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