دانلود مقاله ISI انگلیسی شماره 44230
عنوان فارسی مقاله

بهینه سازی کنترل کننده فازی تطبیقی با استفاده از الگوریتم جدید ترکیب تکاملی و کاربرد آن در سیستم کنترل سیل لاگوس در جنوب نیومکزیکو

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
44230 2016 11 صفحه PDF سفارش دهید 8220 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Optimization of adaptive fuzzy logic controller using novel combined evolutionary algorithms, and its application in Diez Lagos flood controlling system, Southern New Mexico
منبع

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

Journal : Expert Systems with Applications, Volume 43, January 2016, Pages 154–164

کلمات کلیدی
کنترل کننده فازی - الگوریتم ژنتیک - ذرات بهینه سازی ازدحام - مدیریت بهینه سیل - مدل دینامیکی - سیستم های کنترل سیلاب
پیش نمایش مقاله
پیش نمایش مقاله بهینه سازی کنترل کننده فازی تطبیقی با استفاده از الگوریتم جدید ترکیب تکاملی و کاربرد آن در سیستم کنترل سیل لاگوس در جنوب نیومکزیکو

چکیده انگلیسی

In fuzzy logic controllers (FLCs), optimal performance can be defined as performance that minimizes the deviation (error term) between the decisions of the fuzzy logic systems and the decisions of experts. A range of approaches – such as genetic algorithms (GA), particle swarm optimization (PSO), artificial neural networks (ANN), and adaptive network based fuzzy inference systems (ANFIS) – can be used to pursue optimal performance for FLCs by refining the membership function parameters (MFPs) that control performance. Multiple studies have been conducted to refine MFPs and improve the performance of fuzzy logic systems through the application of a single optimization approach, but since different optimization approaches yield different error terms under different scenarios, the use of a single optimization approach does not necessarily produce truly optimal results. Therefore, this study employed several optimization approaches – ANFIS, GA, and PSO – within a defined search engine unit that compared the error values from the different approaches under different scenarios and, in each scenario, selected the results that had the minimum error value. Additionally, appropriate initial variables for the optimization process were introduced through the Takagi–Sugeno method. This system was applied to a case study of the Diez Lagos (DL) flood controlling system in southern New Mexico, and we found that it had lower average error terms than a single optimization approach in monitoring a flood control gate and pump across a range of scenarios. Overall, using evolutionary algorithms in a novel search engine led to superior performance, using the Takagi–Sugeno method led to near-optimum initial values for the MFPs, and developing a feedback monitoring system consistently led to reliable operating rules. Therefore, we recommend the use of different methods in the search engine unit for finding the optimal MFPs, and selecting the MFPs from the method which has the lowest error value among them.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.