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Nonlinear characteristics and functional analysis of masseter electromyography
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作者 胡昕 吴箫博 +3 位作者 邹波 傅予力 喻意 卢广文 《Journal of Central South University》 SCIE EI CAS 2011年第3期834-839,共6页
The C-C method was adopted to analyze the nonlinear characteristics of masseter electromyography (EMG) signals and the chaotic degree by the largest Lyapunov exponent (LLE) of different genders and sides. First, t... The C-C method was adopted to analyze the nonlinear characteristics of masseter electromyography (EMG) signals and the chaotic degree by the largest Lyapunov exponent (LLE) of different genders and sides. First, the embedding dimension and the delay time were obtained through this method, then the phase space was reconstructed to resume the chaotie attractor and determine the LLE. The result shows that the trajectory of attractor is denser than Chen's attractor, and the LLE is positive, which means that not only the signal has the character of chaos, but also the chaotic degree of masseter EMG is relatively high. According to the value of the LLE, the chaotic degree of men's masseter EMG is higher than that of women's; when the dentition is normal, the chaotic degree of two sides is almost the same. Then, a conclusion can be deduced that if the LLE of both sides are in great difference, the unilateral mastication is likely to exist, which means that the nonlinear characteristics of masseter EMG can be applied to predict the unilateral mastication. 展开更多
关键词 nonlinear characteristic masseter electromyograph C-C Method phase space reconstruction ATTRACTOR Lyapunovexponent
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Nonlinear chaotic characteristic in leaching process and prediction of leaching cycle period
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作者 刘超 吴爱祥 +1 位作者 尹升华 陈勋 《Journal of Central South University》 SCIE EI CAS CSCD 2016年第11期2935-2940,共6页
A laboratory leaching experiment with samples of different grades was carried out, and an analytical method of concentration of leaching solution was put forward. For each sample, respectively, by applying phase space... A laboratory leaching experiment with samples of different grades was carried out, and an analytical method of concentration of leaching solution was put forward. For each sample, respectively, by applying phase space reconstruction for time series of monitoring data, the saturated embedding dimension and the correlation dimension were obtained, and the evolution laws between neighboring points in the reconstructed phase space were revealed. With BP neural network, a prediction model of concentration of leaching solution was set up and the maximum error of which was less than 2%. The results show that there exist chaotic characteristics in leaching system, and samples of different grades have different nonlinear dynamic features; the higher the grade of sample, the smaller the correlation dimension; furthermore, the maximum Lyapunov index, energy dissipation and chaotic extent of the leaching system increase with grade of the sample; by phase space reconstruction, the subtle change features of concentration of leaching solution can be magnified and the inherent laws can be fully demonstrated. According to the laws, a prediction model of leaching cycle period has been established to provide a theoretical foundation for solution mining. 展开更多
关键词 leaching system phase space reconstruction chaotic characteristic leaching cycle period neural network prediction
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KLT-based local linear prediction of chaotic time series
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作者 Meng Qingfang Peng Yuhua Chen Yuehui 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第4期694-699,共6页
In the reconstructed phase space, based on the Karhunen-Loeve transformation (KLT), the new local linear prediction method is proposed to predict chaotic time series. & noise-free chaotic time series and a noise ad... In the reconstructed phase space, based on the Karhunen-Loeve transformation (KLT), the new local linear prediction method is proposed to predict chaotic time series. & noise-free chaotic time series and a noise added chaotic time series are analyzed. The simulation results show that the KLT-based local linear prediction method can effectively make one-step and multi-step prediction for chaotic time series, and the one-step and multi-step prediction accuracies of the KLT-based local linear prediction method are superior to that of the traditional local linear prediction. 展开更多
关键词 Karhunen-Loeve transformation local linear prediction phase space reconstruction chaotic time series.
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