An algorithm of adaptive fuzzy Kalman filtering is presented.
提出了一种模糊自适应卡尔曼滤波算法。
In order to resolve the shortcomings of the traditional Kalman filtering, a new method is presented in which the fuzzy reasoning system is combined with the traditional Kalman technology.
为了解决常规卡尔曼滤波法存在的不足，给出了用模糊推理系统与卡尔曼法相结合的方法。
Considering the radiation problem of filtering resulting from the inaccurate model of the Kalman filter, data fusion algorithm based on the fuzzy Kalman filter is advanced.
并针对卡尔曼滤波因模型不准确而导致的滤波发散问题，提出了模糊卡尔曼的数据融合算法。
A method of fuzzy identification based on genetic soft fuzzy clustering and Kalman filtering method is proposed.
提出了一种新的基于遗传模糊软分类和卡尔曼滤波方法的模糊辨识算法。
The input space of fuzzy system is partitioned by means of real time recursive fuzzy clustering, and the parameters of fuzzy model are confirmed by Kalman filtering.
利用递推模糊聚类算法实时对系统的输入空间进行模糊划分，利用卡尔曼滤波算法确定参数。
In order to resolve the shortcoming of the traditional federal Kalman filtering, a new method is presented in which the fuzzy reasoning system is combined with the traditional Kalman technology.
为了解决常规卡尔曼滤波法存在的不足，给出了用模糊推理系统与卡尔曼法相结合的方法。
First, the fuzzy space of input variables is partitioned by means of on-line fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm.
首先，利用在线模糊竞争学习方法划分输入变量的模糊输入空间，然后利用卡尔曼滤波算法估计模糊模型的参数。
First, the fuzzy space of input variables is partitioned by means of on-line fuzzy competitive learning. Further, the parameters of fuzzy model are estimated by means of Kalman filtering algorithm.
首先，利用在线模糊竞争学习方法划分输入变量的模糊输入空间，然后利用卡尔曼滤波算法估计模糊模型的参数。
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