Title :
Temporal pattern generation based on anticipation
Author :
Wang, DeLiang ; Yuwono, Budi
Author_Institution :
Dept. of Comput. & Inf. Sci., Ohio State Univ., Columbus, OH, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
A neural network model of complex temporal pattern generation is proposed and investigated analytically and by computer simulation. Temporal pattern generation is based on recognition of the contexts of individual components. Based an its acquired experience the model actively yields system anticipation, which then compares with the actual input flow. A mismatch triggers self-organization of context learning, which ultimately leads to resolving various ambiguities in producing complex temporal patterns. We show analytically that the network model can learn to generate any complex temporal pattern. Multiple patterns can be acquired sequentially by the system, manifesting a form of retroactive interference. The model is consistent with cognitive studies of sequential learning
Keywords :
learning (artificial intelligence); pattern recognition; self-organising feature maps; ambiguity resolution; anticipation; complex temporal pattern generation; computer simulation; context learning; mismatch; neural network model; retroactive interference; self-organization; sequential learning; Assembly; Cognitive science; Computer networks; Context modeling; Detectors; Information analysis; Information science; Neural networks; Pattern analysis; Predictive models;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374737
