A new least squares parameter estimator for nonlinear regression equations with relaxed excitation conditions and forgetting factor

In this note a new high performance least squares parameter estimator is proposed. The main features of the are: (i) global exponential convergence guaranteed for all identifiable linear regression equations; (ii) it incorporates forgetting factor allowing to preserve alertness time-varying parameters; (iii) thanks addition mixing step relies on set scalar equations ensuring superior transient performance; (iv) applicable nonlinearly parameterized regressions verifying monotonicity condition and class systems with switched (v) shown that bounded-input-bounded-state stable respect additive disturbances; (vi) continuous discrete-time versions are given. proposed illustrated series examples reported in literature.