Training and application of artificial neural networks with incomplete data

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.