Parallel quantification computation method for global cross correlation of non-linear data

Abstract

The present invention discloses a parallel quantification computation method for global cross correlation of non-linear data. A parallel NLI algorithm is developed by using a CPU multi-thread technology and a CUDA based GPGPU multi-thread method in high-performance parallel computation, and an idea of parallel computation is introduced into the algorithm, so as to improve performance of the algorithm and assist analysis of a multi-channel signal; and data processing is extended from dual channels to multiple channels, and by combining a process where an S estimator performs data analysis from locally to globally to locally again, synchronization strength of a signal within a certain regional range is quantified, and more, more accurate and more useful information is mined out of original data to analyze a synchronization problem of the multi-channel signal, so that execution efficiency of the algorithm is greatly improved while the effect of the algorithm is reserved. As an experiment proves, the method has higher efficiency and usability in actual cross correlation analysis of multi-channel non-linear data.

Description

technical field [0001] The invention belongs to the technical field of data analysis, and relates to a data correlation analysis method, in particular to a parallel quantitative calculation method for global, non-linear data global cross-correlation that simultaneously reveals correlation strength and correlation direction. Background technique [0002] In scientific and practical engineering applications, the monitoring data of complex systems (such as brain nerve signals) have obvious nonlinear characteristics, and the research on the synchronization phenomenon between multiple components of complex systems is one of the most active topics in the field of complex system science today. . Taking neural signals as an example, a synchrony measure is an estimate of the synchrony of two or more consecutive time series produced during brain activity, with independent time series having low synchrony values ​​and related time series having high synchrony values. The use of multi-...

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Problems solved by technology

[0005] However, the application of NLI algorithm in multi-channel signal analysis is highly computationally intensive. NLI algorithm is mainly used to study the signal relationship between two channels. Although this algorithm has its unique advantages in processing EEG signals, when the amount of data is large When the number of channels is large or the number of channels is large, the execution efficiency of the algorithm is obviously reduced, which restricts the development and use of the algorithm.

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