Title :
Learning temporal patterns in recurrent neural networks
Author_Institution :
Dept. of Math. Eng. & Inf. Phys., Tokyo Univ., Japan
Abstract :
General learning algorithms for recurrent neural networks that can be used for both discrete-time and continuous-time models are described. They are based on the notion of the derivatives of mappings between functions of time. Simulation results for learning rhythmical sequences are shown. The method can also be applied to networks of higher-order neuron models and multiple time delay connections
Keywords :
learning systems; neural nets; continuous-time models; discrete-time; higher-order neuron models; learning systems; multiple time delay connections; recurrent neural networks; rhythmical sequences; temporal patterns; Computer networks; Equations; Humans; Information processing; Intelligent networks; Joining processes; Output feedback; Physics; Recurrent neural networks; Supervised learning;
Conference_Titel :
Systems, Man and Cybernetics, 1990. Conference Proceedings., IEEE International Conference on
Conference_Location :
Los Angeles, CA
Print_ISBN :
0-87942-597-0
DOI :
10.1109/ICSMC.1990.142085
