Paper Info
Title
Improved statistical features-based control chart patterns recognition using ANFIS with fuzzy clustering
Abstract
Various types of abnormal control chart patterns can be linked to certain assignable causes in industrial processes. Hence, control chart patterns recognition methods are crucial in identifying process malfunctioning and source of variations. Recently, the hybrid soft computing methods have been implemented to achieve high recognition accuracy. These hybrid methods are complicated, because they require optimizing algorithms. This paper investigates the design of efficient hybrid recognition method for widely investigated eight types of X-bar control chart patterns. The proposed method includes two main parts: the features selection and extraction part and the recognizer design part. In the features selection and extraction part, eight statistical features are proposed as an effective representation of the patterns. In the recognizer design part, an adaptive neuro-fuzzy inference system (ANFIS) along with fuzzy c-mean (FCM) is proposed. Results indicate that the proposed hybrid method (FCM-ANFIS) has a smaller set of features and compact recognizer design without the need of optimizing algorithm. Furthermore, computational results have achieved 99.82% recognition accuracy which is comparable to published results in the literature.
Year
DOI
Venue
2019
10.1007/s00521-018-3388-2
Neural Computing and Applications
Keywords
Field
DocType
Control chart patterns recognition, Fuzzy clustering, ANFIS, Statistical features
Fuzzy clustering,Pattern recognition,Fuzzy logic,Control chart,Artificial intelligence,Adaptive neuro fuzzy inference system,Soft computing,Mathematics,Machine learning,Inference system
Journal
Volume
Issue
ISSN
31.0
10
1433-3058
Citations 
PageRank 
References 
0
0.34
19
Authors
2
Name
Order
Citations
PageRank
Munawar Zaman100.34
Adnan Hassan2143.48