We propose an information theory based objective function for measuring the statistics independent of source signals. Then, we develop a learlling algorithm for blind separation of nonstationary signals by minimizing ...We propose an information theory based objective function for measuring the statistics independent of source signals. Then, we develop a learlling algorithm for blind separation of nonstationary signals by minimizing the objective function, in which the property of nonstationary and direct architecture neural network is applied. The analysis demonstrates the equiralence of two neural architectures in some special cases. The computer simulation shows the validity of the proposed algorithm. We give the performance surface of the object function at the last of the paper.展开更多
A neural network method for independent source separation (ISS) of multichannel electroencephalogram (EEG) is proposed in this paper.Using the denoising function of wavelet multiscale decomposition,the high-frequency ...A neural network method for independent source separation (ISS) of multichannel electroencephalogram (EEG) is proposed in this paper.Using the denoising function of wavelet multiscale decomposition,the high-frequency noises are removed from the original (raw) EEGs.Then the multichannel EEGs are treated as the weighted mixtures and the expression of weight vector is obtained by seeking the local extrema of the fourth-order cumulants (i.e.kurtosis coefficients) of the mixtures.After these process steps,the weighted mixtures are used as the input of neural network,so the independent source of EEGs can be separated one by one.The experimental results show that our method is effective for ISS of multichannel EEGs.展开更多
A novel separation identification strategy for the neural fuzzy Wiener–Hammerstein system using hybrid signals is developed in this study.The Wiener–Hammerstein system is described by a model consisting of two linea...A novel separation identification strategy for the neural fuzzy Wiener–Hammerstein system using hybrid signals is developed in this study.The Wiener–Hammerstein system is described by a model consisting of two linear dynamic elements with a nonlinear static element in between.The static nonlinear element is modeled by a neural fuzzy network(NFN)and the two linear dynamic elements are modeled by an autoregressive exogenous(ARX)model and an autoregressive(AR)model,separately.When the system input is Gaussian signals,the correlation technique is used to decouple the identification of the two linear dynamic elements from the nonlinear element.First,based on the input and output of Gaussian signals,the correlation analysis technique is used to identify the input linear element and output linear element,which addresses the problem that the intermediate variable information cannot be measured in the identified Wiener–Hammerstein system.Then,a zero-pole match method is adopted to separate the parameters of the two linear elements.Furthermore,the recursive least-squares technique is used to identify the nonlinear element based on the input and output of random signals,which avoids the impact of output noise.The feasibility of the presented identification technique is demonstrated by an illustrative simulation example and a practical nonlinear process.Simulation results show that the proposed strategy can obtain higher identification precision than existing identification algorithms.展开更多
This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet pa...This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.展开更多
文摘We propose an information theory based objective function for measuring the statistics independent of source signals. Then, we develop a learlling algorithm for blind separation of nonstationary signals by minimizing the objective function, in which the property of nonstationary and direct architecture neural network is applied. The analysis demonstrates the equiralence of two neural architectures in some special cases. The computer simulation shows the validity of the proposed algorithm. We give the performance surface of the object function at the last of the paper.
基金Natural Science Foundation of Fujian Province of Chinagrant number:C0710036 and T0750008
文摘A neural network method for independent source separation (ISS) of multichannel electroencephalogram (EEG) is proposed in this paper.Using the denoising function of wavelet multiscale decomposition,the high-frequency noises are removed from the original (raw) EEGs.Then the multichannel EEGs are treated as the weighted mixtures and the expression of weight vector is obtained by seeking the local extrema of the fourth-order cumulants (i.e.kurtosis coefficients) of the mixtures.After these process steps,the weighted mixtures are used as the input of neural network,so the independent source of EEGs can be separated one by one.The experimental results show that our method is effective for ISS of multichannel EEGs.
基金Project supported by the National Natural Science Foundation of China(No.62003151)the Changzhou Science and Technology Bureau(Nos.CJ20220065 and CM20223015)+1 种基金the Qinglan Project of Jiangsu Province,China(No.2022[29])the Zhongwu Youth Innovative Talents Support Program of Jiangsu University of Technology,China(No.202102003)。
文摘A novel separation identification strategy for the neural fuzzy Wiener–Hammerstein system using hybrid signals is developed in this study.The Wiener–Hammerstein system is described by a model consisting of two linear dynamic elements with a nonlinear static element in between.The static nonlinear element is modeled by a neural fuzzy network(NFN)and the two linear dynamic elements are modeled by an autoregressive exogenous(ARX)model and an autoregressive(AR)model,separately.When the system input is Gaussian signals,the correlation technique is used to decouple the identification of the two linear dynamic elements from the nonlinear element.First,based on the input and output of Gaussian signals,the correlation analysis technique is used to identify the input linear element and output linear element,which addresses the problem that the intermediate variable information cannot be measured in the identified Wiener–Hammerstein system.Then,a zero-pole match method is adopted to separate the parameters of the two linear elements.Furthermore,the recursive least-squares technique is used to identify the nonlinear element based on the input and output of random signals,which avoids the impact of output noise.The feasibility of the presented identification technique is demonstrated by an illustrative simulation example and a practical nonlinear process.Simulation results show that the proposed strategy can obtain higher identification precision than existing identification algorithms.
基金Supported by the National Basic Research Program("973"Program, No2005CB724303 )
文摘This paper presents an effective method for motion classification using the surface electromyographic (sEMG) signal collected from the forearm. Given the nonlinear and time-varying nature of EMG signal, the wavelet packet transform (WPT) is introduced to extract time-frequency joint information. Then the multi-class classifier based on the least squares support vector machine (LS-SVM) is constructed and verified in the various motion classification tasks. The results of contrastive experiments show that different motions can be identified with high accuracy by the presented method. Furthermore, compared with other classifiers with different features, the performance indicates the potential of the SVM techniques combined with WPT in motion classification.
