کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10399212 | 890436 | 2005 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Identification of MIMO Hammerstein models using least squares support vector machines
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
کنترل و سیستم های مهندسی
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
This paper studies a method for the identification of Hammerstein models based on least squares support vector machines (LS-SVMs). The technique allows for the determination of the memoryless static nonlinearity as well as the estimation of the model parameters of the dynamic ARX part. This is done by applying the equivalent of Bai's overparameterization method for identification of Hammerstein systems in an LS-SVM context. The SISO as well as the MIMO identification cases are elaborated. The technique can lead to significant improvements with respect to classical overparameterization methods as illustrated in a number of examples. Another important advantage is that no stringent assumptions on the nature of the nonlinearity need to be imposed except for a certain degree of smoothness.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Automatica - Volume 41, Issue 7, July 2005, Pages 1263-1272
Journal: Automatica - Volume 41, Issue 7, July 2005, Pages 1263-1272
نویسندگان
Ivan Goethals, Kristiaan Pelckmans, Johan A.K. Suykens, Bart De Moor,