Symbolic vector dynamics for processing chaotic signals II: Noise reduction

Author

Wang, Kai ; Pei, Wenjiang ; Xia, Haishan ; He, Zhenya

Author_Institution

Dept. of Radio Eng., Southeast Univ., Nanjing

fYear

2008

fDate

7-11 June 2008

Firstpage

32

Lastpage

36

Abstract

The estimation precision of symbolic vector dynamic method is determined by the symbol error of symbolic sequence. If we get the original symbolic sequences from the noisy signal, then we can recover the original sequence without any error. The aim of this paper is to further develop symbolic vector dynamical estimation method which has been proposed [K. Wang, Phys. Lett. A 367 (2007) 316-321]. We will prove that any ML methods using MMSE criterion can not correct the symbolic error, because they mistakenly take the error generated by symbolic error as the noise. We can use SVD to develop a novel estimation method, which will correct symbolic error in high SNR.

Keywords

chaos; error correction; least mean squares methods; signal denoising; singular value decomposition; ML methods; MMSE; chaotic signal processing; estimation method; noise reduction; symbolic vector dynamic method; Additive white noise; Chaos; Cost function; Error correction; Lattices; Noise reduction; Signal processing; Signal to noise ratio; Spatiotemporal phenomena; Working environment noise; Noise Reduction; Symbolic Vector Dynamical;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks and Signal Processing, 2008 International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4244-2310-1

Electronic_ISBN

978-1-4244-2311-8

Type

conf

DOI

10.1109/ICNNSP.2008.4590303

Filename

4590303