Intuitionistic Fuzzy Kernel Matching Pursuit Based on Particle Swarm Optimization for Target Recognition

Abstract

In order to overcome the long training time caused by searching optimal basic functions 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. 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 aerospace target recognition, which requires real-time ability. Simulation results show that, compared with the conventional approaches, the proposed algorithm can decrease training time and improve calculation efficiency obviously with almost unchanged classification accuracy, while the model has better sparsity and generalization. It is also demonstrated that this approach is suitable for the application requiring both accuracy and efficiency.