Abstract
Multivariate time series analysis in climate and environmental research always requires to process huge amount of data. Inspired by human nervous system, the artificial neural network methodology is a powerful tool to handle this kind of difficult and challenge problems and has been widely used to investigate mechanism of climate change and predict the climate change trend. The main advantage is that artificial neural networks make full use of some unknown information hidden in climate data although they cannot extract it. In this chapter, we will introduce various neural networks, including linear networks, radial basis function networks, generalized regression networks, Kohonen self-organizing networks, learning vector quantization networks, and Hopfield networks.
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Zhang, Z. (2018). Artificial Neural Network. In: Multivariate Time Series Analysis in Climate and Environmental Research. Springer, Cham. https://doi.org/10.1007/978-3-319-67340-0_1
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DOI: https://doi.org/10.1007/978-3-319-67340-0_1
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