Title :
Excitation conditions for signed regressor least mean squares adaptation
Author :
Sethares, W.A. ; Mareels, Iven M Y ; Anderson, Brian D O ; Johnson, C. Richard, Jr. ; Bitmead, Robert R.
Author_Institution :
Sch. of Electr. Eng., Cornell Univ., Ithaca, NY, USA
fDate :
6/1/1988 12:00:00 AM
Abstract :
The stability of the signed regressor variant of least-mean-square (LMS) adaptation is found to be heavily dependent on the characteristics of the input sequence. Averaging theory is used to derive a persistence of excitation condition that guarantees exponential stability of the signed regressor algorithm. Failure to meet this condition (which is not equivalent to persistent excitation for LMS) can result in exponential instability, even with the use of leakage. This persistence of excitation condition is interpreted in both deterministic and stochastic settings
Keywords :
filtering and prediction theory; least squares approximations; signal processing; stability; LMS; averaging theory; deterministic setting; excitation condition; exponential instability; exponential stability; filtering; high data rate applications; input sequence; leakage; least mean squares adaptation; persistence; signal processing; signed regressor algorithm; stability; stochastic settings; Adaptive filters; Echo cancellers; Finite impulse response filter; Least squares approximation; Quantization; Speech processing; Stability; Stochastic processes; Strontium; Systems engineering and theory;
Journal_Title :
Circuits and Systems, IEEE Transactions on
