沿空留巷联合支护技术研究

基于巷旁支护前期的切顶作用以及后期支护体的高支撑力的要求,通过综合技术比较和经济比较,提出了一种将切顶支柱和间隔＂编织袋＂混凝土墙联合布置使用的方案。＂编织袋＂混凝土在充填采空区提供高强支撑力的同时可以有效防止采空区矸石冲击切顶支柱。工程实践表明,在采场顶板坚硬出现悬顶的中厚煤层中,该方案是一种理想的沿空留巷技术。     

混沌高效遗传算法在水库含沙量预报中的应用

利用混沌映射的遍历性和实编码遗传算法的全局优化性,通过在遗传进化过程中加入混沌变异操作,在变量的定义域内投放大量的混沌初始群体,在实编码遗传算法进化过程中加入单纯形法学习算子,建立了一种新的混沌高效遗传算法(chaos higher efficient genetic algorithm, CHEGA).应用该法对3个非线性、高维、多峰值测试函数进行了仿真,在收敛速度和全局优化方面好于现有的简单遗传算法和改进的遗传算法.建立了水库含沙量预报模型.并将CHEGA用于求解上述模型的参数优化问题,与实数编码加速遗传算法(RAGA)、二进制加速遗传算法和随机优化算法等方法相比,CHEGA可以遍历到整个区域,较好的保持了种群的多样性,并且精度高、收敛速度快.CHEGA对求解实际水库计算模型的参数优化问题非常有效.     

高应力软岩下矿井巷道支护

随着煤矿开采深度的不断增加,井下煤矿巷道将处于更高的地应力环境中,尤其在地质构造活动强烈的地区,井下巷道支护及稳定性更加难以保证.研究高地应力软岩环境下巷道科学的支护方式是保证煤矿采掘深部煤层的关键.就高应力软岩的基本概念及形成条件进行了讨论,在掌握高应力软岩巷道的变形特征和支护原理的基础上,以芙蓉矿区白皎煤矿井下高应力软岩巷道支护为对象,对3种不同的支护方式进行了研究与实践,结果表明预留刚隙柔层支护方式为最佳的适合高应力软岩条件下的巷道支护方式.     

枯季径流预报探讨

在分析研究流域气候特征及枯季径流来水规律的基础上,用水文方法分别建立了枯水期月径流预报模型。水文方法是利用水文序列资料建立自回归模型,用实测资料对预报模型进行了验证。     

模拟和数字电视覆盖区方法的计算与分析

本论文通过对几种不同的预测电视覆盖的方法的原理介绍,进而时比各种方法的优劣程度,并且对每种方法可能存在的问题加以分析,提出在规划台站时选用最适合的覆盖分析方法.     

非能量费用影响下的冷热电联供产品成本符号(火用)分析

以符号(火用)经济学为基础构建了冷热电联供系统的产品成本分配模型.该模型同时考虑了能量费用和非能量费用,通过计算产品形成过程中的能量(火用)耗和非能量投入来获得最终成本.实例分析表明,该模型不仅能给出详细的各股(火用)流价值,而且能正确评估非能量费用的参与程度,从而客观反映了冷热电3种产品的经济成本.通过对大型冷热电联...     

模糊推理在导线间串扰预测中的应用

提出应用模糊逻辑推理对互连导线间串扰问题进行预测的方法,从前期测量数据中选择有效的电磁干扰参数作为预测因子,并根据数据的统计特性对其分级,然后归并模糊蕴涵式语句确定模糊蕴涵关系集。该方法与人工神经网络预测方法相比,不仅可以充分利用原有的专家的经验和知识,而且能够从实际数据中自动总结和提取新的推理规则。实例分析表明该方法对导线间串扰问题的预测是可行的。     

混沌算子网络在经济数据预测中的应用研究

采用多个混沌算子单元组成一种新的预测网络,实现经济数据的预测分析.利用已知数据构造出训练样本,通过调节混沌算子单元的控制参数来控制其动力学特性,以此改变预测网络的动力学行为,使预测网络的动力学特性逐渐逼近被预测系统,并随之一致变化,从而实现时间序列的动态预测分析.利用该方法对国内生产总值和国民总收入等经济数据进行了预测...     

Forecast Combination and Bayesian Model Averaging: A Prior Sensitivity Analysis

In this study we evaluate the forecast performance of model‐averaged forecasts based on the predictive likelihood carrying out a prior sensitivity analysis regarding Zellner's g prior. The main results are fourfold. First, the predictive likelihood does always better than the traditionally employed ‘marginal’ likelihood in settings where the true model is not part of the model space. Secondly, forecast accuracy as measured by the root mean square error (RMSE) is maximized for the median probability model. On the other hand, model averaging excels in predicting direction of changes. Lastly, g should be set according to Laud and Ibrahim (1995: Predictive model selection. Journal of the Royal Statistical Society B 57 : 247–262) with a hold‐out sample size of 25% to minimize the RMSE (median model) and 75% to optimize direction of change forecasts (model averaging). We finally apply the aforementioned recommendations to forecast the monthly industrial production output of six countries, beating for almost all countries the AR(1) benchmark model. Copyright © 2011 John Wiley & Sons, Ltd.     

How Informative are the Subjective Density Forecasts of Macroeconomists?

In this paper, we propose a framework to evaluate the subjective density forecasts of macroeconomists using micro data from the euro area Survey of Professional Forecasters (SPF). A key aspect of our analysis is the use of evaluation measures which take account of the entire predictive densities, and not just the probability assigned to the outcome that occurs. Overall, we find considerable heterogeneity in the performance of the surveyed densities at the individual level. However, it is hard to exploit this heterogeneity and improve aggregate performance by trimming poorly performing forecasters in real time. Relative to a set of simple benchmarks, density performance is somewhat better for GDP growth than for inflation, although in the former case it diminishes substantially with the forecast horizon. In addition, we report evidence of an improvement in the relative performance of expert densities during the recent period of macroeconomic volatility. However, our analysis also reveals clear evidence of overconfidence or neglected risks in expert probability assessments, as reflected in frequent occurrences of events which are assigned a zero probability. Copyright © 2014 John Wiley & Sons, Ltd.