随机模糊神经网络模型预测铁水硅含量

该文在考虑了具有模糊化和非模糊化的模糊逻辑系统用于高炉铁水含硅量[Si]时各种噪声干扰的同时,把模糊数学理论和随机系统理论结合在一起,建立了一种新的高炉铁水含硅量[Si]的智能预报模型,该模型是由非单值模糊化、模糊规则库、模糊推理机、特殊非模糊化构成的随机模糊神经网络逻辑系统.通过结合莱钢1号高炉实时在线采集的生产数据,进行仿真研究,结果表明,该套模型能够很好地预测高炉铁水含硅量[Si].     

中国对外直接投资与逆向技术外溢的关系——基于熵和耗散结构理论

基于一国技术进步是耗散结构的假设,构建了我国对外直接投资与国内技术进步之间的灰关联熵模型,并运用2003—2010年的数据实证检验了逆向技术外溢效应的存在性。结果表明:2003—2008年期间,我国对外直接投资对国内技术进步并没有持续、显著的作用,此期间并不存在逆向技术外溢;2009年和2010年我国的对外直接投资给国内技术进步带来了负熵流,此期间存在逆向技术外溢,并且它推动了国内技术向良性方向发展。该结果意味着,对外直接投资是一把"双刃剑",有可能增加我国技术进步的熵值,也可能导致负熵流入、促进我国的技术进步。最后提出,我国企业不能盲目进行对外直接投资,应质量和数量并重,以使对外直接投资为我国带来持续的负熵流。     

"农户+公司"型农产品供应链协同机制研究

文章在市场价格不确定的环境下,构建了一个农户—公司灰色博弈模型并借此探讨了农户+公司型农产品供应链在动态价格下的协同稳定机制,最后通过实例说明了协同机制模型的合理性与有效性。     

基于全寿命周期分析的电力设备费用模型

针对我国供电企业的实际情况,将电力设备费用分为一次投资费用和运行维护费用两部分,建立了电力设备全寿命周期费用定量模型。针对一次投资费用的特点,建立了基于时间分摊的一次投资费用模型;针对运行维护费用长期发生的特点,建立了基于灰色关联度确定分布类型的运行维护费用模型。同时,将两个费用模型综合考虑,建立了供电企业电力设备全寿命周期费用模型,据此可计算电力设备全寿命周期最小费用和最佳使用寿命。最后,以某变电站综合自动化项目为例,验证了该模型的有效性。     

河南省耕地与粮食变化分析及预测

河南省是我国重要的粮食生产基地,其稳定的耕地资源对粮食生产具有重要意义.本文利用河南省29年耕地资源和粮食产量数据进行分析,研究了粮食生产和耕地资源的变化特征.结果表明,随着河南省人口的不断增长,耕地资源在不断减少,其人均耕地面积也在不断下降;河南省粮食总产量和单产极不稳定,给社会经济发展和粮食安全带来了隐患.最后,建立GM(1,1)预测模型,对2010年、2015年和2020年粮食产量、人均耕地等分别进行了预测.针对预测结果,提出了稳定、提高耕地资源和粮食生产的具体建议与对策,为政府正确决策提供了科学依据.     

江西能源消费结构与经济发展的灰色关联分析——基于低碳经济的视角

能源是人类赖以生存和发展的重要物质基础,能源消费与经济发展有着十分密切的关系。江西作为能源资源匮乏的省份,发展低碳经济是弥补劣势的重要手段。利用灰色关联分析方法具体分析了江西省能源消费结构与经济发展的关系。结果表明,江西省应改善以煤炭为主的能源消费结构,大力发展低碳经济,逐步优化产业结构和能源消费结构。     

Forecasting of electricity costs based on an enhanced gray-based learning model: A case study of renewable energy in Taiwan

This work presents a novel gray-based cost efficiency (GCE) model that integrates the gray forecasting model into a two-factor cost efficiency curve model for renewable energy (RE) technologies and identifies the optimal forecasting model for power generation cost of RE technologies. The analytical framework of proposed GCE model improves short-term prediction of power generation cost, and can be applied during the early developmental stages for RE technologies. Empirical analysis is based on wind power data for Taiwan. Time lag of knowledge stock was simulated to represent the actual relationship between R&D expenditures and cost reductions in power generation by knowledge stock. Analytical results demonstrate the GCE model is a useful tool to quantify the influences of cost reductions in power generation. The implications of analytical results are that institutional policy instruments play an important role in RE technologies achieving cost reductions and market adoption. The proposed GCE model can be applied to all high-technology cases, and particularly to RE technologies. The study concludes by outlining the limitations of the proposed GCE model and directions for further research.     

ARIMA计量技术下河南省农民增收的预测模型研究

笔者根据河南省1978年～2005年的农民人均纯收入统计数据,将这些数据进行平稳化、零均值化处理,并利用时间序列的自相关函数,偏自相关函数的性质,确认数据所适合的模型.通过对样本数据所做的回归拟合模型定量分析,从而为河南省"十一·五"规划提供量化指标依据.     

Excessive Volatility is Also a Feature of Individual Level Forecasts

The excessive volatility of prices in financial markets is one of the most pressing puzzles in social science. It has led many to question economic theory, which attributes beneficial effects to markets in the allocation of risks and the aggregation of information. In exploring its causes, we investigated to what extent excessive volatility can be observed at the individual level. Economists claim that securities prices are forecasts of future outcomes. Here, we report on a simple experiment in which participants were rewarded to make the most accurate possible forecast of a canonical financial time series. We discovered excessive volatility in individual-level forecasts, paralleling the finding at the market level. Assuming that participants updated their beliefs based on reinforcement learning, we show that excess volatility emerged because of a combination of three factors. First, we found that submitted forecasts were noisy perturbations of participants’ revealed beliefs. Second, beliefs were updated using a prediction error based on submitted forecast rather than revealed past beliefs. Third, in updating beliefs, participants maladaptively decreased learning speed with prediction risk. Our results reveal formerly undocumented features in individual-level forecasting that may be critical to understand the inherent instability of financial markets and inform regulatory policy.     

Liberalization,Regulatory Delays and Vulnerability to Systemic Banking Crisis

This work aims at contributing to the improvement of the early warning systems of banking crises using a new approach accounting for model uncertainty. We show that a multinomial logit model based on Bayesian model averaging (BMA) is a good strategy to predict banking crisis. To do this, we argue that differences in vulnerability to banking crisis can be largely explained by an asymmetry between financial market evolution and regulation update on a sample of 49 developed and developing countries between 1980 and 2010. When markets are liberalized, competition pushes bankers to take more risks and take advantage of regulatory delays thus increasing crises probabilities. Our empirical evidence supports that crisis probability is higher in country liberalizing their banking system when regulation is not updated. We developed an early warning system for systemic banking crises based on the multinomial logit model. Its main difference to existing prediction models and its contribution to the literature is that it is intended to identify and resolve what is called by Bussiere and Fratzscher [(2006). Towards a new early warning system of financial crises. Journal of International Money and Finance, 25(6), 953–973] as post-crisis bias in binomial models and to develop a new methodology of leading indicators selection based on BMA. Overall, our model predicts all banking crises during our sample period.