基于粒子群优化的直觉模糊核匹配追踪算法

Research of PSO-Based Intuitionistic Fuzzy Kernel Matching Pursuit Algorithm

针对现有直觉模糊核匹配追踪算法采用贪婪算法搜索最优基函数而导致学习时间过长的问题,汲取了粒子群优化算法全局搜索能力强、收敛速度快的优势对最优基函数的搜索过程进行优化,提出了一种基于粒子群优化的直觉模糊核匹配追踪算法,并将该算法应用于时效性要求更高的空天目标识别领域.实验结果表明,与传统方法相比,本文方法在识别率相当的情况下有效缩短一次匹配追踪时间,计算效率明显提高,且所得模型具有稀疏性好,泛化能力高等优点,特别适用于兼顾识别率和实时性的应用领域.

In order to overcome the long learning time caused by searching optimal basic function data based on greedy strategy from a redundant basis function dictionary for the Intuitionistic Fuzzy Kernel Matching Pursuit（ IFKMP）,the particle swarm optimization algorithm with powerful ability of global search and quick convergence rate is applied to speed up searching optimal basic function data in function dictionary. And the approach of intuitionistic fuzzy kernel matching pursuit based on particle swarm optimization algorithm,namely PS-IFKMP,is proposed. This algorithm is applied to the aero target recognition,which requires real-time ability. Simulation results showthat,compared with the conventional approaches,the proposed algorithm can decrease training time and improve calculation efficiency obviously leaving the classification accuracy almost unchanged,while the model has better sparsity and generalization. It is also demonstrated that this approach is much suitable to the application requiring both accuracy and efficiency.