Abstract
Local model networks are hybrid models which allow the easy integration of a priori knowledge, as well as the ability to learn from data to represent complex, multidimensional dynamic systems from data. The paper points out problems with global learning methods in local model networks. The bias/variance trade offs for local and global learning are examined, and it is illustrated that local learning has a regularizing effect that can make it favorable compared to global learning in some cases.
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