基于平均线性粒子群算法的人工神经网络在径流预报中的应用

Application of Artificial Neutral Networks in Runoff Forecasting Based on Mean Linear Particle Swarm Optimization Method

人工神经网络具有很强的非线性处理能力,能够有效地模拟复杂的非线性径流预报过程。传统的基于BP训练算法的人工神经网络具有训练时间较长,容易陷于局部最优值等缺陷,本文对训练算法加以改进,分别使用平均线性粒子群,粒子群和BP算法来优化人工神经网络的各项参数,首先使用标准函数测试了3种算法的全局优化性能,然后用它们对三峡水库的入库径流进行预报,以比较它们的预报性能。结果表明,在3种算法中,平均线性粒子群算法全局寻优的速度最快,稳定性最高,基于平均线性粒子群算法的人工神经网络的径流预报的精度也最高。

Artificial neural networks （ANNs） are effective tools in forecasting runoff in river because of their power capability in mapping in-output relations.However,the traditional ANNs based on back-propagation training algorithm need improvement because they have shortcomings in long training times and prone in falling into local optimum points.Therefore,3 algorithms were used to train the ANNs-mean linear particle swarm optimization （ML-PSO） method,original particle swarm optimization （PSO） method and BP method.Their global optimization capabilities were first tested by using the 3 standard mathematical functions,and the ANNs based on the 3 training algorithms were applied in runoff forecasting to test their performances.The results show that among the 3 algorithms,the ML-PSO algorithm is the fastest and most robust one in finding global optimum,and it also is the most accurate one in forecasting runoff.