The Experts below are selected from a list of 29790 Experts worldwide ranked by ideXlab platform

Mengshan Li - One of the best experts on this subject based on the ideXlab platform.

  • solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self adaptive particle swarm optimization and k harmonic means
    RSC Advances, 2015
    Co-Authors: Mengshan Li, Xingyuan Huang, Yan Wu, Lijiao Wang
    Abstract:

    A novel model combined with Chaos Theory, self-adaptive particle swarm optimization (PSO) algorithm, K-harmonic means (KHM) clustering and radial basis function artificial neural network (RBF ANN) is proposed, hereafter called CSPSO-KHM RBF ANN. Traditional PSO algorithm is modified by Chaos Theory and self-adaptive inertia weight factor in order to reduce premature convergence problem. The modified PSO algorithm is employed to trim the RBF ANN connection weights and biases, whereas KHM is used to tune the hidden centers and spreads. The CSPSO-KHM RBF ANN model was employed to investigate the solubility of supercritical carbon dioxide in 10 polymers. Compared with other methods, such as RBF ANN, adaptive neuro-fuzzy inference system and PSO ANN, the proposed model displays optimal prediction performance. Results discover that the CSPSO-KHM RBF ANN model is an effective method for solubility prediction with high accuracy, and is a practicable method for chemical process analyzing and designing.

  • prediction of gas solubility in polymers by back propagation artificial neural network based on self adaptive particle swarm optimization algorithm and Chaos Theory
    Fluid Phase Equilibria, 2013
    Co-Authors: Aihua Xiong, Mengshan Li, Xingyuan Huang, Yan Wu, Tianwen Dong
    Abstract:

    Abstract A novel prediction method based on Chaos Theory, self-adaptive particle swarm optimization (PSO) algorithm, and back propagation artificial neural network (BP ANN) is proposed to predict gas solubility in polymers, hereafter called CSPSO BP ANN. The premature convergence problem of CSPSO BP ANN is overcome by modifying the conventional PSO algorithm using Chaos Theory and self-adaptive inertia weight factor. Modified PSO algorithm is used to optimize the BP ANN connection weights. Then, the proposed CSPSO BP ANN (two input nodes consisting of temperature and pressure; one output node consisting of gas solubility in polymers) is used to investigate solubility of CO2 in polystyrene, N2 in polystyrene, and CO2 in polypropylene, respectively. Results indicate that CSPSO BP ANN is an effective prediction method for gas solubility in polymers. Moreover, compared with conventional BP ANN and PSO ANN, CSPSO BP ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1275, 0.9963, and 0.0116, respectively. Statistical data demonstrate that CSPSO BP ANN has excellent prediction capability and high accuracy, and the correlation between predicted and experimental data is good.

Heikki V Huikuri - One of the best experts on this subject based on the ideXlab platform.

  • cardiac interbeat interval dynamics from childhood to senescence comparison of conventional and new measures based on fractals and Chaos Theory
    Circulation, 1999
    Co-Authors: Sirkku M Pikkujamsa, Ary L Goldberger, Timo H Makikallio, Leif Sourander, Ismo Raiha, Pauli Puukka, Jarmo Skytta, Chungkang Peng, Heikki V Huikuri
    Abstract:

    Background—New methods of R-R interval variability based on fractal scaling and nonlinear dynamics (“Chaos Theory”) may give new insights into heart rate dynamics. The aims of this study were to (1...

  • cardiac interbeat interval dynamics from childhood to senescence comparison of conventional and new measures based on fractals and Chaos Theory
    Circulation, 1999
    Co-Authors: Sirkku M Pikkujamsa, Ary L Goldberger, Timo H Makikallio, Leif Sourander, Ismo Raiha, Pauli Puukka, Jarmo Skytta, Chungkang Peng, Heikki V Huikuri
    Abstract:

