The Mahalanobis-taguchi System - Neural Network Algorithm For Data-mining In Dynamic Environments

摘要

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.