کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4974140 1365520 2017 38 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A clustering algorithm based TS fuzzy model for tracking dynamical system data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر پردازش سیگنال
پیش نمایش صفحه اول مقاله
A clustering algorithm based TS fuzzy model for tracking dynamical system data
چکیده انگلیسی
This paper presents an optimal fuzzy partition based Takagi Sugeno Fuzzy Model (TSFM) in which a novel clustering algorithm, known as Modified Fuzzy C-Regression Model (MFCRM), has been proposed. The objective function of MFCRM algorithm has been developed by considering of geometrical structure of input data and linear functional relation between input-output data. The MFCRM partitions the data space to create fuzzy subspaces (rules). A new validation criterion has been developed for detecting the right number of rules (subspaces) in a given data set. The obtained fuzzy partition is used to build the fuzzy structure and identify the premise parameters. Once, right number of rules and premise parameters have been identified, then consequent parameters have been identified by orthogonal least square (OLS) approach. The cluster validation index has been tested on synthetic data set. The effectiveness of MFCRM based TSFM has been validated on benchmark examples, such as Boiler Turbine system, Mackey-Glass time series data and Box-Jenkins model. The model performance is also validated through high-dimensional data such as Auto-MPG data and Boston Housing data.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of the Franklin Institute - Volume 354, Issue 13, September 2017, Pages 5617-5645
نویسندگان
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