Control chart pattern identification using a synergy between neural networks and bees algorithm

P. K. Wong*, A. Chua

Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119260, Singapore


In the recent years, as an alternative of the traditional process quality management methods, such as Shewhart statistical process control (SPC), artificial neural networks (ANN) have been widely used to recognize the abnormal pattern of control charts. A common problem of existing approaches to control chart patterns (CCPs) recognition is false classification between different types of CCPs that share similar features in a real-time process-monitoring scenario, in which only limited pattern points are available for recognition. This study presents an automatic recognition system for control chart patterns recognition based on bees algorithm (BA) and artificial neural networks. In this study, BA is used for reducing the dimension of CCPs database and ANN is used for intelligent classification. The proposed BA +ANN system performance is compared with ANN model. The dimension of input feature space is reduced from nine to four by using BA. The proposed method (BA+ANN) uses a multiplayer perceptrons (MLP) neural network as pattern recognizer. The MLP architecture has been successfully applied to solve some difficult and diverse problems in modeling, prediction and pattern classification. Simulation results show that the proposed method (BA+ANN) has very high recognition accuracy. This high efficiency is achieved with only little features, which have been selected using BA.


Artificial neural network, Control chart patterns, Bees algorithm, Shape feature, Feature selection

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Article history

Received 5 November 2018, Received in revised form 20 February 2019, Accepted 27 February 2019

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Wong PK and Chua A (2019). Control chart pattern identification using a synergy between neural networks and bees algorithm. Annals of Electrical and Electronic Engineering, 2(4): 8-13

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