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摘要: 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.Abstract: 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.
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