As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet...As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.展开更多
Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models： Logistic, AR（1） and AR（2）. The time-variable parameters o...Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models： Logistic, AR（1） and AR（2）. The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR（1） model and the AR（2） model of zero-mean series of the weekly dosing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price, Six common statistical methods for error prediction are used to test the predicting results. These methods are： mean error （ME）, mean absolute error （MAE）, root mean squared error （RMSE）, mean absolute percentage error （MAPE）, Akaike＇s information criterion （AIC）, and Bayesian information criterion （BIC）. The investigation shows that AR（1） model exhibits the best predicting result, whereas AR（2） model exhibits predicting results that is intermediate between AR（1） model and the Logistic regression model.展开更多
A one-dimensional fluid/Monte-Carlo（MC）hybrid model is developed to describe capacitively coupled SiH_4/Ar discharge,in which the lower electrode is applied by a RF source and pulse modulated by a square-wave,to inv...A one-dimensional fluid/Monte-Carlo（MC）hybrid model is developed to describe capacitively coupled SiH_4/Ar discharge,in which the lower electrode is applied by a RF source and pulse modulated by a square-wave,to investigate the modulation effects of the pulse duty cycle on the discharge mechanism.An electron Monte Carlo simulation is used to calculate the electron energy distribution as a function of position and time phase.Rate coefficients in chemical reactions can then be obtained and transferred to the fluid model for the calculation of electron temperature and densities of different species,such as electrons,ions,and radicals.The simulation results show that,the electron energy distribution f（ε）is modulated evidently within a pulse cycle,with its tail extending to higher energies during the power-on period,while shrinking back promptly in the afterglow period.Thus,the rate coefficients could be controlled during the discharge,resulting in modulation of the species composition on the substrate compared with continuous excitation.Meanwhile,more negative ions,like Si H_3^-and Si H_2^-,may escape to the electrodes owing to the collapse of ambipolar electric fields,which is beneficial to films deposition.Pulse modulation is thus expected to provide additional methods to customize the plasma densities and components.展开更多
Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alph...Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alpha, and beta power spectrum, these two states can be clearly discriminated. In these two states, frontal areas were activated in delta power, both frontal and occipital areas were activated in theta band, and occipital areas were activated in alpha and beta bands. These four bands had significantly higher power in frontal, parietal, and occipital areas when eyes were close. The results also implied that the optimum order of AR model could be more suitable for estimating EEG power spectrum of different states.展开更多
基金Supported by the National Defence Science and Industry Committee(41314020201)
文摘As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was can:led out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model. The research indicates that it is feasible to use the MRA wavelet network in the short-time prediction of ship motion.
基金The research is supported by the National Natural Science Foundation of China (60574069)the Soft Science Foundation of Guangdong Province (2005B70101044)
文摘Based on the weekly closing price of Shenzhen Integrated Index, this article studies the volatility of Shenzhen Stock Market using three different models： Logistic, AR（1） and AR（2）. The time-variable parameters of Logistic regression model is estimated by using both the index smoothing method and the time-variable parameter estimation method. And both the AR（1） model and the AR（2） model of zero-mean series of the weekly dosing price and its zero-mean series of volatility rate are established based on the analysis results of zero-mean series of the weekly closing price, Six common statistical methods for error prediction are used to test the predicting results. These methods are： mean error （ME）, mean absolute error （MAE）, root mean squared error （RMSE）, mean absolute percentage error （MAPE）, Akaike＇s information criterion （AIC）, and Bayesian information criterion （BIC）. The investigation shows that AR（1） model exhibits the best predicting result, whereas AR（2） model exhibits predicting results that is intermediate between AR（1） model and the Logistic regression model.
基金supported by National Natural Science Foundation of China(No.11275038)
文摘A one-dimensional fluid/Monte-Carlo（MC）hybrid model is developed to describe capacitively coupled SiH_4/Ar discharge,in which the lower electrode is applied by a RF source and pulse modulated by a square-wave,to investigate the modulation effects of the pulse duty cycle on the discharge mechanism.An electron Monte Carlo simulation is used to calculate the electron energy distribution as a function of position and time phase.Rate coefficients in chemical reactions can then be obtained and transferred to the fluid model for the calculation of electron temperature and densities of different species,such as electrons,ions,and radicals.The simulation results show that,the electron energy distribution f（ε）is modulated evidently within a pulse cycle,with its tail extending to higher energies during the power-on period,while shrinking back promptly in the afterglow period.Thus,the rate coefficients could be controlled during the discharge,resulting in modulation of the species composition on the substrate compared with continuous excitation.Meanwhile,more negative ions,like Si H_3^-and Si H_2^-,may escape to the electrodes owing to the collapse of ambipolar electric fields,which is beneficial to films deposition.Pulse modulation is thus expected to provide additional methods to customize the plasma densities and components.
文摘Autoregressive （AR） power spectral density estimate method was used to analyze the electroencephalogram （EEG） signals in eyes-open and eyes-closed states. From the topographical distributions of delta, theta, alpha, and beta power spectrum, these two states can be clearly discriminated. In these two states, frontal areas were activated in delta power, both frontal and occipital areas were activated in theta band, and occipital areas were activated in alpha and beta bands. These four bands had significantly higher power in frontal, parietal, and occipital areas when eyes were close. The results also implied that the optimum order of AR model could be more suitable for estimating EEG power spectrum of different states.
