关键词: 自组织神经网络, 卡尔曼滤波器, 无先导变换

Abstract: The principal goal of the self-organizing feature map is to transform an incoming signal pattern of arbitrary dimension into a one- or two-dimensional discrete map，and to perform this transformation adaptively in a topologically ordered fashion.The learning process is controlled by learning coefficient and the width of neighborhood function，which have to be chosen empirically because there aren’t exist rules or a method for their calculation.To improve the learning ability of the self-organizing maps，a method is presented，which the learning coefficient and the width of neighborhood function is predicted by linear Kalman fitler and the Kalman filter based on the unscented transform respectively.

Key words: self-organizing feature map, Kalman filter, unscented transform