Abstract
This paper introduces an automated diagnosis system with an increased and rated diagnosis reliability in a case where a modelling of a technical diagnosis task is impossible so far. To achieve this a redundant diagnosis concept which features three independent feature extraction methods and three independent classifier which are based on Distance Classification Methods (DCM), Fuzzy Sets (FS) and Neural Networks (NN) were developed. Furthermore the data can be visualised in different plots and also rated by the user.
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Lapp, A., Kranz, H.G. (1997). Neuro-fuzzy diagnosis system with a rated diagnosis reliability and visual data analysis. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052855
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DOI: https://doi.org/10.1007/BFb0052855
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