    BACKGROUND: New methods of R-R interval variability based on fractal scaling and nonlinear dynamics ("Chaos Theory") may give new insights into heart rate dynamics. The aims of this study were to (1) systematically characterize and quantify the effects of aging from early childhood to advanced age on 24-hour heart rate dynamics in healthy subjects; (2) compare age-related changes in conventional time- and frequency-domain measures with changes in newly derived measures based on fractal scaling and complexity (Chaos) Theory; and (3) further test the hypothesis that there is loss of complexity and altered fractal scaling of heart rate dynamics with advanced age. METHODS AND RESULTS: The relationship between age and cardiac interbeat (R-R) interval dynamics from childhood to senescence was studied in 114 healthy subjects (age range, 1 to 82 years) by measurement of the slope, beta, of the power-law regression line (log power-log frequency) of R-R interval variability (10(-4) to 10(-2) Hz), approximate entropy (ApEn), short-term (alpha(1)) and intermediate-term (alpha(2)) fractal scaling exponents obtained by detrended fluctuation analysis, and traditional time- and frequency-domain measures from 24-hour ECG recordings. Compared with young adults ( 60 years, n=29). CONCLUSIONS: Cardiac interbeat interval dynamics change markedly from childhood to old age in healthy subjects. Children show complexity and fractal correlation properties of R-R interval time series comparable to those of young adults, despite lower overall heart rate variability. Healthy aging is associated with R-R interval dynamics showing higher regularity and altered fractal scaling consistent with a loss of complex variability.

Sirkku M Pikkujamsa - One of the best experts on this subject based on the ideXlab platform.

  • cardiac interbeat interval dynamics from childhood to senescence comparison of conventional and new measures based on fractals and Chaos Theory
    Circulation, 1999
    Co-Authors: Sirkku M Pikkujamsa, Ary L Goldberger, Timo H Makikallio, Leif Sourander, Ismo Raiha, Pauli Puukka, Jarmo Skytta, Chungkang Peng, Heikki V Huikuri
    Abstract:

    Background—New methods of R-R interval variability based on fractal scaling and nonlinear dynamics (“Chaos Theory”) may give new insights into heart rate dynamics. The aims of this study were to (1...

  • cardiac interbeat interval dynamics from childhood to senescence comparison of conventional and new measures based on fractals and Chaos Theory
    Circulation, 1999
    Co-Authors: Sirkku M Pikkujamsa, Ary L Goldberger, Timo H Makikallio, Leif Sourander, Ismo Raiha, Pauli Puukka, Jarmo Skytta, Chungkang Peng, Heikki V Huikuri
    Abstract:

    BACKGROUND: New methods of R-R interval variability based on fractal scaling and nonlinear dynamics ("Chaos Theory") may give new insights into heart rate dynamics. The aims of this study were to (1) systematically characterize and quantify the effects of aging from early childhood to advanced age on 24-hour heart rate dynamics in healthy subjects; (2) compare age-related changes in conventional time- and frequency-domain measures with changes in newly derived measures based on fractal scaling and complexity (Chaos) Theory; and (3) further test the hypothesis that there is loss of complexity and altered fractal scaling of heart rate dynamics with advanced age. METHODS AND RESULTS: The relationship between age and cardiac interbeat (R-R) interval dynamics from childhood to senescence was studied in 114 healthy subjects (age range, 1 to 82 years) by measurement of the slope, beta, of the power-law regression line (log power-log frequency) of R-R interval variability (10(-4) to 10(-2) Hz), approximate entropy (ApEn), short-term (alpha(1)) and intermediate-term (alpha(2)) fractal scaling exponents obtained by detrended fluctuation analysis, and traditional time- and frequency-domain measures from 24-hour ECG recordings. Compared with young adults ( 60 years, n=29). CONCLUSIONS: Cardiac interbeat interval dynamics change markedly from childhood to old age in healthy subjects. Children show complexity and fractal correlation properties of R-R interval time series comparable to those of young adults, despite lower overall heart rate variability. Healthy aging is associated with R-R interval dynamics showing higher regularity and altered fractal scaling consistent with a loss of complex variability.

Xingyuan Huang - One of the best experts on this subject based on the ideXlab platform.

  • solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self adaptive particle swarm optimization and k harmonic means
    RSC Advances, 2015
    Co-Authors: Mengshan Li, Xingyuan Huang, Yan Wu, Lijiao Wang
    Abstract:

    A novel model combined with Chaos Theory, self-adaptive particle swarm optimization (PSO) algorithm, K-harmonic means (KHM) clustering and radial basis function artificial neural network (RBF ANN) is proposed, hereafter called CSPSO-KHM RBF ANN. Traditional PSO algorithm is modified by Chaos Theory and self-adaptive inertia weight factor in order to reduce premature convergence problem. The modified PSO algorithm is employed to trim the RBF ANN connection weights and biases, whereas KHM is used to tune the hidden centers and spreads. The CSPSO-KHM RBF ANN model was employed to investigate the solubility of supercritical carbon dioxide in 10 polymers. Compared with other methods, such as RBF ANN, adaptive neuro-fuzzy inference system and PSO ANN, the proposed model displays optimal prediction performance. Results discover that the CSPSO-KHM RBF ANN model is an effective method for solubility prediction with high accuracy, and is a practicable method for chemical process analyzing and designing.

