Some Background on Adaptive Estimation
نویسنده
چکیده
that produce the \best ̄t" to the data. Here, n denotes the noise or ̄tting error. By the term \best ̄t," we mean the minimization of the ̄tting error in some sense. For example, typical norm or norm-like functionals used as arguments in the above minimization problem are: 2 knk1 = ess. supt jn(t)j; the L1 norm. 2 knk2 = ¡R1 0 jn(t)j2 dt¢1=2; the L2 norm (energy). 2 knk2;± = ¡R1 0 e2±tjn(t)j2 dt¢1=2; the exponentially weighted L2 norm. 2 RMS(n) = 1n = inff1j9c : 8T; t > 0; R t+T t jn(¿ )j2 d¿ ∙ c+ 12Tg; the root mean square (RMS) power of the signal.
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