Title :
Inherent structure detection by neural sequential associator
Author_Institution :
Hitachi Ltd., Kawasaki, Japan
Abstract :
A sequential associator based on a feedback multilayer neural network is proposed to analyze inherent structures in a sequence generated by a nonlinear dynamical system and to predict a future sequence based on these structures. The network represents time correlations in the connection weights during learning. It is capable of detecting the inherent structure and explaining the behavior of systems. The structure of the neural sequential associator, inherent structure detection, and the optimal network size based on the use of an information criterion are discussed
Keywords :
identification; neural nets; nonlinear systems; predictive control; feedback multilayer neural network; inherent structure detection; learning systems; neural sequential associator; nonlinear dynamical system; sequence prediction; time correlations; Computer vision; Design methodology; Detectors; Equations; Laboratories; Linear systems; Multi-layer neural network; Neural networks; Neurons; Nonlinear dynamical systems;
Conference_Titel :
Neural Networks, 1991. 1991 IEEE International Joint Conference on
Print_ISBN :
0-7803-0227-3
DOI :
10.1109/IJCNN.1991.170704
