作　　者： ; ;

机构地区： 四川大学工商管理学院

出　　处： 《系统工程理论与实践》 2004年第7期1-14,共14页

摘　　要： 　将粗糙集的概念引入到GMDH(GroupMethodofDataHandling)数据挖掘中,将前馈网络中各层各模型的系数估计,转化为不定性规划和线性规划,构建了建模研究的粗糙GMDH方法,从而在一定程度上增强了复杂系统模型的分析和预测能力.借助于决策者的主观经验对信息的补充、完善及整合,甚而对样本容量的要求可降低到仅为1的水平,拓宽了不定性复杂系统的小样本建模能力.同时,考虑到复杂系统本身对人机交互的自然要求和决策者主观经验和偏好的挖掘问题,在粗糙GMDH中引入了实验方法,设计出探索复杂系统研究的RGMDH(RoughtGroupMehtodofDataHandling)人机交互挖掘系统,由系统的运行为决策者在模型精度、信息冗余、信息缺失风险三重抉择中提供更完整的信息,从而解决决策者信息偏好非结构化而导致的三重抉择难以做出的问题.最后,本文对动态环境中高技术公司核心能力与经济绩效关系进行了探讨,作为粗糙GMDH模型的算法例释. The notion of rough set is leaded into GMDH(Group Method of Data Handling) for the first time. At the same time, the estimation method for coefficients in each model of each level of front-propagation network is turned from traditional regression into uncertain programming and linear programming. By means of this transform, such rough GMDH(Rought Group Method of Data Handling) is proposed that to increase the analyzing ability of modeling and mid-term or long-run prediction ability. By means of DM's subjective experience, the information needed by modeling can be complete, which may decrease the demand for sample set size, even may decrease to the size with only one sample. In addition, for mining the DM's subjective preference and achieving man-machine interaction, one man-machine interactive system with trial mechanism is designed to provide the DM more complete information of results, which may provide a good decision support for the DM to reduce the difficulty when meeting in three-fold problem including accuracy, information redundancy and scarcity. Finally, a simple example researching the relation between company core competence and economic performance is proposed to explain RGMDH modeling.

关 键 词： 不定性 复杂系统 信息偏好 人机交互 模型

领　　域： [经济管理] [经济管理] [自然科学总论]