Adjacent errors of stochastic gradient algorithms under general error criteria

This paper gives a method to distinguish between the transient and steady states of stochastic gradient algorithms under general error criteria using inequalities that exist among a set of output errors called adjacent errors. Based on these inequalities, a variable step size LMF algorithm is proposed which improves convergence and gives lower misadjustment.