Differential correlation analysis of short time series
Włodzimierz Pogribny, Igor Rozhankivsky, Zdzislav Drzycimski, Andrzej Milewski · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
ABSTRACT In this paper effective differential algorithms of the correlation analysis (CA) of short time series in real time oriented onmulti-processand and neural systems have been analysed. According to this analog signal representation delta modulation(DM) format as well as the correaltion analysis of recurrent algorithms have also been considered. Particular attention hasbeen paid to the use of sign delta modulation (SignDM) in CA proposed by the authors. Correlation analysis in SignDMformat was illustrated with the results of computer simulations. The algorithms that were worked out are regular andparticularly useful in neural systems.Keywords: differential correlation analysis, sign delta modulation, short time series. 1. ENTRODUCTION Correlation analysis (CA) is a very effective kind of digital stochastic signal processing for determining stochastic relationsamong them. On the basis of CA also the periods of the noise periodic determined signals, time or phase shifts etc. can bespecified. Therefore, CA is so often used in signal isolation: both l-D signals (e.g. radio signals) and 2-D signals (e.g.