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A Fast Neural Network Simulator for State Transition Analysis

Atsushi KAMO, Hiroshi NINOMIYA, Teru YONEYAMA, Hideki ASAI

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Summary :

This paper describes an efficient simulator for state transition analysis of multivalued continuous-time neural networks, where the multivalued transfer function of neuron is regarded as a stepwise constant one. Use of stepwise constant method enables to analyse the state transition of the network without solving explicitly the differential equations. This method also enables to select the optimal timestep in numerical integration. The proposed method is implemented on the simulator and applied to the general neural network analysis. Furthermore, this is compared with the conventional simulators. Finally, it is shown that our simulator is drastically faster and more practical than the conventional simulators.

Publication
IEICE TRANSACTIONS on Fundamentals Vol.E82-A No.9 pp.1796-1801
Publication Date
1999/09/25
Publicized
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Type of Manuscript
Special Section PAPER (Special Section on Nonlinear Theory and Its Applications)
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