面向高維數(shù)據(jù)的Takagi-Sugeno模糊系統(tǒng)建模新方法

林得富; 王駿; 蔣亦樟; 王士同

doi:10.11999/JEIT170792

面向高維數(shù)據(jù)的Takagi-Sugeno模糊系統(tǒng)建模新方法

doi: 10.11999/JEIT170792 cstr: 32379.14.JEIT170792

基金項目:

國家自然科學基金(61300151)，江蘇省自然科學基金(BK20160187, BK20161268),中央高?；究蒲袠I(yè)務費專項項目(JUSRP11737)

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- 文章訪問數(shù): 1794
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-08-07
- 修回日期: 2018-03-27
- 刊出日期: 2018-06-19

A Novel Takagi-Sugeno Fuzzy Systems Modeling Method for High Dimensional Data

Funds:

The National Natural Science Foundation of China (61300151), The Natural Science Foundation of Jiangsu Province (BK20160187, BK20161268), The Fundamental Research Funds for the Central Universities (JUSRP11737)

摘要

摘要: 對高維數(shù)據(jù)進行建模是Takagi-Sugeno(T-S)模糊系統(tǒng)建模面臨的一個重大挑戰(zhàn)。為此，該文提出一種特征選擇與組稀疏編碼相結合的模糊系統(tǒng)建模新方法WOMP-GS-FIS。首先，運用一種新型的加權正交匹配追蹤算法對原始樣本進行特征選擇，在此基礎上提取出模糊規(guī)則前件并產(chǎn)生模糊系統(tǒng)字典；然后，基于組稀疏正則化構造關于后件參數(shù)的組稀疏優(yōu)化問題，在優(yōu)化問題求解的同時得到重要的模糊規(guī)則。實驗結果表明，在保證模型泛化性能的前提下，該方法不僅能對所獲得的模糊規(guī)則結構進行精簡還可以進一步減少模糊規(guī)則數(shù)，進而解決高維數(shù)據(jù)環(huán)境下模糊規(guī)則可解釋性差的問題。
- T-S模糊系統(tǒng)建模 /
- 特征選擇 /
- 組稀疏編碼 /
- 精簡規(guī)則結構 /
- 模糊規(guī)則約減
Abstract: It is a great challenge to model Takagi-Sugeno(T-S) fuzzy systems on high dimensional data due to the problem of the curse of dimensionality. To this end, a novel T-S fuzzy system modeling method called WOMP-GS-FIS is proposed. The proposed method considers feature selection and group sparse coding simultaneously. Specifically, feature selection is performed by a novel Weighted Orthogonal Matching Pursuit (WOMP) method, based on which the fuzzy rule antecedent part is extracted and the dictionary of the fuzzy system is generated. Then, a group sparse optimization problem based on the group sparse regularization is formulated to obtain the optimal consequent parameters. In this way, the major fuzzy rules are selected by utilizing the group information that existing in the T-S fuzzy systems. The experimental results show that the proposed method can not only simplify the rule,s structure, but also reduce the number of fuzzy rules under the premise of good generalization performance, so as to solve the poor interpretation problem of fuzzy rules on high dimensional data effectively.
- T-S fuzzy systems modeling /
- Feature selection /
- Group sparse coding /
- Simplify rule's structure /
- Fuzzy rules reduction

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