Automated Nonlinear System Modeling with Multiple Fuzzy Neural Networks and Kernel Smoothing
Automated Nonlinear System Modeling with Multiple Fuzzy Neural Networks and Kernel Smoothing
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DOI：
10.1142/s0129065710002516
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发表时间：
2010-10
影响因子：
8
通讯作者：
Wen Yu;Xiaoou Li
中科院分区：
文献类型：
--
作者：
Wen Yu;Xiaoou Li
本文提出了一种新的模糊神经网络辨识方法。它侧重于结构和参数的不确定性，已在文献中广泛探讨。本文的主要贡献是，提出了一个集成的分析框架，自动结构选择和参数识别。核平滑技术用于在固定的时间间隔内自动生成模型结构。为了科普结构变化，提出了一种迟滞策略，以保证有限次切换和期望的性能。
This paper, presents a novel identification approach using fuzzy neural networks. It focuses on structure and parameters uncertainties which have been widely explored in the literatures. The main contribution of this paper is that an integrated analytic framework is proposed for automated structure selection and parameter identification. A kernel smoothing technique is used to generate a model structure automatically in a fixed time interval. To cope with structural change, a hysteresis strategy is proposed to guarantee finite times switching and desired performance.