A new approach to the identification of a fuzzy model

Minkee Park; Seunghwan Ji; Euntai Kim; Mignon Park

doi:10.1016/S0165-0114(97)00214-5

A new approach to the identification of a fuzzy model

Minkee Park, Seunghwan Ji, Euntai Kim, Mignon Park

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

Abstract

This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: structure identification and parameter identification. In this paper, algorithms to identify those parameters and structures are suggested to solve the problems of conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, which consider the linearity and continuity, respectively. For the premise part identification, the input space is partitioned by a clustering method. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

Original language	English
Pages (from-to)	169-181
Number of pages	13
Journal	Fuzzy Sets and Systems
Volume	104
Issue number	2
DOIs	https://doi.org/10.1016/S0165-0114(97)00214-5
Publication status	Published - 1999 Jun 1

Bibliographical note

Funding Information:
* Corresponding author. Tel.: +82-2-970-6464; fax: +82-2-970-6480; e-mail: mkpark@duck.snpu.ac.kr. i This work was supported by Ministry of Information and Communication, Project 96074-IT1-I1.

All Science Journal Classification (ASJC) codes

Logic
Artificial Intelligence

Access to Document

10.1016/S0165-0114(97)00214-5

Cite this

@article{d1aaa2203df1461f9342dc6b68a0622a,

title = "A new approach to the identification of a fuzzy model",

abstract = "This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: structure identification and parameter identification. In this paper, algorithms to identify those parameters and structures are suggested to solve the problems of conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, which consider the linearity and continuity, respectively. For the premise part identification, the input space is partitioned by a clustering method. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.",

author = "Minkee Park and Seunghwan Ji and Euntai Kim and Mignon Park",

note = "Funding Information: * Corresponding author. Tel.: +82-2-970-6464; fax: +82-2-970-6480; e-mail: mkpark@duck.snpu.ac.kr. i This work was supported by Ministry of Information and Communication, Project 96074-IT1-I1.",

year = "1999",

month = jun,

day = "1",

doi = "10.1016/S0165-0114(97)00214-5",

language = "English",

volume = "104",

pages = "169--181",

journal = "Fuzzy Sets and Systems",

issn = "0165-0114",

publisher = "Elsevier",

number = "2",

}

TY - JOUR

T1 - A new approach to the identification of a fuzzy model

AU - Park, Minkee

AU - Ji, Seunghwan

AU - Kim, Euntai

AU - Park, Mignon

N1 - Funding Information: * Corresponding author. Tel.: +82-2-970-6464; fax: +82-2-970-6480; e-mail: mkpark@duck.snpu.ac.kr. i This work was supported by Ministry of Information and Communication, Project 96074-IT1-I1.

PY - 1999/6/1

Y1 - 1999/6/1

N2 - This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: structure identification and parameter identification. In this paper, algorithms to identify those parameters and structures are suggested to solve the problems of conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, which consider the linearity and continuity, respectively. For the premise part identification, the input space is partitioned by a clustering method. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

AB - This paper presents an approach which is useful for the identification of a fuzzy model. The identification of a fuzzy model using input-output data consists of two parts: structure identification and parameter identification. In this paper, algorithms to identify those parameters and structures are suggested to solve the problems of conventional methods. Given a set of input-output data, the consequent parameters are identified by the Hough transform and clustering method, which consider the linearity and continuity, respectively. For the premise part identification, the input space is partitioned by a clustering method. The gradient descent algorithm is used to fine-tune parameters of a fuzzy model. Finally, it is shown that this method is useful for the identification of a fuzzy model by simulation.

UR - http://www.scopus.com/inward/record.url?scp=0001634693&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0001634693&partnerID=8YFLogxK

U2 - 10.1016/S0165-0114(97)00214-5

DO - 10.1016/S0165-0114(97)00214-5

M3 - Article

AN - SCOPUS:0001634693

SN - 0165-0114

VL - 104

SP - 169

EP - 181

JO - Fuzzy Sets and Systems

JF - Fuzzy Sets and Systems

IS - 2

ER -

A new approach to the identification of a fuzzy model

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this