کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
410373 | 679140 | 2010 | 8 صفحه PDF | دانلود رایگان |

It is difficult to establish a black-box model for sparse data, because not enough data can be applied for training. This paper presents a novel identification approach using multiple fuzzy neural networks. It focuses on structure and parameters uncertainty which have been widely explored in the literature. Firstly, the sparse data are used within a fixed time interval to generate model structure. Then kernel regression methods are used to generate training data, a stable updating algorithm is proposed to train the membership functions. To cope structure change, a hysteresis strategy is proposed to enable multiple fuzzy neural identifier switching with guaranteed performance. Both theoretic analysis and simulation example show the efficacy of the proposed method.
Journal: Neurocomputing - Volume 73, Issues 13–15, August 2010, Pages 2446–2453