基于改进二进制萤火虫的BP神经网络并行集成学习算法*

针对传统BP神经网络的随机初始权值和阈值易导致网络学习速度慢、容易陷入局部解及运算精度低等缺陷,提出基于改进二进制萤火虫算法(IBGSO)的BP神经网络并行集成学习算法.首先构建以高斯变异函数作为概率映射函数的IBGSO,并从理论上分析算法的有效性.然后结合IBGSO与BP神经网络构建并行集成学习算法,并将算法应用于农业干旱灾害评估中.实验表明,相比传统算法,文中算法在计算速度及精度方面更优,可以提高旱情等级评估的准确性.

Parallel Ensemble Learning Algorithm Based on Improved Binary Glowworm Swarm Optimization Algorithm and BP Neural Network

The traditional back propagation(BP) neural network has low learning speed and calculution accuracy and it is easy to fall into local solution. Aiming at these defects, a parallel ensemble learning algorithm based on improved binary glowworm swarm optimization algorithm(IBGSO) and BP neural network is proposed. Firstly, a kind of improved binary glowworm swarm algorithm is constructed based on Gauss variation function as probability mapping function, and the validity of the algorithm is analyzed theoretically. Secondly, The IBGSO algorithm and BP neural network are combined to construct a parallel ensemble learning algorithm. Finally, the parallel ensemble learning algorithm is applied to the assessment of agricultural drought disaster. The experimental results show that the algorithm has advantages over the traditional algorithms in terms of convergence speed and operation accuracy. Therefore, IBGSO-BP algorithm can effectively improve the accuracy of agricultural drought assessment.