Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-me...Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.展开更多
OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ...OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ,该算法使用 AF CM方法对数据进行聚类 ,并获取基函数的平滑参数 ,然后使用 OL S方法从聚类结果中选取网络中心。利用实测的 4类飞机目标数据对其进行性能检验 ,试验结果验证了该训练算法可提高网络的训练速度 ,缩小网络规模 ,提高网络的分类能力。展开更多
For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const fa...For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate （CFAR） schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean （FCM） cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.展开更多
基金supported by the National Natural Science Foundation of China for Distinguished Young Scholars(70625005)
文摘Intuitionistic fuzzy sets(IFSs) are useful means to describe and deal with vague and uncertain data.An intuitionistic fuzzy C-means algorithm to cluster IFSs is developed.In each stage of the intuitionistic fuzzy C-means method the seeds are modified,and for each IFS a membership degree to each of the clusters is estimated.In the end of the algorithm,all the given IFSs are clustered according to the estimated membership degrees.Furthermore,the algorithm is extended for clustering interval-valued intuitionistic fuzzy sets(IVIFSs).Finally,the developed algorithms are illustrated through conducting experiments on both the real-world and simulated data sets.
文摘为了提高复杂交通环境下多目标数据关联的实时性与可靠性,本文中基于半抑制式模糊聚类(half suppressed fuzzy cmeans clustering,HSFCM)发展了一种快速多目标车辆跟踪算法。首先对多目标车辆跟踪问题进行了数学描述,并建立了相机像素坐标系与世界坐标系的空间映射关系;其次基于模糊理论将点迹-航迹关联问题转换成量测模糊聚类问题,通过求解各候选量测与聚类中心的模糊隶属度,间接计算出联合概率数据关联(joint probability data association,JPDA)算法中不确定性量测与各目标的关联概率,再利用概率加权融合对多目标状态进行滤波估计;再次在车辆密集工况下通过合理调整卡尔曼增益对量测更新进行抑制,以克服车辆跟踪中目标短暂跟丢问题。实车试验与仿真结果验证了该跟踪算法的可行性与有效性。
文摘OL S训练方法应用在径向基 (RBF )神经网络里时 ,存在当训练数据量很大时速度很慢的问题 ,并且 OL S方法不能自动确定基函数的平滑参数。本文针对此问题提出了一种基于快速模糊 C-均值算法 (A FCM)与 OL S算法相结合的 AF OL S训练算法 ,该算法使用 AF CM方法对数据进行聚类 ,并获取基函数的平滑参数 ,然后使用 OL S方法从聚类结果中选取网络中心。利用实测的 4类飞机目标数据对其进行性能检验 ,试验结果验证了该训练算法可提高网络的训练速度 ,缩小网络规模 ,提高网络的分类能力。
基金This work was supported by the Aeronautical Science Foundation of China under Grand No. 04D52032.
文摘For an airborne Iookdown radar, clutter power often changes dynamically about 80 dB with wide distributions as the platform moves. Therefore, clutter tracking techniques are required to guide the selection of const false alarm rate （CFAR） schemes. In this work, clutter tracking is done in image domain and an algorithm combining multifractal and fuzzy C-mean （FCM） cluster is proposed. The clutter with large dynamic distributions in power density is converted to steady distributions of multifractal exponents by the multifractal transformation with the optimum moment. Then, later, the main lobe and side lobe are tracked from the multifractal exponents by FCM clustering method.
