| 一种模糊神经网络的快速参数学习算法 |
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| 引用本文： | 陈 非,敬忠良,姚晓东.一种模糊神经网络的快速参数学习算法[J].控制理论与应用,2002,19(4):583-587. |
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| 作者姓名： | 陈 非 敬忠良 姚晓东 |
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| 作者单位： | 上海交通大学电子信息学院航空航天信息与控制研究所,上海,200030 |
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| 摘 要： | 提出了一种新的模糊神经网络的快速参数学习算法, 采用一些特殊的处理, 可以用递推最小二乘法(RLS)来调整所有的参数. 以前的学习算法在调整模糊隶属度函数的中心和宽度的时候, 用的是梯度下降法, 具有容易陷入局部最小值点、收敛速度慢等缺点, 而本算法则可以克服这些缺点, 最后通过仿真验证了算法的有效性.
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| 关 键 词： | T-S模糊推理系统 多层前向神经网络 改进RLS算法 模糊神经网络 |
| 文章编号： | 1000-8152(2002)04-05-0583 |
| 收稿时间： | 2000/7/17 0:00:00 |
| 修稿时间： | 2000年7月17日 |
Fast parameter learning algorithm for fuzzy neural networks |
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| CHEN Fei,JING Zhong-liang and YAO Xiao-dong.Fast parameter learning algorithm for fuzzy neural networks[J].Control Theory & Applications,2002,19(4):583-587. |
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| Authors: | CHEN Fei JING Zhong-liang and YAO Xiao-dong |
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| Affiliation: | School of Electronics and Information Technology, Institute of Aerospace Information and Control, Shanghai Jiaotong University, Shanghai 200030,China;School of Electronics and Information Technology, Institute of Aerospace Information and Control, Shanghai Jiaotong University, Shanghai 200030,China;School of Electronics and Information Technology, Institute of Aerospace Information and Control, Shanghai Jiaotong University, Shanghai 200030,China |
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| Abstract: | A novel parameter learning algorithm for fuzzy neural networks (FNN) is proposed. The conventional methods usually use the gradient descent based backpropogation algorithm to adjust the center and width of the membership functions. To avoid the inborn problem of BP algorithm, such as local minima and slow convergence, a modified RLS method is employed here to adjust the parameters of FNN, which is faster than the conventional BP algorithm. The validity of this method has been demonstrated by simulation results. |
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| Keywords: | T-S fuzzy inference system multi-layer neural networks modified RLS algorithm fuzzy neural networks (FNN) |
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