کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
389113 661094 2015 23 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Design of a new adaptive neuro-fuzzy inference system based on a solution for clustering in a data potential field
ترجمه فارسی عنوان
طراحی یک سیستم استنتاج فازی جدید تطبیقی ​​مبتنی بر یک راه حل برای خوشه بندی در زمینه پتانسیل داده
کلمات کلیدی
سیستم استنتاج نوری فازی سازگار، خوشه بندی داده ها، شناسایی مدل فازی، خوشه هیپر جعبه، خوشه هیبرید هواپیما، میدان بالقوه
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

In this study, we propose a new method for building adaptive neuro-fuzzy inference systems (ANFIS) via datasets. In order to improve the performance of conventional ANFIS to handle noisy data, we focus on ameliorating the cluster-data space established from a given dataset. To achieve this, we propose a weighted clustering process in the joint input–output data space. Thus, during the clustering process, the cluster with the smallest potential distance, which is a combination of the Euclidean distance and the size of the clusters, has priority when obtaining the surveyed sample. Based on this principle, we formulate a new algorithm for synthesizing an ANFIS via the proposed data potential field, called ANFIS-PF, which has the following features: it establishes a data potential field that covers the entire initial data space, a cluster-data space is built based on the generated data potential field, and the ANFIS is synthesized using this cluster-data space. Finally, we performed experiments using datasets with and without noise to demonstrate the effectiveness of the proposed method in several applications, including dynamic-response noisy datasets obtained from a magnetorheological damper.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Fuzzy Sets and Systems - Volume 279, 15 November 2015, Pages 64–86
نویسندگان
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