IEA/AIE '99 Proceedings of the 12th international conference on Industrial and engineering applications of artificial intelligence and expert systems: multiple approaches to intelligent systems
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Learning from Incomplete Data
AI techniques in modelling, assignment, problem solving and optimization
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
Missing data handling in multi-layer perceptron
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
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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 authors? earlier research for finding an appropriate input-output configuration of ANN models [16]. The introduced concept shows that it is worth purposely impairing the data used for learning to prepare the ANN model for handling incomplete data efficiently. The applicability of the proposed solution is demonstrated by the results of experimental runs with both artificial and real data. New experiments refer to the modelling and monitoring of cutting processes. Keywords: Neural Networks, Machine Learning, Applications to Manufacturing.