کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10324194 | 661413 | 2005 | 20 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches](/preview/png/10324194.png)
چکیده انگلیسی
The nonlinear autoregressive moving average with exogenous inputs (NARMAX) model provides a powerful representation for time series analysis, modeling and prediction due to its capability of accommodating the dynamic, complex and nonlinear nature of real-world time series prediction problems. This paper focuses on the modeling and prediction of NARMAX-model-based time series using the fuzzy neural network (FNN) methodology. Both feedforward and recurrent FNNs approaches are proposed. Furthermore, an efficient algorithm for model structure determination and parameter identification with the aim of producing improved predictive performance for NARMAX time-series models is developed. Experiments and comparative studies demonstrate that the proposed FNN approaches can effectively learn complex temporal sequences in an adaptive way and they outperform some well-known existing methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuzzy Sets and Systems - Volume 150, Issue 2, 1 March 2005, Pages 331-350
Journal: Fuzzy Sets and Systems - Volume 150, Issue 2, 1 March 2005, Pages 331-350
نویسندگان
Yang Gao, Meng Joo Er,