Research on D-FNN Algorithm with the Combination of Column Pivot#br#
SVD-QR Method Pruning Strategy and Parameters Adjustment

ZHANG Defeng; MA Zilong

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Research on D-FNN Algorithm with the Combination of Column Pivot#br# SVD-QR Method Pruning Strategy and Parameters Adjustment

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- Research on D-FNN Algorithm with the Combination of Column Pivot#br# SVD-QR Method Pruning Strategy and Parameters Adjustment
- Acta Scientiarum Naturalium Universitatis SunYatseni Vol. 52, Issue 2, Pages: 8-13(2013)
- 作者机构：
  
  1. 佛山科学技术学院计算机系,广东,佛山,528000
  2. 哈尔滨工业大学电子工程系,黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013，
  
  Published Online：25 March 2013，
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ZHANG Defeng, MA Zilong. Research on D-FNN Algorithm with the Combination of Column Pivot#br# SVD-QR Method Pruning Strategy and Parameters Adjustment. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 52(2):8-13(2013)
DOI：

ZHANG Defeng, MA Zilong. Research on D-FNN Algorithm with the Combination of Column Pivot#br# SVD-QR Method Pruning Strategy and Parameters Adjustment. [J]. Acta Scientiarum Naturalium Universitatis SunYatseni 52(2):8-13(2013) DOI：

摘要

针对动态模糊神经网络，提出了列主元SVD-QR方法修剪策略与参数调整的新算法。其中采用列主元SVD-QR方法修剪策略从给定的规则库中提取最重要模糊规则，使得网络结构不会持续增长，避免了过拟合及过训练现象；采用扩展的卡尔曼滤波方法把全局算法划分成线性和非线性部分，线性和非线性参数可以分别被更新，从而可以达到快速的学习速度。通过对血压的控制来验证所提出算法的有效性，结果证明了列主元SVD-QR方法修剪策略参数调整的D-FNN算法具有良好的性能。

Abstract

Aiming at the imporvement of dynamic fuzzy neural network

a new algorithm is put forward

which combines the pruning strategy using column pivot SVD-QR method with the parameters adjustment. The pruning strategy with column pivot SVD-QR method is used to extract the most important fuzzy rules from a given rule base

which prevents the network structure from sustainable growth

and avoids the overfitting and over-training phenomenon. Extended Calman filtering method is utilized to divide the global algorithm into linear and nonlinear parts. The linear parameters and nonlinear ones can be updated respectively

thus the fast learning speed is achievable. The validity of the proposed algorithm is verified through the control of blood pressure. The results prove that the D-FNN with the pruning strategy using the column pivot SVD-QR method and the parameters adjustment has a good performance.

关键词

动态模糊神经网络修剪策略参数调整血压控制

Keywords

dynamic fuzzy neural network (D-FNN)pruning strategyparameter adjustmentblood pressure control

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