Reza Gholipour - Faculty of Electrical and Computer Engineering, Babol ( Noushirvani) University of Technology, Babol
Jalil Addeh - Faculty of Electrical and Computer Engineering, Babol ( Noushirvani) University of Technology, Babol
Ali Reza Sahab - Lahijan Branch Islamic Azad University
Samareh Fallah - Department of food science and technology, Faculty of Agriculture and Natural Resources, science and research Branch, Islamic Azad university, Tehran

[Majid Masoumi] Electrical Engineering Department, Islamic Azad University Qazvin Branch, Qazvin, Iran Unnatural patterns in the control charts can be associated with a specific set of assignable causes for process variation. Hence pattern recognition is very useful in identifyingprocess problem. This paper presents a novel hybrid intelligent method for recognition of common types of control chartpatterns (CCP). The proposed method includes three main modules: a feature extraction module, a classifier module and an optimization module. In the feature extraction module, aproper set of the shape and statistical features are proposed as the efficient characteristic of the patterns. In the classifiermodule adaptive neuro-fuzzy inference system (ANFIS) is proposed that is a hybrid combination of artificial neural networks (ANN) and fuzzy inference system (FIS). In ANFIS training, the vector of radius has very important role for its recognition accuracy. Therefore, in the optimization module,bees algorithm (BA) is proposed for finding optimum vector of radius. Simulation results show that the proposed system hashigh recognition accuracy.

ANFIS; Bees algorithm; Control chart patterns; Shape feature; Statistical feature