Abstract
One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.
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© 2004 Springer-Verlag Berlin Heidelberg
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Choi, B., Hendtlass, T., Bluff, K. (2004). A Comparison of Neural Network Input Vector Selection Techniques. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_1
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DOI: https://doi.org/10.1007/978-3-540-24677-0_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
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