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基于EKF的模糊神经网络快速自组织学习算法


  摘 要:为了快速地构造一个有效的模糊神经网络,提出一种基于扩展卡尔曼滤波(EKF)的模糊神经网络自组织学习算法。在本算法中,按照提出的无须经过修剪过程的生长准则增加规则,加速了网络在线学习过程;使用EKF算法更新网络的自由参数,增强了网络的鲁棒性。仿真结果表明,该算法具有快速的学习速度、良好的逼近精度和泛化能力。

  关键词:模糊神经网络;扩展卡尔曼滤波;自组织学习

  中图分类号:TP273文献标志码:A

  文章编号:1001-3695(2010)07-2462-03

  doi:10.3969/j.issn.1001-3695.2010.07.016

  Fast self-organizing learning algorithm based on EKF for fuzzy neural network

  ZHOU Shang-bo,LIU Yu-jiong

  (College of Computer Science, Chongqing University, Chongqing 400044, China)

  Abstract:To construct an effective fuzzy neural network, this paper presented a self-organizing learning algorithm based on extended Kalman filter for fuzzy neural network. In the algorithm, the network grew rules according to the proposed growing criteria without pruning, speeding up the online learning process.All the free parameters were updated by the extended Kalman filter approach and the robustness of the network was obviously enhanced. The simulation results show that the proposed algorithm can achieve fast learning speed, high approximation precision and generation capability.

  Key words:fuzzy neural network; extended Kalman filter(EKF); self-organizing learning

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