计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 327-333.doi: 10.11896/jsjkx.191200126
张德干1,2, 杨鹏1,2, 张捷3, 高瑾馨1,2, 张婷1,2
ZHANG De-gan1,2, YANG Peng1,2, ZHANG Jie3, GAO Jin-xin1,2, ZHANG Ting1,2
摘要: 文中提出一种基于量子粒子群优化策略的车联网交通流量预测算法。根据交通流量数据特征建立对应模型,将遗传模拟退火算法应用到量子粒子群算法中得到优化的初始聚类中心,并将优化后的算法应用于径向基神经网络预测模型的参数优化,通过径向基神经网络的高维映射得到所需预测的数据结果。另外,将所提算法与QPSO-RBF等其他相关算法进行了比较研究。仿真结果显示,相比于其他算法,所提算法能够降低预测误差,得到更好、更稳定的预测结果。
中图分类号:
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