发源
发表于 2025-3-25 03:48:52
Luis M. Camarinha-Matos,Hamideh AfsarmaneshThis chapter briefly describes how to deal with complex signals in adaptive-filtering context in a simple manner; for further details the reader is encouraged to refer to .
亲属
发表于 2025-3-25 07:59:41
http://reply.papertrans.cn/15/1447/144641/144641_22.png
Prologue
发表于 2025-3-25 12:08:32
http://reply.papertrans.cn/15/1447/144641/144641_23.png
tympanometry
发表于 2025-3-25 18:33:46
Luís Seabra Lopes,Luís M. Camarinha-Matos the concepts that are directly relevant to adaptive filtering. The properties of thecorrelation matrix of the input signal vector are investigated in some detail, since they play a key role in the statistical analysis of the adaptive-filtering algorithms.
abreast
发表于 2025-3-25 22:52:27
http://reply.papertrans.cn/15/1447/144641/144641_25.png
Custodian
发表于 2025-3-26 03:35:30
R. Roshardt,C. Uhrhan,T. Waefler,S. Weikective of the alternative LMS-based algorithms is either to reduce computational complexity or convergence time. In this chapter, several LMS-based algorithms are presented and analyzed, namely, the quantized-error algorithms , the frequency-domain (or transform-domain) LMS algorithm ,
organic-matrix
发表于 2025-3-26 07:59:19
http://reply.papertrans.cn/15/1447/144641/144641_27.png
离开就切除
发表于 2025-3-26 09:18:29
R. Roshardt,C. Uhrhan,T. Waefler,S. Weikient. These characteristics are easily observable in stationary environments. In general fast-converging algorithms tend to be very dynamic, a feature not necessarily advantageous after convergence in a stationary environment. In this chapter, an alternative formulation to govern the updating of the
不安
发表于 2025-3-26 13:07:41
https://doi.org/10.1007/978-0-387-35065-3alization are very attractive because they allow modular implementation and require a reduced number of arithmetic operations (of order .) . As a consequence, the lattice recursive least-squares (LRLS) algorithms are considered fast implementations of the RLS problem.
garrulous
发表于 2025-3-26 17:51:42
http://reply.papertrans.cn/15/1447/144641/144641_30.png