Smoothed L^sup 1/2^ regularizer learning for split-complex valued neuro-fuzzy algorithm for TSK system and its convergence results

Smoothed L^sup 1/2^ regularizer learning for split-complex valued neuro-fuzzy algorithm for TSK system and its convergence results

This paper investigates an evolving split-complex valued neuro-fuzzy (SCVNF) algorithm for Takagi–Sugeno–Kang (TSK) system. In a bid to avoid the contradiction between boundedness and analyticity, splitting technique is traditionally employed to independently process the real part and the imaginary...

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Veröffentlicht in:Journal of the Franklin Institute 2018-09, Vol.355 (13), p.6132
Hauptverfasser: Liu, Yan, Yang, Dakun, Li, Feng
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Artificial neural networks
Convergence
Fuzzy logic
Fuzzy sets
Fuzzy systems
Machine learning
Set theory
Weight
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Zusammenfassung:This paper investigates an evolving split-complex valued neuro-fuzzy (SCVNF) algorithm for Takagi–Sugeno–Kang (TSK) system. In a bid to avoid the contradiction between boundedness and analyticity, splitting technique is traditionally employed to independently process the real part and the imaginary part of weight parameters in the system, which doubles weight dimension and causes oversized structure. For improving efficiency of structural optimization, previous studies have revealed that L1/2-norm regularizer can be effective in such sparse tasks thus is regarded as a representative of Lq (0 
ISSN:0016-0032
0016-0032