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Soft-Boosted Self-Constructing Neural Fuzzy Inference Network
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DOI：10.1109/TSMC.2015.2507139.png)
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摘要
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This correspondence paper proposes an improved version of the self-constructing neural fuzzy inference network (SONFIN), called soft-boosted SONFIN (SB-SONFIN). The design softly boosts the learning process of the SONFIN in order to decrease the error rate and enhance the learning speed. The SB-SONFIN boosts the learning power of the SONFIN by taking into account the numbers of fuzzy rules and initial weights which are two important parameters of the SONFIN, SB-SONFIN advances the learning process by: 1) initializing the weights with the width of the fuzzy sets rather than just with random values and 2) improving the parameter learning rates with the number of learned fuzzy rules. The effectiveness of the proposed soft boosting scheme is validated on several real world and benchmark datasets. The experimental results show that the SB-SONFIN possesses the capability to outperform other known methods on various datasets.
Keyword:
Fuzzy neural network
online learning system
parameter learning
soft boost
structure learning
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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引用论文
Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm基于聚类、训练误差和遗传算法的三种新型模糊神经网络学习算法.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
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