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An Improved Noise Elimination Model of EEG Based on Second Order Volterra Filter 2019

An Improved Noise Elimination Model of EEG Based on Second O...

3rd International Conference on Digital Signal Processing (ICDSP) / 2nd International Conference on Computer Graphics and Virtuality (ICCGV)

作者： Wu, Xia Zhang, Yumei Wu, Xiaojun Shaanxi Normal Univ Minist Educ Key Lab Modern Teaching Technol Xian Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China

ISBN: (纸本)9781450362047

Recently, electroencephalogram (EEG) is widely applied for physiological research and clinical diagnosis of brain diseases. Therefore, how to eliminate noise to gain a pure EEG signal becomes a common difficulty in this field. As a typical method for chaotic time series, Volterra is widely used to study EEG signal. However, the calculation of Volterra coefficients is likely to cause dimensionality disaster. In addition, EEG signals collected in real environment are not easy to extract the prior information, which is related to the quality of the reconstructed phase space. In order to overcome these two problems, we introduce a uniform searching particle swarm optimization (upso) algorithm to optimize the coefficients of Volterra then a noise elimination method based on upso second order Volterra filter (upso-SOVF) can be constructed. The proposed model can improve the quality of phase-space reconstruction by implicating the phase space reconstruction process in the model solving process and then get the embedding dimension and delay time dynamically. In this paper, some experiments are made on different EEG signals and compared with the particle swarm optimization second order Volterra filter (PSO-SOVF). The result shows that the proposed model has a better performance in avoiding the dimensional disaster and can better reflect regularities of the EEG signal series than PSO-SOVF. It can fully meet the requirements for noise elimination of EEG signal.

关键词： Nonlinear model EEG signal Volterra filter upso algorithm

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RPSOVF Prediction Model for Speech Signal Series Based on upso

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INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS 2019年第6期29卷

作者： Zhang, Yumei Guo, Xiangying Wu, Xia Shi, Suzhen Wu, Xiaojun Shaanxi Normal Univ Minist Educ Key Lab Modern Teaching Technol Xian 710062 Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710062 Shaanxi Peoples R China Beijing Univ Technol Dept Mech Engn Beijing 100124 Peoples R China

In this paper, we propose a nonlinear prediction model of speech signal series with an explicit structure. In order to overcome some intrinsic shortcomings, such as traps at the local minimum, improper selection of parameters, and slow convergence rate, which are always caused by improper parameters generated by, typically, the low performance of least mean square (LMS) in updating kernel coefficients of the Volterra model, a uniform searching particle swarm optimization (upso) algorithm to optimize the kernel coefficients of the Volterra model is proposed. The second-order Volterra filter (SOVF) speech prediction model based on upso is established by using English phonemes, words, and phrases. In order to reduce the complexity of the model, given a user-designed tolerance of errors, we extract the reduced parameter of SOVF (RPSOVF) for acceleration. The experimental results show that in the tasks of single-frame and multiframe speech signals, both upso-SOVF and upso-RPSOVF are better than LMS-SOVF and PSO-SOVF in terms of root mean square error (RMSE) and mean absolute deviation (MAD). upso-SOVF and upso-RPSOVF can better reflect trends and regularity of speech signals, which can fully meet the requirements of speech signal prediction. The proposed model presents a nonlinear analysis and valuable model structure for speech signal series, and can be further employed in speech signal reconstruction or compression coding.

关键词： Speech signal chaos Volterra prediction model upso algorithm

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An Improved Noise Elimination Model of EEG Based on Second Order Volterra Filter

An Improved Noise Elimination Model of EEG Based on Second O...

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作者： Xia Wu Yumei Zhang Xiaojun Wu Key Laboratory of Modern Teaching technology Ministry of Education (Shaanxi Normal University) School of Computer Science Shaanxi Normal University

Recently, electroencephalogram(EEG) is widely applied for physiological research and clinical diagnosis of brain diseases. Therefore, how to eliminate noise to gain a pure EEG signal becomes a common difficulty in this field. As a typical method for chaotic time series, Volterra is widely used to study EEG signal. However, the calculation of Volterra coefficients is likely to cause dimensionality disaster. In addition, EEG signals collected in real environment are not easy to extract the prior information, which is related to the quality of the reconstructed phase space. In order to overcome these two problems, we introduce a uniform searching particle swarm optimization(upso) algorithm to optimize the coefficients of Volterra then a noise elimination method based on upso second order Volterra filter(upso-SOVF) can be constructed. The proposed model can improve the quality of phase-space reconstruction by implicating the phase space reconstruction process in the model solving process and then get the embedding dimension and delay time dynamically. In this paper, some experiments are made on different EEG signals and compared with the particle swarm optimization second order Volterra filter(PSO-SOVF). The result shows that the proposed model has a better performance in avoiding the dimensional disaster and can better reflect regularities of the EEG signal series than PSO-SOVF. It can fully meet the requirements for noise elimination of EEG signal.

关键词： Nonlinear model EEG signal Volterra filter upso algorithm

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Suppression of Chaotic Behavior in Duffing-holmes System using Backstepping Controller Optimized by Unified Particle Swarm Optimization algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2013年第11期26卷 1299-1306页

作者： Gholipour, R. Khosravi, A. Mojallali, H. Babol Univ Technol Fac Elect & Comp Engn Babol Sar Iran Univ Guilan Elect Engn Dept Fac Engn Rasht Iran

The nonlinear behavior analysis and chaos control for Duffing-Holmes chaotic system is discussed in this paper. In order to suppress the irregular chaotic motion, an optimal backstepping controller is designed. The backstepping method consists of parameters with positive values. The improper selection of the parameters leads to inappropriate responses or even may lead to instability of the system. In this paper, the Unified particle swarm optimization (upso) algorithm is utilized to determine the convenient and optimal values of the parameters. The minimized objective function via upso algorithm is a weighted sum of the Integral of Time multiplied Absolute Error (ITAE) and squared control signal. Fast control of chaos in a very short time and having more limited control signal for this purpose, are the great advantages of the proposed controller. Numerical simulations show the high performance of this method for chaos elimination in Duffing-Holmes system.

关键词： Duffing-holmes System Control of Chaos Backstepping Controller upso algorithm

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