支持向量机在模式识别中的应用

针对传统神经网络存在网络结构难于确定、过学习以及局部极小等问题，研究了基于支持向量机（SVM）的模式识别问题。通过对棋盘这种典型非线性二值问题的分类研究，分析了支持向量机的分类与泛化能力。支持向量机在分类和泛化能力方面远远优于传统神经网络。最后将支持向量机用于对两类飞机目标的分类识别，通过多组蒙特卡罗试验，获得了较好的识别结果。支持向量机在目标识别中有巨大潜力和广阔前景。     

建设工程合同若干问题探讨

招标投标是建设工程合同成立的主要形式,其程序较为复杂,各阶段的性质及法律效力的界定十分重要,它决定着建设工程合同何时成立,也决定着毁标行为应承担违约责任还是缔约过失责任。理论上和实践中对此存在不同的认识和观点,主要原因在于现行立法存在缺陷。     

Time-varying risk aversion and renminbi exchange rate volatility: Evidence from CARR-MIDAS model

In this paper, we investigate the relation between time-varying risk aversion and renminbi exchange rate volatility using the conditional autoregressive range-mixed-data sampling (CARR-MIDAS) model. The CARR-MIDAS model is a range-based volatility model, which exploits intraday information regarding the intraday trajectory of the price. Moreover, the model features a MIDAS structure allowing for time-varying risk aversion to drive the long-run volatility dynamics. Our empirical results show that time-varying risk aversion has a significantly negative effect on the long-run volatility of renminbi exchange rate. Moreover, we observe that both intraday ranges and time-varying risk aversion contain important information for forecasting renminbi exchange rate volatility. The range-based CARR-MIDAS model incorporating time-varying risk aversion provides more accurate out-of-sample forecasts of renminbi exchange rate volatility compared to a variety of competing models, including the return-based GARCH, GARCH-MIDAS and GARCH-MIDAS incorporating time-varying risk aversion as well as range-based CARR, CARR-MIDAS and heterogeneous autoregressive (HAR), for forecast horizons of 1 day up to 3 months. This result is robust to alternative risk aversion measure, alternative MIDAS lags as well as alternative out-of-sample periods. Overall, our findings highlight the value of incorporating intraday information and time-varying risk aversion for forecasting the renminbi exchange rate volatility.     

Threats to the Validity of Logistics and Supply Chain Management Research

To increase the relevance of logistics and supply chain academic research, this paper recommends the development and testing of middle‐range theory and practice‐level theory. Yet, there are a number of research issues that arise when academic researchers test middle‐range and practice‐level theory, both in measuring constructs and in testing theoretical relationships between constructs. Concerning the measurement of constructs, this paper recommends that academic researchers pay more attention to content validity and undertake rigorous processes to ensure content validity. In addition, academic researchers need to more explicitly define constructs as either reflective or formative. If the construct is defined as formative, then the traditional statistical approaches to validate these measurement scales are not recommended. The appropriate use of employing single‐item measures for concrete constructs is discussed. In regard to conducting hypothesis tests, research issues associated with multicollinearity and omitted variable bias are discussed. Relative weight analysis is ideal for testing theoretical models and research hypotheses when survey data are obtained, multicollinearity is present, and there are a large number of independent variables predicting a dependent variable. Thus, relative weight analysis is ideal for testing research hypotheses in logistics and supply chain management.     

基于正交试验的“十三五”中国能源强度情景预测与分析

基于1990—2014年相关数据,利用多元回归法构建了中国能源强度预测模型,分析了能源价格、产业结构、能源结构、人口总数4个因素对中国能源强度的影响。将正交试验法与情景分析法相结合,对"十三五"期间中国的能源强度发展进行情景预测及相关路径规划。结果表明:4个因素对中国能源强度均具有显著影响,其中人口总数增加和能源结构优化对中国能源强度的降低具有抑制作用,产业结构调整和能源价格上升对能源强度的降低具有促进作用。对2016—2020年中国能源强度的发展路径进行情景预测,其中2020年中国能源强度最低可降至0.477吨标准煤/万元。最后,提出合理的政策建议。     

Exploring the predictability of range‐based volatility estimators using recurrent neural networks

We investigate the predictability of several range‐based stock volatility estimates and compare them with the standard close‐to‐close estimate, which is most commonly acknowledged as the volatility. The patterns of volatility changes are analysed using long short‐term memory recurrent neural networks, which are a state‐of‐the‐art method of sequence learning. We implement the analysis on all current constituents of the Dow Jones Industrial Average index and report averaged evaluation results. We find that the direction of changes in the values of range‐based estimates are more predictable than that of the estimate from daily closing values only.     

中国农村经济发展积累过程形态分析

了解中国农村经济发展的经济结构对认识中国农村经济有着特殊的意义。本文旨在通过全国30个省、自治区和直辖市经济发展的比较研究,运用值域分析、方差分析和回归分析方法、探索出全国经济积累过程的发展形态,总结出地区伴随着经济发展过程而出现的特有经济结构现象,寻找问题症结的所在和解决问题的突破点,为国家经济提供建设性参考建议。     

本振相位噪声对接收机性能的影响

针对工程中本振相位噪声对接收机的影响常难以估量的问题,提出利用等效相位白噪声谱密 度进行评估的方法。通过分析相位噪声的产生及其对剩余误码率和动态范围的影响机理,仿 真了不同相位噪声对误码率的影响程度。通过仿真结果可知：相位噪声对接收机性能具有重 要的影响,工程中应针对不同的应用场合要求合理的相位噪声指标。     

地震检波器使用中应注意的几个问题

地震检波器是用于地震勘探的专用振动传感器,其性能对地震记录的品质有着重大影响。结合野外勘探对地震检波器的实际需求,探讨了检波器的假频,失真系数,允差,温度性能及可靠性等技术指标,为用户选用合适的检波器提供了更为完善的依据。     

日本国债：安全资产还是风险潜在

金融危机后全球安全资产减少,使日本国债地位相对改善,成为世界金融市场的避险工具之一.但由于日本长期政府债务净额占GDP之比居高不下,国债信用已经受到损毁,主权信用评级被下调.从日本国内资金循环特征看,日本企业转变为储蓄大户的原因不是企业的盈利增加,而是由于资产负债表的调整压力使投资意愿降低,因此,日本政府只有不断增发国债来弥补筹资主体的缺位;日本国债期限结构的短期化和日益增长的利息负担使其面临的市场利率风险加大;同时,日本中央银行购买国债规模膨胀,不断冲击财政纪律的约束,财政赤字货币化风险隐藏其中.长期来看,日本泡沫经济崩溃后,资产去泡沫化的过程也正是国债泡沫化的过程.在日本财政改革前景未见定论,企业的投资需求尚未恢复的前提下,日本国债的可持续性存在巨大风险.