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مقاله Gradient Based Iterative Identification of Multivariable HammersteinدرWiener Models with Application to a Steam Generator Boiler دارای 6 صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است

فایل ورد مقاله Gradient Based Iterative Identification of Multivariable HammersteinدرWiener Models with Application to a Steam Generator Boiler کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه و مراکز دولتی می باشد.

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توجه : در صورت  مشاهده  بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی مقاله Gradient Based Iterative Identification of Multivariable HammersteinدرWiener Models with Application to a Steam Generator Boiler،به هیچ وجه بهم ریختگی وجود ندارد

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بخشی از متن مقاله Gradient Based Iterative Identification of Multivariable HammersteinدرWiener Models with Application to a Steam Generator Boiler :

سال انتشار: 1391
محل انتشار: بیستمین کنفرانس مهندسی برق ایران
تعداد صفحات: 6
چکیده:

Most of the real industrial systems are nonlinear and multivariable which might be correlated with some noises. Therefore, considering a model which can effectivelycharacterize these types of systems are very appealing. In this regard, this paper presents a multivariable Hammerstein- Wiener model for identification of nonlinear systems withmoving average noises. For this purpose, this model is first reexpressed as a multivariable pseudo-linear regression problem.Then, a gradient based iterative learning algorithm is invoked which can successfully estimate the matrix of unknownparameters as well as the noises. The efficiency of the proposed identification scheme is investigated through data for a real multivariable nonlinear process as a case study. This process isa Steam Generator Boiler at Abbott Power Plant in Champaign IL which has characteristics of instabilities, nonlinearity, nonminimumphase behaviour, time delays, noise spectrum in the same frequency range of the plant dynamics, and load disturbances. As the results verify, this approach is quite efficient for identification of multivariable nonlinear systems

 

 

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