The invention relates to a travel time prediction method based on a particle swarm optimization wavelet neural network. The method comprises a particle swarm optimization part and a wavelet neural network prediction part. According to the particle swarm optimization algorithm, parameters of a wavelet neural network are optimized through continuous iteration, and the defects, including slow convergence speed, easiness in falling into a local minimum value and easiness in generating an oscillation effect, of a wavelet neural network model are overcome. Through comparison experiment analysis, itis found that the particle swarm optimization wavelet neural network model can accurately predict the change trend of the travel time and can also accurately predict the fluctuation condition of the travel time, and it is proved that the method has the advantages of being high in convergence speed, high in prediction precision and high in adaptability.
本发明涉及一种基于粒子群优化小波神经网络的行程时间预测方法,该方法包含粒子群算法优化部分和小波神经网络预测部分。其中粒子群优化算法通过不断的迭代优化小波神经网络的参数,解决了小波神经网络模型的缺陷,包括收敛速度缓慢、易陷入局部最小值和易产生振荡效应。通过对比实验分析后发现,粒子群优化小波神经网络模型不仅能准确预测行程时间的变化趋势,也能比较准确预测行程时间的波动情况,证明了本发明具有收敛速度快,预测精度高,适应性强的优点。
Expressway travel time prediction method based on particle swarm optimization wavelet neural network
一种基于粒子群优化小波神经网络的高速公路行程时间预测方法
2020-06-19
Patent
Electronic Resource
Chinese
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