Neural Network Based Non-Parametric Control Chart for Location
Sharadkumar Nimbale
, Vikas Ghute
Abstract
Standard control charts for monitoring the process parameters are often based on the assumption that the process data follow a specific parametric distribution, such as normal. In many applications we do not have enough information to make this assumption and in such situations development and application of control charts that do not depend on a particular distributional assumption is desirable. In recent years, artificial neural network (ANN) model are proposed in literature as an alternative to the traditional control charts and no assumption of underlying process distribution is required for the implementation of the ANN models. In this paper, ANN model is developed to compare with the nonparametric control chart for monitoring the shifts in process location when underlying distribution is not known exactly. The average run length (ARL) performance of the proposed ANN model is evaluated through a simulation study and is compared with the nonparametric sign control chart. The simulation study indicates that the proposed ANN scheme is more efficient than the nonparametric sign control chart for detecting the shifts in the process location.
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