원문정보
초록
영어
Focused on the problem of imperfect information in the process of reconstruction from single time series, a new technology for phase space reconstruction from multi-time series based on the data fusion is proposed. Firstly, the methods Cao and mutual information are used to select the reconstruction parameters, time delay and embedded dimension; secondly, the social cognitive optimization algorithm is brought to calculate the weights for each variable; thirdly, an adaptive weighted fusion estimating method is applied for data fusion; lastly, the effectiveness of the methods mentioned in this paper is demonstrated by the analysis results of one case study of real chemical plant data sets, and the proposed methods in this paper can improve the completeness of the information of the reconstructed phase space, which is also a good foundation for further analysis of complex system.
목차
1. Introduction
2. Phase Space Reconstruction from Multi-Time Series
2.1 The Mathematic Model of Phase Space Reconstruction
2.2 Adaptive Weighted Fusion Estimating Based Data Fusion
2.3 Weight Optimization Based On Social Cognitive Algorithm
3. Process of Phase Space Reconstruction from Multi-Time Series
4. The Application and Effects Analysis
5. Conclusions
References