  • prediction of gas solubility in polymers by back propagation artificial neural network based on self adaptive particle swarm optimization algorithm and Chaos Theory
    Fluid Phase Equilibria, 2013
    Co-Authors: Aihua Xiong, Mengshan Li, Xingyuan Huang, Yan Wu, Tianwen Dong
    Abstract:

    Abstract A novel prediction method based on Chaos Theory, self-adaptive particle swarm optimization (PSO) algorithm, and back propagation artificial neural network (BP ANN) is proposed to predict gas solubility in polymers, hereafter called CSPSO BP ANN. The premature convergence problem of CSPSO BP ANN is overcome by modifying the conventional PSO algorithm using Chaos Theory and self-adaptive inertia weight factor. Modified PSO algorithm is used to optimize the BP ANN connection weights. Then, the proposed CSPSO BP ANN (two input nodes consisting of temperature and pressure; one output node consisting of gas solubility in polymers) is used to investigate solubility of CO2 in polystyrene, N2 in polystyrene, and CO2 in polypropylene, respectively. Results indicate that CSPSO BP ANN is an effective prediction method for gas solubility in polymers. Moreover, compared with conventional BP ANN and PSO ANN, CSPSO BP ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1275, 0.9963, and 0.0116, respectively. Statistical data demonstrate that CSPSO BP ANN has excellent prediction capability and high accuracy, and the correlation between predicted and experimental data is good.

Yan Wu - One of the best experts on this subject based on the ideXlab platform.

  • solubility prediction of supercritical carbon dioxide in 10 polymers using radial basis function artificial neural network based on chaotic self adaptive particle swarm optimization and k harmonic means
    RSC Advances, 2015
    Co-Authors: Mengshan Li, Xingyuan Huang, Yan Wu, Lijiao Wang
    Abstract:

    A novel model combined with Chaos Theory, self-adaptive particle swarm optimization (PSO) algorithm, K-harmonic means (KHM) clustering and radial basis function artificial neural network (RBF ANN) is proposed, hereafter called CSPSO-KHM RBF ANN. Traditional PSO algorithm is modified by Chaos Theory and self-adaptive inertia weight factor in order to reduce premature convergence problem. The modified PSO algorithm is employed to trim the RBF ANN connection weights and biases, whereas KHM is used to tune the hidden centers and spreads. The CSPSO-KHM RBF ANN model was employed to investigate the solubility of supercritical carbon dioxide in 10 polymers. Compared with other methods, such as RBF ANN, adaptive neuro-fuzzy inference system and PSO ANN, the proposed model displays optimal prediction performance. Results discover that the CSPSO-KHM RBF ANN model is an effective method for solubility prediction with high accuracy, and is a practicable method for chemical process analyzing and designing.

  • prediction of gas solubility in polymers by back propagation artificial neural network based on self adaptive particle swarm optimization algorithm and Chaos Theory
    Fluid Phase Equilibria, 2013
    Co-Authors: Aihua Xiong, Mengshan Li, Xingyuan Huang, Yan Wu, Tianwen Dong
    Abstract:

    Abstract A novel prediction method based on Chaos Theory, self-adaptive particle swarm optimization (PSO) algorithm, and back propagation artificial neural network (BP ANN) is proposed to predict gas solubility in polymers, hereafter called CSPSO BP ANN. The premature convergence problem of CSPSO BP ANN is overcome by modifying the conventional PSO algorithm using Chaos Theory and self-adaptive inertia weight factor. Modified PSO algorithm is used to optimize the BP ANN connection weights. Then, the proposed CSPSO BP ANN (two input nodes consisting of temperature and pressure; one output node consisting of gas solubility in polymers) is used to investigate solubility of CO2 in polystyrene, N2 in polystyrene, and CO2 in polypropylene, respectively. Results indicate that CSPSO BP ANN is an effective prediction method for gas solubility in polymers. Moreover, compared with conventional BP ANN and PSO ANN, CSPSO BP ANN shows better performance. The values of average relative deviation (ARD), squared correlation coefficient (R2) and standard deviation (SD) are 0.1275, 0.9963, and 0.0116, respectively. Statistical data demonstrate that CSPSO BP ANN has excellent prediction capability and high accuracy, and the correlation between predicted and experimental data is good.