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Fuzzy neural network structure identification based on soft competitive learning

机译：基于软竞争学习的模糊神经网络结构辨识

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Fuzzy neural network combines the learning capacity of artificial neural networks with the interpretability of the fuzzy systems. A novel structure learning algorithm for fuzzy neural networks (SLNN) is presented in this paper. The neurons of SLNN are created and adapted as online learning proceeds. The learning rule of SLNN is based on Hebb as well as soft competitive learning. The soft competitive learning cannot only let SLNN be able to learn from new data but also prevent it from losing the knowledge that has been learned earlier. To obtain a concise fuzzy rule, a pruning algorithm is adopted in SLNN, which does not disobey the basic design philosophy of fuzzy system. Simulations are performed on the primary benchmarks: circle-in-the-square, two spirals apart, UCI machine learning archive's synthetic control chart time series, and KDDCUP'99 data set. Compared with fuzzy ARTMAP, BP and hierarchical neuro-fuzzy quadtree (HNFQ), the fuzzy neural network achieves higher performance.
机译：模糊神经网络将人工神经网络的学习能力与模糊系统的可解释性相结合。提出了一种新颖的模糊神经网络结构学习算法。随着在线学习的进行，SLNN的神经元将被创建和调整。 SLNN的学习规则基于Hebb以及软竞争性学习。软竞争性学习不仅可以让SLNN能够从新数据中学习，而且还可以防止SLNN丢失先前学习的知识。为了获得简洁的模糊规则，SLNN中采用了修剪算法，该算法不违背模糊系统的基本设计原理。仿真是在以下主要基准上执行的：平方圆，相隔两个螺旋线，UCI机器学习档案的综合控制图时间序列以及KDDCUP'99数据集。与模糊ARTMAP，BP和层次神经模糊四叉树（HNFQ）相比，模糊神经网络具有更高的性能。

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