The Experts below are selected from a list of 90681 Experts worldwide ranked by ideXlab platform
Zhi-qiang Jiang - One of the best experts on this subject based on the ideXlab platform.
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Multifractal Cross Wavelet Analysis
Fractals, 2017Co-Authors: Zhi-qiang Jiang, Xing-lu Gao, Wei-xing Zhou, H. Eugene StanleyAbstract:Complex systems are composed of mutually interacting components and the output values of these components are usually long-range cross-correlated. We propose a method to characterize the joint multifractal nature of such long-range cross correlations based on Wavelet Analysis, termed multifractal cross Wavelet Analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, the empirical joint multifractality of MFXWT is found to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indexes and uncover intriguing joint multifractal nature in pairs of index returns and volatilities.
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multifractal cross Wavelet Analysis
Fractals, 2017Co-Authors: Zhi-qiang Jiang, Xing-lu Gao, Wei-xing Zhou, Eugene H StanleyAbstract:Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using Wavelet Analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross Wavelet Analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.
Jia-cong Cao - One of the best experts on this subject based on the ideXlab platform.
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study of forecasting solar irradiance using neural networks with preprocessing sample data by Wavelet Analysis
Energy, 2006Co-Authors: Jia-cong Cao, S H CaoAbstract:Artificial neural network is a powerful tool in the forecast of solar irradiance. In order to gain higher forecasting accuracy, artificial neural network and Wavelet Analysis have been combined to develop a new method of the forecast of solar irradiance. In this paper, the data sequence of solar irradiance as samples is mapped into several time-frequency domains using Wavelet transformation, and a recurrent back-propagation (BP) network is established for each domain. The solar irradiance forecasted equals the algebraic sum of the components, which were predicted correspondingly by the established networks, of all the time-frequency domains. A discount coefficient method is adopted in updating the weights and biases of the networks so that the late forecasts play more important roles. On the basis of the principle of combination of artificial neural networks and Wavelet Analysis, a model is completed for fore-casting solar irradiance. Based on the historical day-by-day records of solar irradiance in Shanghai an example of forecasting total irradiance is presented. The results of the example indicate that the method makes the forecasts much more accurate than the forecasts using the artificial neural networks without combination with Wavelet Analysis.
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forecast of solar irradiance using recurrent neural networks combined with Wavelet Analysis
Applied Thermal Engineering, 2005Co-Authors: Shuanghua Cao, Jia-cong CaoAbstract:In this paper, artificial neural network is combined with Wavelet Analysis for the forecast of solar irradiance. This method is characteristic of the preprocessing of sample data using Wavelet transformation for the forecast, i.e., the data sequence of solar irradiance as the sample is first mapped into several time-frequency domains, and then a recurrent BP network is established for each domain. The forecasted solar irradiance is exactly the algebraic sum of all the forecasted components obtained by the respective networks, which correspond respectively the time-frequency domains. Discount coefficients are applied to take account of different effect of different time-step on the accuracy of the ultimate forecast when updating the weights and biases of the networks in network training. On the basis of combination of recurrent BP networks and Wavelet Analysis, a model is developed for more accurate forecasts of solar irradiance. An example of the forecast of day-by-day solar irradiance is presented in the paper, the historical day-by-day records of solar irradiance in Shanghai constituting the data sample. The results of the example show that the accuracy of the method is more satisfactory than that of the methods reported before.
Hongzhong Li - One of the best experts on this subject based on the ideXlab platform.
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Wavelet Analysis of pressure fluctuation signals in a bubbling fluidized bed
Chemical Engineering Journal, 1999Co-Authors: Xuesong Lu, Hongzhong LiAbstract:Pressure fluctuations of fluidized beds have been used to evaluate the fluidization quality. In bubbling fluidized beds, the bed behavior is characterized by bubbling. The information indicated by the pressure fluctuation signals can be applied to describe those behaviors. The signals representing the characteristics of bubbling can be separated from original signals through discrete Wavelet Analysis. Considering the principles of Wavelet, the Scale 4 detail signals can reveal the bubble behaviors in a fluidized bed. The peak frequency of the Scale 4 detail signal stands for the bubbling frequency and the peak amplitude for the bubble size.
Eugene H Stanley - One of the best experts on this subject based on the ideXlab platform.
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multifractal cross Wavelet Analysis
Fractals, 2017Co-Authors: Zhi-qiang Jiang, Xing-lu Gao, Wei-xing Zhou, Eugene H StanleyAbstract:Complex systems are composed of mutually interacting components and the output values of these components usually exhibit long-range cross-correlations. Using Wavelet Analysis, we propose a method of characterizing the joint multifractal nature of these long-range cross correlations, a method we call multifractal cross Wavelet Analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, we find the empirical joint multifractality of MFXWT to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indices, and in pairs of index returns and volatilities we find an intriguing joint multifractal behavior. The tests on surrogate series also reveal that the cross correlation behavior, particularly the cross correlation with zero lag, is the main origin of cross multifractality.
H. Eugene Stanley - One of the best experts on this subject based on the ideXlab platform.
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Multifractal Cross Wavelet Analysis
Fractals, 2017Co-Authors: Zhi-qiang Jiang, Xing-lu Gao, Wei-xing Zhou, H. Eugene StanleyAbstract:Complex systems are composed of mutually interacting components and the output values of these components are usually long-range cross-correlated. We propose a method to characterize the joint multifractal nature of such long-range cross correlations based on Wavelet Analysis, termed multifractal cross Wavelet Analysis (MFXWT). We assess the performance of the MFXWT method by performing extensive numerical experiments on the dual binomial measures with multifractal cross correlations and the bivariate fractional Brownian motions (bFBMs) with monofractal cross correlations. For binomial multifractal measures, the empirical joint multifractality of MFXWT is found to be in approximate agreement with the theoretical formula. For bFBMs, MFXWT may provide spurious multifractality because of the wide spanning range of the multifractal spectrum. We also apply the MFXWT method to stock market indexes and uncover intriguing joint multifractal nature in pairs of index returns and volatilities.
