新的大群体一致性学习修正决策方法

New method of huge group-consistency amendment with learning ability

针对某决策方案,提出了一种基于聚类算法、且能面向较大规模群体、考虑成员学习进化能力并有效收敛群体意见的群体一致性修正方法。首先设计了群体成员学习进化决策程序;接着,借助一种能够处理大数据量聚类的C-均值类型聚类算法,通过梯度下降法逐渐修正群体一致性来避免因个别成员意见偏离太大而引起的群决策失误;最后通过计算机仿真和实验对比分析验证了该方法的正确、有效性。

According to a certain decision project, a group-consistency amendment method based on a kind of optimized c means clustering algorithm, aiming at lager scale group, and considering the ability of evolution by learning is presented. First, the evolutionary program of the group member is given. Then, a gradual decision method of amendment group-consistency by optimizing attributes ＇ weight is presented to avoid the fault caused by individual idea divergence. Lastly, the velidity of this method is proved by the computer simulation and comparing analysis.