کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
381700 | 1437494 | 2008 | 21 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Nonlinear system identification: From multiple-model networks to Gaussian processes
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Nonlinear system identification: From multiple-model networks to Gaussian processes Nonlinear system identification: From multiple-model networks to Gaussian processes](/preview/png/381700.png)
چکیده انگلیسی
Neural networks have been widely used to model nonlinear systems for control. The curse of dimensionality and lack of transparency of such neural network models has forced a shift towards local model networks and recently towards the nonparametric Gaussian processes approach. Assuming common validity functions, all of these models have a similar structure. This paper examines the evolution from the radial basis function network to the local model network and finally to the Gaussian process model. A simulated example is used to explain the advantages and disadvantages of each structure.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Engineering Applications of Artificial Intelligence - Volume 21, Issue 7, October 2008, Pages 1035–1055
Journal: Engineering Applications of Artificial Intelligence - Volume 21, Issue 7, October 2008, Pages 1035–1055
نویسندگان
Gregor Gregorčič, Gordon Lightbody,