کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4942335 | 1437251 | 2017 | 44 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Multi-layer architecture for adaptive fuzzy inference system with a large number of input features
ترجمه فارسی عنوان
معماری چند لایه برای سیستم استنتاج فازی سازگار با تعداد زیادی از ویژگی های ورودی
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
The Sugeno adaptive fuzzy neural network using training data is a good approximation to model different systems. The large number of adaptive neuro-fuzzy inference system (ANFIS) input features is a major challenge in using ANFIS and is not applicable with increased parameters. We present a solution for many input features solving modular problems; we created a multi-layer architecture of SUB-ANFIS (MLA-ANFIS) for this purpose. Different topologies were created with various combinations of multiple input features, and an error indicator was calculated for each combination of topologies. Finally, the best topology was chosen among the states with the highest possible performance. We implemented a multi-layered approach based on 365-day concrete compressive strength data with eight input features and the optimized MLA-ANFIS topology (5-3-1) for this purpose from different ANFIS topologies and neural networks. Finally, the results from five other datasets prove the impact of the proposed MLA-ANFIS approach compared to the neural network method.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Cognitive Systems Research - Volume 42, May 2017, Pages 23-41
Journal: Cognitive Systems Research - Volume 42, May 2017, Pages 23-41
نویسندگان
Mohammad Saber Iraji,