دانشگاه سیستان و بلوچستان

  اخبار و اعلانات

 آمار

تعداد نشریات29
تعداد شماره‌ها630
تعداد مقالات6,368
تعداد مشاهده مقاله9,730,806
تعداد دریافت فایل اصل مقاله6,362,426
Moradkhani, N., Teshnehlab, M.. (1402). (2205-7386) Identification of cement rotary kiln using type 2 Takagi-Sugeno neuro-fuzzy system considering the effect of different noisy condition. نشریه علمی دانشگاه سیستان و بلوچستان, (), -. doi: 10.22111/ijfs.2023.7653
N. Moradkhani; M. Teshnehlab. "(2205-7386) Identification of cement rotary kiln using type 2 Takagi-Sugeno neuro-fuzzy system considering the effect of different noisy condition". نشریه علمی دانشگاه سیستان و بلوچستان, , , 1402, -. doi: 10.22111/ijfs.2023.7653
Moradkhani, N., Teshnehlab, M.. (1402). '(2205-7386) Identification of cement rotary kiln using type 2 Takagi-Sugeno neuro-fuzzy system considering the effect of different noisy condition', نشریه علمی دانشگاه سیستان و بلوچستان, (), pp. -. doi: 10.22111/ijfs.2023.7653
Moradkhani, N., Teshnehlab, M.. (2205-7386) Identification of cement rotary kiln using type 2 Takagi-Sugeno neuro-fuzzy system considering the effect of different noisy condition. نشریه علمی دانشگاه سیستان و بلوچستان, 1402; (): -. doi: 10.22111/ijfs.2023.7653

(2205-7386) Identification of cement rotary kiln using type 2 Takagi-Sugeno neuro-fuzzy system considering the effect of different noisy condition

Iranian Journal of Fuzzy Systems
مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 02 خرداد 1402    اصل مقاله (1.58 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22111/ijfs.2023.7653
نویسندگان
N. Moradkhani* ؛ M. Teshnehlab
Electrical Engineering Department, K.N. Toosi University of Technology, Tehran, Iran
چکیده
A Cement rotary kiln is the main part of the cement production process, which has always attracted many researchers’ attention. However, this complex nonlinear system has not been modeled efficiently, which can make an appropriate performance, especially in noisy condition. In this work, the type 2 Takagi-Sugeno neuro-fuzzy system (T2TSNFS) is used to identify the cement rotary kiln, and the gradient descent (GD) algorithm is applied for tuning the parameters of antecedent and consequent parts of fuzzy rules. In addition, the optimal inputs of the system are selected by the genetic algorithm (GA) to achieve less complexity in the fuzzy system. The data relating to the Saveh White Cement (SWC) factory is used in the simulations. The Results demonstrate that the proposed identifier has an appropriate performance in noisy conditions. Furthermore, in this work, T2TSNFS is evaluated in noisy conditions, which had not been worked out before in related research works. Also, T2TSNFS and type 1 Takagi-Sugeno neuro-fuzzy system (T1TSNFS) are compared. The simulations show that T2TSNFS has more proper performance when the standard deviation of noise increases.
کلیدواژه‌ها
Cement Rotary Kiln؛ identification؛ type 2 fuzzy system؛ feature selection؛ noisy condition
آمار
تعداد مشاهده مقاله: 45
تعداد دریافت فایل اصل مقاله: 35


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب