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Appl. Sci. 2016, 6(7), 189; doi:10.3390/app6070189

Fuzzy Case-Based Reasoning System

1
Shanxi Meteorological Administration, Taiyuan 030006, China
2
School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
3
Computer Science Department, Oklahoma State University, Stillwater, OK 74075, USA
4
Training Center, Anhui Meteorological Administration, Hefei 230001, China
5
Weather Forecasting Office, National Meteorological Center of China Meteorological Administration, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Academic Editor: José Santamaria
Received: 13 April 2016 / Revised: 24 May 2016 / Accepted: 9 June 2016 / Published: 29 June 2016
View Full-Text   |   Download PDF [2241 KB, uploaded 29 June 2016]   |  

Abstract

In this paper, we propose a fuzzy case-based reasoning system, using a case-based reasoning (CBR) system that learns from experience to solve problems. Different from a traditional case-based reasoning system that uses crisp cases, our system works with fuzzy ones. Specifically, we change a crisp case into a fuzzy one by fuzzifying each crisp case element (feature), according to the maximum degree principle. Thus, we add the “vague” concept into a case-based reasoning system. It is these somewhat vague inputs that make the outcomes of the prediction more meaningful and accurate, which illustrates that it is not necessarily helpful when we always create accurate predictive relations through crisp cases. Finally, we prove this and apply this model to practical weather forecasting, and experiments show that using fuzzy cases can make some prediction results more accurate than using crisp cases. View Full-Text
Keywords: case-based reasoning; fuzzy logic; maximum degree principal case-based reasoning; fuzzy logic; maximum degree principal
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lu, J.; Bai, D.; Zhang, N.; Yu, T.; Zhang, X. Fuzzy Case-Based Reasoning System. Appl. Sci. 2016, 6, 189.

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