Characterization of chaotic multiscale features on the time series of melt index in industrial propylene polymerization system

Wang, Ting; Liu, Xinggao; Zhang, Zeyin

doi:10.1016/j.jfranklin.2013.09.022

Cited by 9 publications

(3 citation statements)

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“…For the MI series, it is nonlinear and stable. We demonstrate that the random nature of MI can be explained as a chaotic phenomenon [46]. After comparison tests for predicting MI, the two algorithms mentioned previously are as good as the proposed IFOA in accuracy but little worse in learning speed.…”

Section: Resultsmentioning

confidence: 65%

Improved fruit fly optimization algorithm optimized wavelet neural network for statistical data modeling for industrial polypropylene melt index prediction

Wang

Zhang

Liu

2015

Journal of Chemometrics

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This paper presents the development of wavelet neural network (WNN) with an improved fruit fly optimization algorithm (IFOA) for the melt index prediction in the industrial propylene polymerization process. The structure, calculation, and prediction process of WNN are proposed, and the improved details of IFOA are introduced, which can enhance the searching efficiency and improve the searching quality over the traditional fruit fly optimization algorithm. Finally, the WNN-IFOA model can obtain the least predicting errors compared with other existing models and shows better generality for the online melt index prediction from the experimental results.

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Section: Resultsmentioning

confidence: 65%

Improved fruit fly optimization algorithm optimized wavelet neural network for statistical data modeling for industrial polypropylene melt index prediction

Wang

Zhang

Liu

2015

Journal of Chemometrics

Self Cite

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“…In the feature transformation phase, we project the original time series into the reconstructed phase space in the higher dimensions using the delayed time method [32]. For data assumed to be deterministically chaotic, the system dynamics in the reconstructed data series in the phase space is the same as those of the original data.…”

Section: A Multivariate Emd Based Forecasting Model For Crude Oil Primentioning

confidence: 99%

Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price

Zha

et al. 2016

Sustainability

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Abstract:Recent empirical studies reveal evidence of the co-existence of heterogeneous data characteristics distinguishable by time scale in the movement crude oil prices. In this paper we propose a new multivariate Empirical Mode Decomposition (EMD)-based model to take advantage of these heterogeneous characteristics of the price movement and model them in the crude oil markets. Empirical studies in benchmark crude oil markets confirm that more diverse heterogeneous data characteristics can be revealed and modeled in the projected time delayed domain. The proposed model demonstrates the superior performance compared to the benchmark models.

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“…Nowadays, the EMD method has been widely applied in many engineering fields, such as machinery, transportation, marine, medicine, electricity, etc. [8,13,21,27]. The basic idea of EMD decomposition is that any signal is composed of series basic model components, with their amplitudes and phases varying with time.…”

Section: Introductionmentioning

confidence: 99%