Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Hybrid neural plausibility networks for news agents
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Field Guide to Dynamical Recurrent Networks
Field Guide to Dynamical Recurrent Networks
Self-organization of behavioral primitives as multiple attractor dynamics: A robot experiment
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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Recent research has revealed that hierarchical linguistic structures can emerge in a recurrent neural network with a sufficient number of delayed context layers. As a representative of this type of network the Multiple Timescale Recurrent Neural Network (MTRNN) has been proposed for recognising and generating known as well as unknown linguistic utterances. However the training of utterances performed in other approaches demands a high training effort. In this paper we propose a robust mechanism for adaptive learning rates and internal states to speed up the training process substantially. In addition we compare the generalisation of the network for the adaptive mechanism as well as the standard fixed learning rates finding at least equal capabilities.