Hybrid Neural Network Architecture for On-Line Learning

Understand

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.