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

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。

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

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.