Robust learning and identification of patterns in statistical process control charts using a hybrid RBF fuzzy ARTMAP neural network

The quality control of the manufacturing process in FMS is a critical factor, requiring flexible and intelligent quality control systems that are capable of autonomous pattern identification. Due to its learning and generalization capabilities, neural networks have good perspectives for this task. One of the most important difficulties in pattern identification with neural networks is the sensibility to the presentation order of the training patterns. This paper presents a hybrid network, RBF fuzzy-ARTMAP, which is capable of online incremental learning, 98% less sensible to the presentation order of training patterns than the fuzzy-ARTMAP network. Also, this work compares the performance of the RBF fuzzy-ARTMAP network with the fuzzy-ARTMAP network in the identification of six different "patterns" in SPC qualify control charts.