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A platform for research: civil engineering, architecture and urbanism
Fuzzy rule classifier: Capability for generalization in wood color recognition
In this paper, a classification method based on fuzzy linguistic rules is exposed. It is applied for the recognition of the gradual color of wood in an industrial context. The wood, which is a natural material, implies uncertainty in the definition of its color. Moreover, the timber context leads obtaining imprecise data. Several factors can have an impact on the sensors (ageing of the acquisition system, variation of the ambient temperature, etc.). Finally, the data sets are often small and incomplete. Thus the proposed method must work within these constraints, and must be compatible with the time-constraint of the system. This generally imposes a weak complexity of the recognition system. The Fuzzy Rule Classifier is split in two main parts, the fuzzification step and the rule generation step. To improve the tuning of this classifier, a specific fuzzification method is presented and compared with more classical ones. Several comparisons have been made with other classification method such as neural network or support vector machine. This experimental study showed the suitability of the proposed approach essentially in term of generalization capabilities from small data sets, and recognition rate improvement.
Fuzzy rule classifier: Capability for generalization in wood color recognition
In this paper, a classification method based on fuzzy linguistic rules is exposed. It is applied for the recognition of the gradual color of wood in an industrial context. The wood, which is a natural material, implies uncertainty in the definition of its color. Moreover, the timber context leads obtaining imprecise data. Several factors can have an impact on the sensors (ageing of the acquisition system, variation of the ambient temperature, etc.). Finally, the data sets are often small and incomplete. Thus the proposed method must work within these constraints, and must be compatible with the time-constraint of the system. This generally imposes a weak complexity of the recognition system. The Fuzzy Rule Classifier is split in two main parts, the fuzzification step and the rule generation step. To improve the tuning of this classifier, a specific fuzzification method is presented and compared with more classical ones. Several comparisons have been made with other classification method such as neural network or support vector machine. This experimental study showed the suitability of the proposed approach essentially in term of generalization capabilities from small data sets, and recognition rate improvement.
Fuzzy rule classifier: Capability for generalization in wood color recognition
Fuzzy-Regel-Klassifizierer: Fähigkeit zur Verallgemeinerung in der Holzfarberkennung
Bombardier, Vincent (author) / Schmitt, Emmanuel (author)
Engineering Applications of Artificial Intelligence ; 23 ; 978-988
2010
11 Seiten, 9 Bilder, 9 Tabellen, 69 Quellen
Article (Journal)
English
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