سال انتشار: ۱۳۹۱

محل انتشار: پانزدهمین کنفرانس دانشجویی مهندسی برق ایران

تعداد صفحات: ۶

نویسنده(ها):

Jalil Addeh –
Hossein Babaee, –
Ata Ebrahimzadeh –

چکیده:

Precise and fast control chart pattern (CCP) recognition is important for monitoring process environments to achieve appropriate control and to produce high qualityproducts. This paper presents a method for recognition of common types of CCP. The proposed method includes two mainmodules: a feature extraction module and a classifier module. Inthe feature extraction module, the multi-resolution wavelets (MRW) are proposed as the effective features for representationof CCPs. In the classifier module, multi layer perceptron neural networks (MLPNNs) are applied. In MLPNNs training,improved back-propagation algorithm is used to help the network avoid the local minima problem due to neuron saturation in the hidden layer. Simulation results show that the proposed system has high recognition accuracy