دانلود مقاله ISI انگلیسی شماره 79427
عنوان فارسی مقاله

برنامه نویسی ژنتیک چند مرحله ای: یک استراتژی جدید برای مدل سازی سیستم غیر خطی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79427 2011 13 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Multi-stage genetic programming: A new strategy to nonlinear system modeling
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Information Sciences, Volume 181, Issue 23, 1 December 2011, Pages 5227–5239

کلمات کلیدی
برنامه نویسی ژنتیک چند مرحله ای؛ مدل سازی سیستم غیر خطی؛ مشکلات مهندسی - فرمول بندی
پیش نمایش مقاله
پیش نمایش مقاله برنامه نویسی ژنتیک چند مرحله ای: یک استراتژی جدید برای مدل سازی سیستم غیر خطی

چکیده انگلیسی

This paper presents a new multi-stage genetic programming (MSGP) strategy for modeling nonlinear systems. The proposed strategy is based on incorporating the individual effect of predictor variables and the interactions among them to provide more accurate simulations. According to the MSGP strategy, an efficient formulation for a problem comprises different terms. In the first stage of the MSGP-based analysis, the output variable is formulated in terms of an influencing variable. Thereafter, the error between the actual and the predicted value is formulated in terms of a new variable. Finally, the interaction term is derived by formulating the difference between the actual values and the values predicted by the individually developed terms. The capabilities of MSGP are illustrated by applying it to the formulation of different complex engineering problems. The problems analyzed herein include the following: (i) simulation of pH neutralization process, (ii) prediction of surface roughness in end milling, and (iii) classification of soil liquefaction conditions. The validity of the proposed strategy is confirmed by applying the derived models to the parts of the experimental results that were not included in the analyses. Further, the external validation of the models is verified using several statistical criteria recommended by other researchers. The MSGP-based solutions are capable of effectively simulating the nonlinear behavior of the investigated systems. The results of MSGP are found to be more accurate than those of standard GP and artificial neural network-based models.

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