Decentralized Nonstationary Fuzzy Neural Network with Meta-Learning-Net

Zhen Zhang, Meiling Yu and Hui Jia ()
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Zhen Zhang: College of Artificial Intelligence, Nankai University, Tianjin 300381, China
Meiling Yu: College of Artificial Intelligence, Nankai University, Tianjin 300381, China
Hui Jia: College of Artificial Intelligence, Nankai University, Tianjin 300381, China

Mathematics, 2025, vol. 13, issue 4, 1-18

Abstract: The nonstationary fuzzy neural network (NFNN) has proven to be an effective and interpretable tool in machine learning, capable of addressing uncertainty problems similarly to type-2 fuzzy neural networks, while offering reduced computational complexity. However, the update of disturbance parameters in an NFNN is restricted due to the necessity of maintaining a regular membership function, which limits its learning capability. To address this limitation, we propose a Decentralized NFNN (DNFNN) that overcomes the obstacles in the backward update process and enhances the efficiency of large-scale optimization. Additionally, we demonstrate improved computational efficiency and establish the linear convergence of the proposed decentralized algorithm. By integrating a meta-learning network, we further enhance the output strategy of the NFNN, enabling it to adaptively determine the contribution of individual sub-networks. Experimental results on various UCI datasets, spanning multiple domains and exhibiting diverse dimensions and sizes, show that the DNFNN outperforms existing methods in terms of classification accuracy, robustness and practicality.

Keywords: fuzzy system; nonstationary fuzzy neural network; decentralized optimization; meta-learning; linear convergence (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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