A global optimization method for continuous-time adaptive recursive filters

A major drawback of recursive adaptive filters based on gradient methods is that convergence to a global minimum is not always achieved. This is due to a nonconvex mean square error (MSE) performance surface. This article develops a continuous-time least mean square algorithm that converges to the global minimum with probability one.