Exact Hessian Calculation in Feedforward FIR Neural Networks
نویسندگان
چکیده
FIR neural networks are feedforward neural networks with regular scalar synapses replaced by linear finite impulse response filters. This paper introduces the Second Order Temporal Backpropagation algorithm which enables the exact calculation of the second order error derivatives for a FIR neural network. This method is based on the error gradient calculation method first proposed by Wan and referred to as Temporal Backpropagation. A reduced FIR synapse model obtained by ignoring unnecessary time lags is proposed to reduce the number of network parameters.
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