کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6863815 | 1439524 | 2018 | 40 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Reinforced hybrid interval fuzzy neural networks architecture: Design and analysis
ترجمه فارسی عنوان
معماری شبکه های عصبی فازی تقویت شده ترکیبی: طراحی و تجزیه و تحلیل
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
This paper is concerned with a new architecture of a reinforced hybrid interval fuzzy neural networks (RHIFNN) classifier developed with aid of Fuzzy C-Means (FCM) clustering and Particle Swarm Optimization (PSO). The key objectives of this study concern the following: (a) selection of preprocessing techniques for the dimensionality reduction of input space. Linear discriminant analysis (LDA) or principal component analysis (PCA) algorithm forms a front end of the network to form the low-dimensional input variables. (b) The efficient process of dealing with uncertain information by interval type-2 fuzzy sets using Fuzzy C-Means (FCM) clustering. The premise (condition) part of the rules is realized by two FCM clustering algorithms, which are invoked by using different values of the fuzzification coefficient subsequently resulting in interval-valued type-2 membership functions. (c) The simultaneous structural and parametric optimization of network by evolutionary algorithm is completed. The parameters of the network including both the premise and consequent parts are optimized by means of the particle swarm optimization (PSO). The proposed classifier is applied to a variety of machine learning datasets and the results are compared with those provided by other classifiers reported in the literature.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 303, 16 August 2018, Pages 20-36
Journal: Neurocomputing - Volume 303, 16 August 2018, Pages 20-36
نویسندگان
Eun-Hu Kim, Sung-Kwun Oh, Witold Pedrycz,