一种基于实数粗集空间的自组织映射方法及在模式识别上的应用

提出一种实数粗糙集,避免了经典粗糙集必须经过离散化处理的环节;并且用实数粗糙集的下、上近似集的精确概念划分自组织映射的输出结果,使得修改后的映射结果中各类样本点之间有明显的间隔,易于进行分类识别.最后通过对某型歼击机的舵面故障的模式识别仿真验证了其方法的正确性和有效性.

Self-organizing Map Method Based on Real Rough Sets Space and Its Application of Pattern Recognition

This paper presents a real rough sets space and corresponding concepts of real lower and upper approximation sets which correspond to the real-valued attributes. Therefore, the real rough sets space can be investigated directly. A rhombus neighborhood for SOM is proposed, and the combination of SOM and rough sets theory is explored. According to the distance between the weight of winner node and the input vector in the real rough sets space, new weight learning rules are defined. The modified method makes the classification of the output of SOM clearer and the intervals of different classes larger. Finally, an example based on fault identification of an aircraft actuator is presented. The result of the simulation shows that this method is right and effective.