews and responses

This study aims to use computational linguistics, visual analytics, and deep learning techniques to analyze hotel reviews and responses collected on TripAdvisor and to identify response strategies. To this end, we collected and analyzed 113,685 hotel reviews and responses and their semantic and syntactic relations. We are among the first to use visual analytics and deep learning-based natural language processing to empirically identify managerial responses. The empirical results indicate that our proposed multi-feature fusion, convolutional neural network model can make different types of data complement each other, thereby outperforming the comparisons. The visualization results can also be used to improve the performance of the proposed model and provide insights into response strategies, which further shows the theoretical and technical contributions of this study.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.     

Determinants in the forecast of the gross national income of China and India from 1952 to 2015

In 2015, China and India's population represented approximately 35.74% of the total number of people living in the world. Due to the historical context and behavior of the most relevant indicators, this study proposes to utilize a wide variety of demographic, economic, and production indicators from 1952 to 2015 to assess their impact on the GNI in China and India. A comprehensive and new fangled modeling process with stepwise, regularization and distributed lag regression approaches is presented. Accordingly, theoretical results were corroborated through extensive diagnostic tests and an empirical check of the models' predictive capacity. The findings show that GNI in China is most influenced by variables such as reserves in foreign currency and the dependency ratio; whereas, variables of energy production and birth rate were generated for India. Therefore, it's the timing for China to relax the universal two-child policy. Due to the current value below the substitution rate, a gloomy outlook for China's future population and economy is predicted. Conversely, a positive outlook is forecasted for India, given the low price in the future of oil- India's primary raw material.     

Systemic financial risk early warning of financial market in China using Attention-LSTM model

We propose an Attention-LSTM neural network model to study the systemic risk early warning of China. Based on text mining, the network public opinion index is constructed and used as a training set to be incorporated into the early warning model to test the early warning effect. The results show that: (i) the network public opinion is the non-linear Granger causality of systemic risk. (ii) The Attention-LSTM neural network has strong generalization ability. Early warning effects have been significantly improved. (iii) Compared with the BP neural network model, the SVR model and the ARIMA model, the LSTM neural network early warning model has a higher accuracy rate, and its average prediction accuracy for systemic risk indicators has been improved over short, medium and long terms. When the attention mechanism is included in the LSTM, the Attention-LSTM neural network model is even more accurate in all the cases.