基于模糊子空間聚類的〇階L2型TSK模糊系統(tǒng)
doi: 10.11999/JEIT150074 cstr: 32379.14.JEIT150074
基金項目:
國家自然科學基金(61170122)，江蘇省杰出青年基金(BK20140001)和新世紀優(yōu)秀人才支持計劃(NCET120882)
Fuzzy Subspace Clustering Based Zero-order L2-norm TSK Fuzzy System
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摘要: 經(jīng)典數(shù)據(jù)驅(qū)動型TSK(Takagi-Sugeno-Kang)模糊系統(tǒng)在獲取模糊規(guī)則時，會考慮數(shù)據(jù)的所有特征空間，其帶來一個重要缺陷：如果數(shù)據(jù)的特征空間維數(shù)過高，則系統(tǒng)獲取的模糊規(guī)則繁雜，使系統(tǒng)復雜度增加而導致解釋性下降。該文針對此缺陷，探討了一種基于模糊子空間聚類的〇階L2型TSK模糊系統(tǒng)(Fuzzy Subspace Clustering based zero-order L2- norm TSK Fuzzy System, FSC-0-L2-TSK-FS)構(gòu)建新方法。新方法構(gòu)建的模糊系統(tǒng)不僅能縮減模糊規(guī)則前件的特征空間，而且獲取的模糊規(guī)則可對應于不同的特征子空間，從而具有更接近人類思維的推理機制。模擬和真實數(shù)據(jù)集上的建模結(jié)果表明，新方法增強了面對高維數(shù)據(jù)所建模型的解釋性，同時所建模型得到了較之于一些經(jīng)典方法更好或可比較的泛化性能。Abstract: The classical data driven Takagi-Sugeno-Kang (TSK) fuzzy system considers all the features of trained data, and faces a challenge that the interpretation is degenerated and the obtained fuzzy rule is complex when trained by high dimensional data. In this paper, a new fuzzy model, i.e., Fuzzy Subspace Clustering based zero-order L2-norm TSK Fuzzy System (FSC-0-L2-TSK-FS) is proposed to overcome this difficulty. The proposed fuzzy system not only reduces the feature spaces of the rule of antecedent, but also makes different rules implement the inference in different subspaces. The inference mechanism of the proposed fuzzy model training algorithm is very similar to the inference procedure of human. The experimental studies on the synthetic and real datasets prove that the interpretation of model constructed by the proposed method is enhanced when trained by high dimensional data and the generalization performance is better or comparative to several classical TSK fuzzy systems training methods.
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