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TSK 퍼지 규칙과 랜덤 클러스터링을 이용한 ELM 예측기의 설계

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Author(s)
염찬욱
Issued Date
2017
Abstract
Yeom, Chan-Uk
Advisor : Prof. Kwak, Keun Chang, Ph. D.
Dept. of Control and Instrumentation Eng.,
Graduate School of Chosun University
In this paper propose an ELM predictor using TSK fuzzy rule and random clustering method to improve the prediction performance of ELM predictor. The TSK-based ELM model consists of a structure that uses a linear function in place of a weight and sets the center of the cluster at random. There is no weight between the input layer and the hidden layer, and the weight between the hidden layer and the output layer is a linear equation. We propose method generates meaningful rules by using the if-then rule. Also, it’s improves the performance of the predictor by randomly generating the cluster center through the initial membership matrix. Experiments were compared with existing ELM predictors using function approximation, Boston Housing, and Auto-MPG data. Experimental results showed that the proposed method showed better performance than the conventional ELM predictor.
Alternative Title
Design of ELM Predictor using TSK Fuzzy Rule and Random Clustering
Alternative Author(s)
Yeom, Chan-Uk
Affiliation
조선대학교 일반대학원 제어계측공학과
Department
일반대학원 제어계측공학과
Advisor
곽근창
Awarded Date
2017-08
Table Of Contents
제1장 서론 1
제2장 ELM 모델 5
제1절 ELM의 구조 5
제2절 ELM의 분류기와 예측기 8
제3절 ELM의 활성 함수 12
제3장 TSK 기반의 ELM 예측기 16
제1절 TSK 기반 ELM 모델의 구조 16
제2절 TSK 기반 ELM 예측기의 설계 18
제4장 실험 및 결과분석 21
제1절 함수 데이터 예측 문제 22
제2절 Boston Housing 데이터 예측 문제 28
제3절 Auto-MPG 데이터 예측 문제 34
제5장 결론 40
참고문헌 42
Degree
Master
Publisher
조선대학교
Citation
염찬욱. (2017). TSK 퍼지 규칙과 랜덤 클러스터링을 이용한 ELM 예측기의 설계.
Type
Dissertation
URI
https://oak.chosun.ac.kr/handle/2020.oak/13256
http://chosun.dcollection.net/common/orgView/200000266279
Appears in Collections:
General Graduate School > 3. Theses(Master)
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