Similarity measure based on nonlinear compensatory model and fuzzy logic inference

Y. Zeng; M. Zhou; R. Wang

doi:10.1109/GRC.2005.1547300

Similarity measure based on nonlinear compensatory model and fuzzy logic inference

Y. Zeng, M. Zhou, R. Wang

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

2 Citations (Scopus)

Abstract

In this paper, we propose a novel Nonlinear Nearest-Neighbor (NNN) matching for similarity measure based on Nonlinear Compensatory (NC) choice model. Based on fuzzy logic inference, we propose NC choice model which granulates the psychological boundary between linear and nonlinear compensatory in the decision-making. Based on our NC mode, we develop a NNN matching function to consider both linear and nonlinear psychological compensatory effects. Theory analysis and experiment have demonstrated the success of NNN matching and NC model.

Original language	English
Title of host publication	2005 IEEE International Conference on Granular Computing
Pages	342-345
Number of pages	4
DOIs	https://doi.org/10.1109/GRC.2005.1547300
Publication status	Published - 2005
Externally published	Yes
Event	2005 IEEE International Conference on Granular Computing - Beijing, China Duration: 25 Jul 2005 → 27 Jul 2005

Publication series

Name	2005 IEEE International Conference on Granular Computing
Volume	2005

Conference

Conference	2005 IEEE International Conference on Granular Computing
Country/Territory	China
City	Beijing
Period	25/07/05 → 27/07/05

Keywords

Compensation model
Decision making
Fuzzy logic
Nearest-neighbor matching
Similarity measure

ASJC Scopus subject areas

General Engineering

Access to Document

10.1109/GRC.2005.1547300

Cite this

@inproceedings{369e0b00a10743a49f526b086e2ee3ec,

title = "Similarity measure based on nonlinear compensatory model and fuzzy logic inference",

abstract = "In this paper, we propose a novel Nonlinear Nearest-Neighbor (NNN) matching for similarity measure based on Nonlinear Compensatory (NC) choice model. Based on fuzzy logic inference, we propose NC choice model which granulates the psychological boundary between linear and nonlinear compensatory in the decision-making. Based on our NC mode, we develop a NNN matching function to consider both linear and nonlinear psychological compensatory effects. Theory analysis and experiment have demonstrated the success of NNN matching and NC model.",

keywords = "Compensation model, Decision making, Fuzzy logic, Nearest-neighbor matching, Similarity measure",

author = "Y. Zeng and M. Zhou and R. Wang",

year = "2005",

doi = "10.1109/GRC.2005.1547300",

language = "English",

isbn = "0780390172",

series = "2005 IEEE International Conference on Granular Computing",

pages = "342--345",

booktitle = "2005 IEEE International Conference on Granular Computing",

note = "2005 IEEE International Conference on Granular Computing ; Conference date: 25-07-2005 Through 27-07-2005",

}

Zeng, Y, Zhou, M & Wang, R 2005, Similarity measure based on nonlinear compensatory model and fuzzy logic inference. in 2005 IEEE International Conference on Granular Computing., 1547300, 2005 IEEE International Conference on Granular Computing, vol. 2005, pp. 342-345, 2005 IEEE International Conference on Granular Computing, Beijing, China, 25/07/05. https://doi.org/10.1109/GRC.2005.1547300

TY - GEN

T1 - Similarity measure based on nonlinear compensatory model and fuzzy logic inference

AU - Zeng, Y.

AU - Zhou, M.

AU - Wang, R.

PY - 2005

Y1 - 2005

N2 - In this paper, we propose a novel Nonlinear Nearest-Neighbor (NNN) matching for similarity measure based on Nonlinear Compensatory (NC) choice model. Based on fuzzy logic inference, we propose NC choice model which granulates the psychological boundary between linear and nonlinear compensatory in the decision-making. Based on our NC mode, we develop a NNN matching function to consider both linear and nonlinear psychological compensatory effects. Theory analysis and experiment have demonstrated the success of NNN matching and NC model.

AB - In this paper, we propose a novel Nonlinear Nearest-Neighbor (NNN) matching for similarity measure based on Nonlinear Compensatory (NC) choice model. Based on fuzzy logic inference, we propose NC choice model which granulates the psychological boundary between linear and nonlinear compensatory in the decision-making. Based on our NC mode, we develop a NNN matching function to consider both linear and nonlinear psychological compensatory effects. Theory analysis and experiment have demonstrated the success of NNN matching and NC model.

KW - Compensation model

KW - Decision making

KW - Fuzzy logic

KW - Nearest-neighbor matching

KW - Similarity measure

UR - http://www.scopus.com/inward/record.url?scp=33845341815&partnerID=8YFLogxK

U2 - 10.1109/GRC.2005.1547300

DO - 10.1109/GRC.2005.1547300

M3 - Conference contribution

AN - SCOPUS:33845341815

SN - 0780390172

SN - 9780780390171

T3 - 2005 IEEE International Conference on Granular Computing

SP - 342

EP - 345

BT - 2005 IEEE International Conference on Granular Computing

T2 - 2005 IEEE International Conference on Granular Computing

Y2 - 25 July 2005 through 27 July 2005

ER -

Similarity measure based on nonlinear compensatory model and fuzzy logic inference

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this