  Approaches to machine intelligence based on brain models have stressed the
use of neural networks for generalization. Here we propose the use of a hybrid
neural network architecture that uses two kind of neural networks
simultaneously: (i) a surface learning agent that quickly adapt to new modes of
operation; and, (ii) a deep learning agent that is very accurate within a
specific regime of operation. The two networks of the hybrid architecture
perform complementary functions that improve the overall performance. The
performance of the hybrid architecture has been compared with that of
back-propagation perceptrons and the CC and FC networks for chaotic time-series
prediction, the CATS benchmark test, and smooth function approximation. It has
been shown that the hybrid architecture provides a superior performance based
on the RMS error criterion.
