Convergence analysis of the batch gradient-based neuro-fuzzy learning algorithm with smoothing \(L_{1/2}\) regularization for the first-order Takagi-Sugeno system

DOI10.1016/J.FSS.2016.07.003zbMath1380.68335OpenAlexW2494598326MaRDI QIDQ1697505

Dakun Yang, Yan Liu

Publication date: 20 February 2018

Published in: Fuzzy Sets and Systems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.fss.2016.07.003

zbMATH Keywords

convergencefirst-order Takagi-Sugeno inference systempi-sigma networksmoothing \(L_{1 / 2}\) regularization

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Learning and adaptive systems in artificial intelligence (68T05) Reasoning under uncertainty in the context of artificial intelligence (68T37)

Related Items (4)

Deterministic convergence analysis via smoothing group Lasso regularization and adaptive momentum for Sigma-Pi-Sigma neural network ⋮ Smoothed \(L_{1/2}\) regularizer learning for split-complex valued neuro-fuzzy algorithm for TSK system and its convergence results ⋮ An online gradient-based parameter identification algorithm for the neuro-fuzzy systems ⋮ Tracking control optimization scheme for a class of partially unknown fuzzy systems by using integral reinforcement learning architecture

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