Han Honggui, Liu Hongxu, Qiao Junfei
IEEE Trans Neural Netw Learn Syst. 2024 Feb;35(2):2081-2093. doi: 10.1109/TNNLS.2022.3186671. Epub 2024 Feb 5.
Fuzzy neural networks (FNNs) hold the advantages of knowledge leveraging and adaptive learning, which have been widely used in nonlinear system modeling. However, it is difficult for FNNs to obtain the appropriate structure in the situation of insufficient data, which limits its generalization performance. To solve this problem, a data-knowledge-driven self-organizing FNN (DK-SOFNN) with a structure compensation strategy and a parameter reinforcement mechanism is proposed in this article. First, a structure compensation strategy is proposed to mine structural information from empirical knowledge to learn the structure of DK-SOFNN. Then, a complete model structure can be acquired by sufficient structural information. Second, a parameter reinforcement mechanism is developed to determine the parameter evolution direction of DK-SOFNN that is most suitable for the current model structure. Then, a robust model can be obtained by the interaction between parameters and dynamic structure. Finally, the proposed DK-SOFNN is theoretically analyzed on the fixed structure case and dynamic structure case. Then, the convergence conditions can be obtained to guide practical applications. The merits of DK-SOFNN are demonstrated by some benchmark problems and industrial applications.
模糊神经网络（FNNs）具有知识利用和自适应学习的优势，已广泛应用于非线性系统建模。然而，在数据不足的情况下，FNNs难以获得合适的结构，这限制了其泛化性能。为解决这一问题，本文提出了一种具有结构补偿策略和参数强化机制的数据知识驱动自组织FNN（DK-SOFNN）。首先，提出一种结构补偿策略，从经验知识中挖掘结构信息以学习DK-SOFNN的结构。然后，通过充分的结构信息获得完整的模型结构。其次，开发一种参数强化机制来确定最适合当前模型结构的DK-SOFNN的参数演化方向。然后，通过参数与动态结构之间的相互作用获得鲁棒模型。最后，对所提出的DK-SOFNN在固定结构情况和动态结构情况下进行理论分析。然后，可以获得收敛条件以指导实际应用。一些基准问题和工业应用证明了DK-SOFNN的优点。
