基于新的AFCM算法的陀螺仪漂移预测

摘要

为克服模糊规则提取的盲目性和随机性,提出了一种基于新的自适应模糊C-均值聚类(AFCM)算法的T-S 模糊建模方法.首先利用减法聚类来确定聚类数目的上限和初始聚类中心,然后采用改进的模糊C-均值聚类(FCM).算法进一步优化聚类中心,最后通过聚类有效性评判方法自适应地确定规则数及聚类中心,同时改进的FCM算法也克服了野点数据对聚类结果的影响;进而利用加权最小二乘法估计模糊模型的结论参数.用于某型陀螺仪漂移趋势预测中,能够自适应地确定模糊规则个数,并取得了较高精度.仿真实验结果验证了该方法的有效性和可行性.%In order to avoid the blindness and randomness in extracting fuzzy rules, an approach for constructing T-S fuzzy models was proposed on the basis of a new Adaptive Fuzzy C-means (AFCM) Clustering Algorithm. Firstly, subtractive clustering was utilized to determine the upper limit of clustering number and the initial clustering centers. Then an improved Fuzzy C-means clustering algorithm was adopted to optimize the clustering centers. Finally, the number of fuzzy rules and the clustering centers were confirmed adaptively through clustering validity method. On the other hand, the improved Fuzzy C-means clustering algorithm could eliminate the influence of noise on the clustering result. In the constructed model, the unknown parameters in consequent terms were identified by the weighted least square method. A gyroscope's drift data was used to demonstrate the detailed implementation of the proposed method, for which the number of fuzzy rules was confirmed adaptively and the accuracy of the prediction result was high comparatively. The results show the effectiveness and feasibility of the algorithm.