The Gauss-Newton Learning Method for a Generalized Dynamic Synapse Neural Network
نویسندگان
چکیده
A new architecture for Dynamic Synapse Neural Networks (DSNNs) has been introduced based on incorporating a continuous nonlinear mechanism to simulate synaptic neurotransmitter release, adding a nonlinear output layer, and utilizing a Gauss-Newton learning method to train the network. We applied this network to simulate two nonlinear dynamical systems and then tried to identify the dynamical systems by generating random noise observation data. The network estimation error per sample on the training phase was less than approximately 2% and on the test set was less than approximately 3%.
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تاریخ انتشار 2002