Improved Redundant Rule-Based Stochastic Gradient Algorithm for Time-Delayed Models Using Lasso Regression

This paper proposes an improved redundant rule based lasso regression stochastic gradient (RR-LR-SG) algorithm for time-delayed models. The improved SG algorithm can update the parameter elements with different step-sizes and directions, thus it is more adaptive; while the lasso regression method can pick out the small weights from the redundant parameter vector, it therefore can obtain the time-delay easily. To show the effectiveness of the proposed algorithm, the convergence analysis is also given. The simulated numerical results are consistent with the analytically derived results of the proposed algorithm.