基于GASA-FCM混合聚类与霍夫变换的欠定混合矩阵估计

针对模糊C-均值聚类(fuzzy C-means clustering，FCM)算法在欠定混合矩阵估计中精度低、鲁棒性差的缺点，提出一种基于遗传模拟退火优化FCM(GASA-FCM)混合聚类和霍夫变换的欠定混合矩阵估计算法。该算法首先结合了模拟退火算法(simulated annealing algorithm，SA)全局搜索、高精度的优点和遗传算法(genetic algorithm，GA)强大的空间搜索能力，将经遗传模拟退火算法得到的聚类中心点赋给FCM，避免了初值选择的随机性。再利用霍夫变换对聚类得到的每一类数据的中心进行修正，提高混合矩阵的估计精度。实验结果表明，提出的算法明显改善了算法的稳定性和混合矩阵估计精度，具有一定的有效性和可行性。

Underdetermined mixing matrix estimation algorithm using GASA-FCM based mixing clustering and Hough transform

With regards to the low precision and poor robustness of fuzzy C-means clustering algorithm (FCM) in the underdetermined mixing matrix estimation, this paper proposed an algorithm using genetic simulated annealing optimized FCM(GASA-FCM) based mixed clustering with hough transform to solve underdetermined mixing matrix estimation. It could combine the global search, high-precision advantages of simulated annealing algorithm(SA) and powerful search ability of space of genetic algorithm(GA) , FCM assigned the clustering center point obtained by genetic simulated annealing algorithm, which avoided the randomness of initial selection. The center of each kind of data obtained by clustering by hough transform is modified to improve the estimation accuracy of the mixing matrix. Experimental results show that the proposed algorithm significantly improves the stability of the algorithm and the accuracy of the mixing matrix estimation, and has certain validity and feasibility.