融合多特征信息的模式识别方法

研究在有不确定知识的情况下,融合多种类特征信息的模式识别方法.首先用模糊逻辑表示关于对象的具有不确定性的经验知识,通过模糊推理可由特征得到关于对象的用基本概率分配函数表示的具有不确定性的模式分类信息.然后利用D-S证据推理的方法最大程度地消除其不确定性,得到对象的最终分类结果.仿真结果证明,该方法对处理具有多种类特征且带有不确定性知识的一类模式识别问题是有效的.通过将模糊推理方法与证据理论的结合能有效利用多种特征的不确定知识对目标进行分类.

A New Pattern Recognition Method Based on the Fusionof Multiple Feature Information

Studies methods of pattern recognition fusing multi feature information in the presence of imprecise knowledge. Imprecise knowledge related with the object is first expressed with fuzzy logic rules, and raw information about object class expressed as BPA is obtained through applying fuzzy inference to multiple features. The final recognition result is then achieved making use of the Demster Shafter (D S) theory to eliminate the imprecision of BPA as much as possible. The simulation results prove that the recognition method presented here is effective to solve multi feature fusion recognition problems with the existence of imprecise knowledge. The combination of fuzzy logic and D S theory can effectively utilize the imprecise knowledge to classify the objects.