Zsolt János Viharos

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The paper describes a novel approach for learning and applying artificial neural network (ANN) models based on incomplete data. A basic novelty in this approach is not to replace the missing part of incomplete data but to train and apply ANN-based models in a way that they should be able to handle such situations. The root of the idea is inherited form the(More)
Reliable process models are extremely important in different fields of computer integrated manufacturing. They are required e.g. for selecting optimal parameters during process planning, for designing and implementing adaptive control systems or model based monitoring algorithms. Because of their model free estimation, uncertainty handling and learning(More)
The application of pattern recognition (PR) techniques, artificial neural networks (ANNs), and nowadays hybrid artificial intelligence (AI) techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The fundamental aim of the paper is to outline the importance of soft computing and hybrid AI techniques in(More)
Today's complex manufacturing systems operate in a changing environment rife with uncertainty. The performance of manufacturing companies ultimately hinges on their ability to rapidly adapt production to current internal and external circumstances. Partly based on a running national research project on digital enterprises and production networks, the paper(More)
This paper recapitulates the results of a long research on a family of artificial intelligence (AI) methods—relying on, e.g., artificial neural networks and search techniques—for handling systems with high complexity, high number of parameters whose input or output nature is partly unknown, high number of dependencies, as well as uncertainty and incomplete(More)
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