Abstract
The increased dependence on artificial neural network (ANN) models leads to a key question – will the ANN models provide accurate and reliable predictions? However, this important issue has received little systematic study. Thus this paper makes general researches on verification and validation (V&V) of ANN models. Basic problems for V&V of ANN models are explicitly presented, a new V&V approach for ANN models is developed, V&V methods for ANN models are deeply discussed, further research areas for V&V of ANN models are recommended, and an example is given.
Foundation Item: Project supported by Natural Science Foundation of China (grant No. 60434010).
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Liu, F., Yang, M. (2005). Verification and Validation of Artificial Neural Network Models. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_137
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DOI: https://doi.org/10.1007/11589990_137
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
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