Underwater Target Recognition Using Artificial Fish-Swarm Algorithm

In order to decrease negative effects brought by the particularity and complexity of imaging environment, and satisfy the real-time need of the underwater task, combined invariant moments are extracted as recognition features. Furthermore, an underwater target recognition system based on neural network which improved by Artificial Fish Swarm Algorithm (AFSA) is proposed. AFSA is of capable of attaining global optimum which can speed up converging velocity of pure BP neural network, and avoid tending to get into its local optimum. In our experiments, different kinds of targets images are used to test in the proposed recognition system, so as to some other optimized algorithm trained recognition system. Experimental results show that the proposed system is of well clustering and high classified accuracy.