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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/26063

Title: Some effects of database corruption in system prediction performance
Authors: Chamberlain, Matthew R.
Jackson, Michael R.
Parkin, Robert M.
Keywords: A priori
Neural network
Data corruption
Motor control
Issue Date: 2011
Publisher: ICMT Organizing Committee
Citation: CHAMBERLAIN, M., JACKSON, M. and PARKIN, R., 2011. Some effects of database corruption in system prediction performance. IN: Proceedings of 2011 15th International Conference on Mechatronics Technology (ICMT2011), Melbourne, Australia, 30 November-2 December 2011.
Abstract: Many types of intelligent adaptive systems use vast databases of a-priori knowledge during training phases. These systems are then reliant on both the accuracy of this data and on the breadth of the data. It is assumed whilst training that the data encompasses the total operating window for the system in enough detail to generate an accurate ‘black box’ model of the plant under control. It may be that under certain unforeseen operating conditions, or in a scenario where there is little prior knowledge, the system may be forced to operate outside the scope of the original a-priori knowledge. Lastly the data gathered into the a-priori source may have been unintentionally corrupted. This paper aims to examine some of these effects upon two common adaptive intelligent tools, neural networks and an adaptive neuro-fuzzy inference system, ANFIS, network.
Version: Accepted for publication
URI: https://dspace.lboro.ac.uk/2134/26063
Publisher Link: https://sites.google.com/site/2011icmt/
Appears in Collections:Closed Access (Mechanical, Electrical and Manufacturing Engineering)

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