Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer

Abstract

An adaptive resonance theory (ART) based, general-purpose control chart pattern recognizer (CCPR) which is capable of fast and cumulative learning is presented. The implementation of this ART-based CCPR was made possible by introducing two key alternatives, that is, incorporating a synthesis layer in addition to the existing two-layer architecture and adopting a quasi-supervised training strategy. A detailed algorithm with the training and the testing modes was presented. Extensive simulations and performance evaluations were conducted and proved that this ART-based CCPR indeed possesses the capability of fast and cumulative learning. When compared with a back-propagation pattern recognizer (BPPR), the ART-based CCPR is superior on cyclic patterns, inferior on mixture patterns, and comparable on other patterns. Furthermore, an ART-based CCPR is easier to develop since it needs fewer training templates and takes less time to learn. This study not only provides a basis for understanding the capabilities of ART-based neural networks on control chart pattern recognition but re-confirms the applicability of the neural network approach.