Parameters optimization of synergetic neural network based on immunity clonal algorithm

Due to the shortages of optimization algorithms available in synergetic neural network (SNN), an algorithm of parameters optimization on immunity clonal algorithm (ICA) was proposed here. Compared with the algorithm under balanced attention parameters and that under unbalanced attention parameters on genetic algorithm (GA) and simulated annealing algorithm (SA), the new algorithm has the global and local searching ability and is not easy to get into local optima. And the iterative step is adjusted adaptively. Experiments on textural images and remote sensing images show that the proposed algorithm has not only faster convergent speed but also better classification performance. Simultaneously, the effect of attention parameters and all parameters on the competition of prototype patterns is verified and then better recognition result can be obtained.