The effects of precision constraints in a backpropagation learning network
Summary form only given, as follows. A study is presented of precision constraints imposed by a hybrid chip architecture with analog neurons and digital backpropagation calculations. Conversions between the analog and digital domains and weight storage restrictions impose precision limits on both analog and digital calculations. It is shown through simulations that a learning system of this nature can be implemented in spite of limited resolution in the analog circuits and using fixed-point arithmetic to implement the backpropagation algorithm. >
This work revisits the constant stepsize stochastic approximation algorithm for tracking a slowly moving target and obtains a bound for the tracking error that is valid for all time, using the Alekseev non-linear variation of constants formula.