摘要
使用输入-输出数据来确立模糊模型成为一种趋势。这种做法可视为一个系统辨识过程。模糊系统模型的辨识包括两个主要阶段:结构辨识和参数估计。旨在找到一个灵活的方法来学习和优化模糊推理系统的结构和参数。我们采取网络结构的Sugeno模糊系统作为初步预测模型,用改进的遗传算法来确定其结构和参数。通过对某电网负荷预测的实例表明,该模型具有较好的拟合精度。
An proach can be alternative direction in the development of fuzzy models is based on the use of input-output data. This ap- regarded as a process of system identification. The identification of a fuzzy system model consists of two major phases： structure identification and parameter identification. The aim of this paper is to determine the main aspects involved in developing a flexible method able to learn and optimize both the structure and the parameters of fuzzy infer- ence system. We take a Sugeno fuzzy system with network structure as the initial forecasting model, and the improved ge- netic algorithm is used to confirm its structure and parameters. A case study of load forecasting in a certain power network shows that the model which is based on this way has a better fitting precision.
出处
《电气开关》
2012年第2期43-46,共4页
Electric Switchgear
关键词
系统辨识
网络结构
模糊系统
遗传算法
system identification
fuzzy system model
network structure
improved genetic algorithmic approach
