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A multiple-kernel LSSVR method for separable nonlinear system identifcation 被引量:5
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作者 Yanning CAI Hongqiao WANG +1 位作者 Xuemei YE Qinggang FAN 《控制理论与应用(英文版)》 EI CSCD 2013年第4期651-655,共5页
In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem... In some nonlinear dynamic systems, the state variables function usually can be separated from the control variables function, which brings much trouble to the identification of such systems. To well solve this problem, an improved least squares support vector regression (LSSVR) model with multiple-kernel is proposed and the model is applied to the nonlinear separable system identification. This method utilizes the excellent nonlinear mapping ability of Morlet wavelet kernel function and combines the state and control variables information into a kernel matrix. Using the composite wavelet kernel, the LSSVR includes two nonlinear functions, whose variables are the state variables and the control ones respectively, in this way, the regression function can gain better nonlinear mapping ability, and it can simulate almost any curve in quadratic continuous integral space. Then, they are used to identify the two functions in the separable nonlinear dynamic system. Simulation results show that the multiple-kernel LSSVR method can greatly improve the identification accuracy than the single kernel method, and the Morlet wavelet kernel is more efficient than the other kernels. 展开更多
关键词 Least squares support vector regression multiple-kernel learning Composite kernel Wavelet kernel System identification
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Noisy speech emotion recognition using sample reconstruction and multiple-kernel learning 被引量:1
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作者 Jiang Xiaoqing Xia Kewen +1 位作者 Lin Yongliang Bai Jianchuan 《The Journal of China Universities of Posts and Telecommunications》 EI CSCD 2017年第2期1-9,17,共10页
Speech emotion recognition (SER) in noisy environment is a vital issue in artificial intelligence (AI). In this paper, the reconstruction of speech samples removes the added noise. Acoustic features extracted from... Speech emotion recognition (SER) in noisy environment is a vital issue in artificial intelligence (AI). In this paper, the reconstruction of speech samples removes the added noise. Acoustic features extracted from the reconstructed samples are selected to build an optimal feature subset with better emotional recognizability. A multiple-kernel (MK) support vector machine (SVM) classifier solved by semi-definite programming (SDP) is adopted in SER procedure. The proposed method in this paper is demonstrated on Berlin Database of Emotional Speech. Recognition accuracies of the original, noisy, and reconstructed samples classified by both single-kernel (SK) and MK classifiers are compared and analyzed. The experimental results show that the proposed method is effective and robust when noise exists. 展开更多
关键词 speech emotion recognition compressed sensing multiple-kernel learning feature selection
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