关键词: 质量控制, 统计过程控制, 自相关, Hopfield神经网络, 联想存储, 模拟

Abstract: To improve the sensitivity and reliability of Statistical Process Control (SPC) methods for autocorrelation processes, a Hopfield Neural Networks (HNN) was used to detect continuous mean shift of autocorrelation processes. Firstly, observation values of quality characteristics were decomposed into original patterns and background noises. Then, the original patterns were stored into HNN through dynamic encoding. After that, the observation values were encoded by using the principles of ‘relative increase’ and ‘beyond mean’, so that the background noises were filtered out by associative learning and thereby the original patterns were retrieved. As a result, the continuous shifts were detected. Research showed that the proposed method was applicable to processes with various autocorrelation parameters. It needed neither statistical model of processes nor large historical sample for network weights training. All of these manifested high sensitivity and reliability of the proposed method.

Key words: quality control, statistical process control, autocorrelation, Hopfield neural networks, associative storage, simulation