A multi-layer neural network model for detecting changes in the process mean
Computers and Industrial Engineering
A neural network based model for abnormal pattern recognition of control charts
Computers and Industrial Engineering
A neural network-based procedure for the monitoring of exponential mean
Computers and Industrial Engineering
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Using novelty detection to identify abnormalities caused by mean shifts in bivariate processes
Computers and Industrial Engineering
Artificial neural networks to classify mean shifts from multivariate χ2 chart signals
Computers and Industrial Engineering
A hybrid learning-based model for on-line detection and analysis of control chart patterns
Computers and Industrial Engineering
Improving the Efficiency of Counting Defects by Learning RBF Nets with MAD Loss
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
MAD Loss in Pattern Recognition and RBF Learning
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Random Projection RBF Nets for Multidimensional Density Estimation
International Journal of Applied Mathematics and Computer Science - Issues in Fault Diagnosis and Fault Tolerant Control
RBF nets in faults localization
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
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We propose a new radial basis function (RBF) neural network for probability density function estimation. This network is used for detecting changes in multivariate processes. The performance of the proposed model is tested in terms of the average run lengths (ARL), i.e., the average time delays of the change detection. The network allows the processing of large streams of data, memorizing only a small part of them. The advantage of the proposed approach is in the short and reliable net training phase.