【作者】 王巍；

【导师】 赵国杰；

【作者基本信息】 天津大学 ， 技术经济及管理， 2008， 博士

【摘要】 随着项目涉及的风险因素日益增多,传统的风险管理方法将难以适应和满足现代项目管理发展的需要,因此项目风险管理相关新理论、新方法的研究迫在眉睫。统计学习理论为项目风险管理向智能化、科学化方向发展提供了一个新的思路。本论文对统计学习理论及其实现算法在项目风险管理中的应用进行了深入地研究,提出了智能化风险评价、预警和预测的实用方法,不仅具有很高的理论价值,而且对项目风险管理的工程实践具有重要的指导意义。特征指标的选择是对项目风险进行正确评价和预警的重要前提。本论文利用距离评判技术对项目的众多风险指标进行量化评判,去除了与评价和预警无关、甚至起消极作用的冗余指标,建立起最敏感的项目风险评价指标体系。案例分析表明,该方法既可以提高项目智能评价和预警的准确性和运算效率,也能够减少未来评价同类项目时的信息采集工作量,从而提高工作效率,节约项目成本。项目风险评价其实质是模式分类问题。但是,项目风险评价又是一个典型的小样本问题,历史数据十分缺乏,而传统的模式识别方法,如神经网络,建立在经验风险最小化的基础上,是依据学习样本数趋于无穷多的假设条件下的最优化结果。因此,在项目风险评价中应用传统的模式识别方法往往得不到理想的分类效果。本论文基于统计学习理论,研究了支持向量机分类方法,提出了基于距离评判和最小二乘支持向量机的智能评价模型,取得了很好的应用效果。重大项目的高风险样本数据非常罕见,历史经验的缺乏使得这类项目更加难以利用传统的方法进行有效评价。本论文将基于支持向量数据描述的单值分类方法引入项目风险预警中,提出了基于距离评判和支持向量数据描述的智能预警模型。该模型仅仅依靠一类低风险项目样本,而不需要或很少需要高风险项目样本就可以训练并建立分类器,进行项目的风险预警。案例分析表明,该方法对于高风险项目样本缺乏条件下的风险智能预警具有十分重要的应用价值。项目风险因素的预测是项目风险管理的又一重要环节,但由于许多项目风险因素其时间序列是非平稳、非线性的,传统的预测方法往往难以准确预测其未来趋势。本论文提出了基于经验模式分解和支持向量回归的混合智能预测模型。工程材料价格预测实例表明,该模型较单一支持向量回归预测而言,其单步和多步预测精度都有很大程度的提高,对准确预测和识别项目风险起到了积极的作用。更多还原

【Abstract】 Because of the increasing risk factors, traditional risk management methods will not adapt to and satisfy the development of modern project management, and it is necessary to study the new theories and methods that related to project risk management. Statistical learning theory (SLT) which is based on the structural risk minimization provides a new idea to the research of intelligent and scientific project risk management. This dissertation researches on project risk management based on the SLT, and some intelligent methods of risk evaluation, early warning and prediction are proposed. It is not only significant for theory study, but also helpful for risk management practice.The choice of feature indicators is important for exact project risk evaluation and early warning. Distance evaluation technique (DET) is introduced to feature indicators extraction in the dissertation. Redundant evaluating indicators which are unrelated or negative to risk evaluation can be extracted and the most sensitive evaluating indicators can be found by DET automatically. The results of the application show that the veracity and efficiency of assessment and early warning will be improved by DET, and it is also available for decreasing the workload of data acquisition and saving project cost.The essential of project risk evaluation is pattern classification. Traditional pattern classification methods, such as neural network (NN) is based on the empirical risk minimization, and is concerned with the machine learning principles under the infinite-sample situation. They are not suitable for project risk assessment which is a typical small-sample problem. Support vector machine (SVM) which is developed in the framework of SLT is a new machine learning algorithm for small-sample problem. A novel intelligent evaluating model based on DET and SVM is presented in this dissertation, and satisfied results are obtained in its application.The high risk data of significant projects is infrequent, and they are more difficult to be evaluated by traditional methods. A one-class classification method called support vector data description (SVDD) is studied, and an intelligent early warning method based on DET and SVDD is also proposed. With this model the risk level can be distinguished only by one-class data of low risk projects. The results of its application show that the method is valuable for early warning with the shortage of high risk data.The time sequence prediction of project risk factors is another key problem of risk management. In most cases, traditional methods can’t obtain the satisfied forecasting results because the time sequences are usually non-stationary and non-linear. A hybrid intelligent forecasting method based on empirical mode decomposition and support vector regression (SVR) is proposed. The example of project material price forecasting reveals that the hybrid model is better than single SVR both in one-step and multi-step prediction.更多还原