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高斯激活函数特征值分解修剪技术的D-FNN算法研究 被引量：3

Research on Adaptive Dynamic Fuzzy Neural Network Algorithms with Gauss Activation Function and Eigenvalue Decomposition Pruning Technologies
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摘要 提出了一种D-FNN的新算法。其算法的最主要特点是:D-FNN选择高斯函数作为网络的激活函数和模糊系统的隶属函数,该算法不仅具有强大的全局映射泛化能力,而且在细化局部方面也有效;使用特征值分解修剪技术使得网络结构不会持续增长,可获得更为紧凑的D-FNN结构,避免了过拟合现象。最后通过对Her-mite多项式逼近能力来验证所提方案的有效性。仿真结果表明使用特征值分解修剪技术和高斯激活函数的D-FNN具有良好的性能。 A new algorithm, which uses D-FNN Gauss function as a network activation function and fuzzy membership function, is proposed. The algorithm obtains strong global mapping generalization abili- ty and effectiveness in local refinement, and adopts eigenvalue decomposition pruning technology to ena- bles more compact D-FNN structure to avoid the phenomenon of over fitting. Finally, the algorithm is confirmed through the Hermite polynomial approximation to approach ability of validity. The simulation results show that the eigenvalue decomposition of pruning techniques and Gauss activation function of D- FNN has good performance.
作者 何正风 张德丰 孙亚民
机构地区 佛山科学技术学院理学院 佛山科学技术学院计算机系 南京理工大学计算机科学与技术学院
出处 《中山大学学报（自然科学版）》 CAS CSCD 北大核心 2013年第1期34-39,共6页 Acta Scientiarum Naturalium Universitatis Sunyatseni
基金 广东省自然科学基金资助项目(S2011020002719)
关键词 动态模糊神经网络 模糊规则 修剪技术 特征值分解 dynamic fuzzy neural network （D-FNN） fuzzy rule pruning technology eigenvalue de-composition （ED）
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献15

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二级参考文献23

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共引文献10

同被引文献31

  • 1刘金琨.机器人控制系统的设计与MATLAB仿真[M].北京:清华大学出版社,2008.
  • 2JANG J-S R. ANFIS: Adaptive-network-based fuzzy in- ference system [ J ]. IEEE Transactions on Systems, Man, and Cybernetics ,2013, 23 (3) : 665 - 684.
  • 3KADIRKAMANATHAN V, NIRANJAN M. A function estimation approach to sequential learning with neural networks[ J ]. neural computation, 2012,16 (4) : 954 - 975.
  • 4HONG Z Q. Algebraic feature extraction of image for rec- ognition[ J ]. Pattern Recognition, 2011,24 ( 3 ) : 211 - 219.
  • 5WU S Q, ER M J. Dynamic fuzzy neural networks: a no- vel approach to function approximation [ J ]. IEEE Trans-actions on Systems, Man, and Cybernetics :Part B,2000, 30(2) : 358 -364.
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引证文献3

二级引证文献1

中山大学学报（自然科学版）
2013年 第1期

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