比较法在房地产开发中的应用

对"比较法"原理和应用情况进行了分析,说明其在房地产开发中能够科学地预测房地产的价格和收益,从而提高财务分析的科学性和权威性,为项目决策提供科学依据,以促进"比较法"在房地产开发中的应用。     

Data mining of resilience indicators

In recent years, the Asia-Pacific region has experienced several financial setbacks, including speculative attacks in 1998 and the SARS outbreak in 2003. Financial stresses of this nature are unanticipated, and not all of the dangers can be predicted by the examination of market information and macroeconomic indicators. The Early Warning System (EWS) that has been adopted by the International Monetary Fund may not be able to predict future financial crises for all possible scenarios, because shocks come in many different forms. To supplement the EWS, this paper proposes a data mining framework to measure the resilience of an economy. The resilience framework does not predict a crisis, but rather assesses the current state of health of an economy and its ability to withstand a financial shock should one occur. The framework is based on a feedback system consisting of two stages. The first stage assigns a resilience score to each economy based on a fuzzy logic scoring scheme that is built on the ambiguous reasoning of experts. The second stage uses the classification tree approach to estimate thresholds for each economic indicator, and examines the quality of the fuzzy score. The result from the second stage is then passed back to the first stage as feedback. The final result is obtained when the feedback system reaches its equilibrium state. The proposed resilience framework is applied to the external-sector and the public-sector economies of several countries to illustrate its applicability.     

Strategic appraisal of energy models for Mozambique

The needs that an energy supply system must meet are constantly changing, due to technological, social and political reasons. Effective energy planning is a dynamic process that is repeated periodically and adjusts to changing conditions. Energy decision makers and planners are no longer able to rely on inductive decision making since they have to investigate the effect of various decision parameters and possible future changes. To help in this process, models have been developed where estimates of future load growth, candidate power plants, fuels and other key factors can be introduced, from which the planners can evaluate decision parameters and the available alternatives. The paper presents the different methodologies and practices that are used by 11 energy models for energy demand forecasting, supply side management and generation expansion planning, demand side management and integrated resource planning. The paper concludes to the presentation of a strategic appraisal of the examined energy models appropriate for energy planning in Mozambique. Three models are proposed for conducting demand forecasting, generation expansion planning and demand side management. Copyright © 2002 John Wiley & Sons, Ltd.     

Forecasting of Turkey's net electricity energy consumption on sectoral bases

In this study forecast of Turkey's net electricity energy consumption on sectoral basis until 2020 is explored. Artificial neural networks (ANN) is preferred as forecasting tool. The reasons behind choosing ANN are the ability of ANN to forecast future values of more than one variable at the same time and to model the nonlinear relation in the data structure. Founded forecast results by ANN are compared with official forecasts.     

道路交通状态预测研究

针对道路交通状态预测的研究成果,综述了基于实时数据分析的道路交通状态预测理论和方法的相关研究.从分析基于实时数据分析的道路交通预测的基本流程、特点和要求和单一断面交通状态预测和多断面交通状态预测角度回顾了道路交通状态预测的研究现状.结果表明:在不断发展完善单一断面预测方法的同时,需要发展以多个断面交通状态为研究对象的多断面预测方法,以促进我国智能交通系统的基础理论发展及其应用水平的提高.     

Effective prediction of DEA model by neural network

In this paper,a fast neural network model for the forecasting of effective points by DEA model is proposed,which is based on the SPDS training algorithm.The SPDS training algorithm overcomes the drawbacks of slow convergent speed and partially minimum result for BP algorithm.Its training speed is much faster and its forecasting precision is much better than those of BP algorithm.By numeric examples,it is showed that adopting the neural network model in the forecasting of effective points by DEA model is valid.     

石油企业财务风险评价神经网络模型的建立

石油企业财务风险评价是石油行业的重要课题.在确立石油企业财务风险评价指标体系的基础上,建立了基于模糊神经网络的石油企业财务风险评价模型.该评价模型分为模糊量化模块和模糊神经网络(FNN)模块,可根据实际问题进行具体调节,具有学习的功能.经过对200多家石油企业的实证分析,结果表明该模型网络预测误差小,是一种可靠、有效的...     

海洋平台灰预测和支持向量机的逆控制

导管架式海洋平台在随机波浪等外载荷作用下极易产生有害振动,且其动力响应具有极强的非线性和时变性,采用被动控制方法和基于精确数学模型的主动控制方法控制海洋平台的有害振动很难达到理想的控制效果.结合灰预测和支持向量机的优点,提出了基于灰预测和支持向量机的自适应逆控制方法,并将其与动态刚度阵法相结合应用于导管架式海洋平台的振动主动控制中.数值算例分析表明,该控制方法可有效地控制波浪和风载荷作用下引起导管架式平台的有害振动,并能解决由于控制信号传输等原因引起的时滞问题.     

用聚类预报模型预报三峡水库来水量的探讨

为了开展三峡水库入库来水量长期预报,从北半球500 hPa高度场、太平洋海温场、北半球100 hPa高度场、北半球地面气压场与74项环流特征指数作为预报因子,采用回归方程求解和回归效果检验,建立回归聚类预报物理模型,对三峡水库6～8月各月平均来水量进行了长期预报分析。结果表明,三峡水库6~8月份各月来水量回归聚类预报的技术路线是可行的,正、负距平符号一致的概率能达到80%以上,效果较好,基本上可以满足生产需要。回归聚类预报物理模型具有一定的应用推广价值。     

短期负荷预测Volterra滤波器模型

讨论依据相空间邻近轨道演化相似性的特点构造训练模式,建立短期负荷预测Ｖｏｌｔｅｒｒａ滤波器模型的问题．以距离相似度和趋势相似度来衡量电力负荷吸引子轨道的相似度,提出了电力负荷吸引子邻近轨道判别的新方法．从模型训练充分性的角度出发,探讨了模型训练集规模的选择依据．仿真结果表明该模型是有效的．