We introduce an adaptive prequential learning framework for Bayesian Network Classifiers which attempts to handle the costperformance trade-off and cope with concept drift. Our strategy for incorporating new data is based on bias management and gradual adaptation. Starting with the simple Näıve Bayes, we scale up the complexity by gradually increasing the maximum number of allowable attribute d...

Sampling strategy fundamentally influences the effectiveness of quality control with control charts. This study shows a simple approach for optimizing the control strategy for automatic multichannel analyzers that takes into account cost-efficiency considerations. Our main focus is on the frequency of controls necessary. The methods used are based on a field study (on a Hitachi/BM 747), the vie...

This paper formulates Shewhart mean (X-bar) and range (R) control charts for diagnosis and interpretation by artificial neural networks. Neural networks are trained to discriminate between samples from probability distributions considered within control limits and those which have shifted in both location and variance. Neural networks are also trained to recognize samples and predict future poi...

controlling a system with minimum information and regardless of dynamic equations which dominate systems is the aim of intelligent control. one of the common approaches for process control is applying shewhart's quality control charts. neuro-fuzzy networks, as one of the branches of artificial intelligence (ai), can play an effective role in the enforcement of process control's common...

Multivariate control charts such as Hotelling`s T^ 2 and X^ 2 are commonly used for monitoring several related quality characteristics. These control charts use correlation structure that exists between quality characteristics in an attempt to improve monitoring. The purpose of this article is to discuss some issues related to the G chart proposed by Levinson et al. [9] for detecting shifts in ...