广义模糊神经网络

从非线性系统本身的物理背景出发,根据系统本身的内在特性、先验知识和经验建立系统辨识模型,提出了广义模糊神经网络(GFNN).文中证明了GFNN的函数逼近定理,并据此提出了GFNN的结构自组织和参数自学习算法.GFNN在预设的辨识精度下能自动辨识系统的网络结构以及进行参数自学习,实现GFNN网络结构的真正在线自组织.仿真结果表明,对于慢时变非线性对象,GFNN表现出了很强的非线性逼近能力,是模糊逻辑系统与人工神经网络两类方法的比较成功的融合.

Generalized Fuzzy Neural Network

Based on the intrinsic physical background of nonlinear system, a system identification model is derived from the inherent systematic characteristics, a priori knowledge and experiences. And then, the GFNN (generalized fuzzy neural network) is put forwand, the GFNN approximation theorem is proved. The structure-self-organization and parameter-self-learning algorithm is proposed, which can automatically and simultaneously deal with the process of the system structure identification and parameter self-learning under predefined precision, so that the novel on-line structure self-organization of GFNN is realized. Simulation shows the nonlinear approximation abilities of GFNN, especially for identification of slow time-varying plant. The GFNN is a successful integrated algorithm of fuzzy logic and neural network.