Decentralized Nonstationary Fuzzy Neural Network with Meta-Learning-Net
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/13/4/552 |
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| Summary: | 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. |
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| ISSN: | 2227-7390 |