No / Extensive research has been performed for developing knowledge based intelligent monitoring systems for improving the reliability of manufacturing processes. Due to the high expense of obtaining knowledge from human experts, it is expected to develop new techniques to obtain the knowledge automatically from the collected data using data mining techniques. Inductive learning has become one of the widely used data mining methods for generating decision rules from data. In order to deal with the noise or uncertainties existing in the data collected in industrial processes and systems, this paper presents a new method using fuzzy logic techniques to improve the performance of the classical inductive learning approach. The proposed approach, in contrast to classical inductive learning method using hard cut point to discretize the continuous-valued attributes, uses soft discretization to enable the systems have less sensitivity to the uncertainties and noise. The effectiveness of the proposed approach has been illustrated in an application of monitoring the machining conditions in uncertain environment. Experimental results show that this new fuzzy inductive learning method gives improved accuracy compared with using classical inductive learning techniques.
| Identifer | oai:union.ndltd.org:BRADFORD/oai:bradscholars.brad.ac.uk:10454/2362 |
| Date | January 2004 |
| Creators | Peng, Yonghong |
| Source Sets | Bradford Scholars |
| Language | English |
| Detected Language | English |
| Type | Article, Published version |
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