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» Extraction of Symbolic Rules from Artificial Neural Networks
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JCIT
2010
156views more  JCIT 2010»
13 years 2 months ago
Intelligent Monitoring Approach for Pipeline Defect Detection from MFL Inspection
Artificial Neural Networks(ANNS) have top level of capability to progress the estimation of cracks in metal tubes. The aim of this paper is to propose an algorithm to identify mod...
Saeedreza Ehteram, Seyed Zeinolabedin Moussavi, Mo...
IJCNN
2008
IEEE
14 years 1 months ago
Evolving a neural network using dyadic connections
—Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network ...
Andreas Huemer, Mario A. Góngora, David A. ...
EMS
2008
IEEE
14 years 2 months ago
Rough Set Generating Prediction Rules for Stock Price Movement
This paper presents rough sets generating prediction rules scheme for stock price movement. The scheme was able to extract knowledge in the form of rules from daily stock movement...
Hameed Al-Qaheri, Shariffah Zamoon, Aboul Ella Has...
IJCNN
2008
IEEE
14 years 1 months ago
Learning associations of conjuncted fuzzy sets for data prediction
— Fuzzy Associative Conjuncted Maps (FASCOM) is a fuzzy neural network that represents information by conjuncting fuzzy sets and associates them through a combination of unsuperv...
Hanlin Goh, Joo-Hwee Lim, Chai Quek
HIS
2004
13 years 9 months ago
Classification Ensembles for Shaft Test Data: Empirical Evaluation
: A-scans from ultrasonic testing of long shafts are complex signals. The discrimination of different types of echoes is of importance for non-destructive testing and equipment mai...
Kyungmi Lee, Vladimir Estivill-Castro