Using virtual sample generation to build up management knowledge in the early manufacturing stages

Since only few examples can be obtained in the early stages in a manufacturing system and that fewer exemplars usually lead to a lower learning accuracy, this research uses intervalized kernel methods of Density Estimation to improve the small-data-set learning. Used techniques include the Intervalization Process to improve the kernel density estimation and virtual sample generation to produce extra information for expediting the learning. Results obtained from the provided example, using a back-propagation neural network as the learning tool, show that this unique approach is an effective method of scheduling knowledge creation for a system in the early stages.