.png){kind=icon}
.png){kind=icon}
Return
Improving fuzzy knowledge integration with particle swarmoptimization
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2010.06.030.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents an approach to integrate multiple fuzzy knowledge bases for increasing the accuracy and decreasing the complexity of the integrated knowledge base. The proposed approach consists of two phases: PSO-based fuzzy knowledge encoding, and PSO-based fuzzy knowledge fusion. In the encoding phase, the fuzzy rule sets and fuzzy sets with its corresponding membership functions are encoded as a string and are put together in the initial knowledge population. In the fusion phase, the particle swarm algorithm is used to explore the fuzzy rule sets, fuzzy sets and membership functions to its optimal or the approximately optimal extent. Two application domains, including diagnosis on students' program learning style and situational learning services composition, were used to demonstrate the performance of the proposed knowledge integration approach. Experiment results revealed that our approach will effectively increase the accuracy and decrease the complexity of integrated knowledge base. The results of this study could extend the effectiveness of knowledge inference and decision making. (C) 2010 Elsevier Ltd. All rights reserved.
Keywords:
Knowledge integration
Fuzzy rule
Particle swarm optimization
Evolutionary computing
Swarm intelligence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
View more organizations