The Mahalanobis–Taguchi system – Neural network algorithm for data-mining in dynamic environments

Data-mining analysis has two important processes: searching for patterns and model construction. From previous works finding that the Mahalanobis–Taguchi System (MTS) algorithm is successful and effective for data-mining. Conventional research in searching for patterns and modeling in data-mining is typically in a static state. Studies using a dynamic environment for data-mining are scarce. The artificial neural network (ANN) algorithm can solve dynamic condition problems. This study integrates the MTS and ANN algorithm to create the novel (MTS–ANN) algorithm that solves the pattern-recognition problems and can be applied to construct a model for manufacturing inspection in dynamic environments. From the results of the experiment, we find that the methodology of the MTS algorithm can easily solves pattern-recognition problems, and is computationally efficient as well as the ANN algorithm is a simple and efficient procedure for constructing a model of a dynamic system. The MTS–ANN algorithm is good at pattern-recognition and model construction of dynamic systems.