一种新的模糊规则动态调整正则项系数的神经网络学习方法

A NOVEL NEURAL NETWORK LEARNING METHOD OF DYNAMICALLY TUNING REGULARIZATION COEFFICIENT ACCORDINT TO FUZZY RULES

从偏差 方差模型出发 ,提出了一种通过模糊规则推理动态调整正则项系数的新方法 ,并有效地确定了模糊推理规则和隶属度函数 .并将该方法与BP算法和固定正则项系数的方法进行了比较 ,该方法具有精度高、收敛快和泛化能力高等优点 。

Based on bias-variance model, a novel method of dynamically tuning the regularization coefficient by fuzzy rules inference was proposed. The fuzzy inference rules and membership functions were effectively determined. Furthermore, the method was compared with the traditional BP algorithm and fixed regularization coefficien's method. The result is that the proposed method has the merits of the highest precision, rapid convergence and best generalization capacity. The capacity proposed method is shown to be a very effective method by several examples simulation.