竞争算法优化BP神经网络性能研究

针对诸多群智能算法容易陷入局部最优、收敛速度慢的特点,提出一种参数设置少,全局搜索能力强的竞争算法.通过10个基准函数与粒子群算法的比较, 30次试验下竞争算法的平均值与最小值均优于粒子群算法,验证了该算法的有效性.用竞争算法优化BP神经网络,并对11个测试数据集进行分类,实验结果表明,用竞争算法优化后的BP神经网络在11个测试集上性能均优于原始算法,且在大部分测试集上性能优于用遗传算法优化的BP神经网络.该算法能有效提高分类正确率,增强鲁棒性.

Performance Study of BP Neural Network Based on PK Algorithm

Aiming at the characteristics that many cluster intelligent algorithms are easy to fall into local optimum and have slow convergence rate, a new algorithm (PK algorithm) with less parameter settings and strong global search ability is proposed. The comparison of 10 benchmark functions with particle swarm optimization algorithm verifies the effectiveness of the algorithm, because the average and minimum values of the PK algorithm under 30 trials are better than the particle swarm optimization algorithm. Then using the PK algorithm to optimize the BP neural network, and 11 test data sets were classified. The experimental results show that the BP neural network based on PK algorithm has better performance than the original algorithm on 11 test sets, and the performance is superior to BP neural network based on genetic algorithm on most test sets. Thus, we conclude that the BP neural network based on PK algorithm can effectively improve the classification accuracy and enhance the robustness.