The kernel learning algorithm has been widely used to solve the generalization problem existing in reinforcement learning. However, when the state-action space of the sample is large. the computation and storage burde...
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ISBN:
(纸本)9789881563972
The kernel learning algorithm has been widely used to solve the generalization problem existing in reinforcement learning. However, when the state-action space of the sample is large. the computation and storage burden in the kernel learning algorithm will increase. In such case, there will be long running time and the real-time performance of the control system cannot be guaranteed. Therefore, in order to enhance the computational speed, this paper proposes a parallel architecture based iterative segmentation optimal cyclic block kernel learning algorithm. The experimental results show that the proposed method can significantly improve the computational efficiency of the kernel learning algorithm, which has important engineering practice significance.
The kernel learning algorithm has been widely used to solve the generalization problem existing in reinforcement learning. However, when the state-action space of the sample is large, the computation and storage burde...
详细信息
The kernel learning algorithm has been widely used to solve the generalization problem existing in reinforcement learning. However, when the state-action space of the sample is large, the computation and storage burden in the kernel learning algorithm will increase. In such case, there will be long running time and the real-time performance of the control system cannot be guaranteed. Therefore, in order to enhance the computational speed, this paper proposes a parallel architecture based iterative segmentation optimal cyclic block kernel learning algorithm. The experimental results show that the proposed method can significantly improve the computational efficiency of the kernel learning algorithm, which has important engineering practice significance.
The continuous stirred tank reactor(CSTR)is one of the typical chemical *** at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is *** QKLMS algorithm is based on a simple onli...
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The continuous stirred tank reactor(CSTR)is one of the typical chemical *** at its strong nonlinear characteristics,a quantized kernel least mean square(QKLMS)algorithm is *** QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification,which can compress the input or feature space and suppress the growth of the radial basis function(RBF)structure in the kernellearning *** verify the effectiveness of the algorithm,it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product *** additiion,the proposed algorithm is further compared with least squares support vector machine(LS-SVM),echo state network(ESN),extreme learning machine with kernels(KELM),*** experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.
A new method by combining wavelet packet transform with kernel learning algorithms is proposed to estimate the mental fatigue state in this paper. The first step of this method is to investigate the impact of long ter...
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A new method by combining wavelet packet transform with kernel learning algorithms is proposed to estimate the mental fatigue state in this paper. The first step of this method is to investigate the impact of long term mental arithmetic task on psychology and physiology of subjects by subjective self-reporting measures, action performance test, power spectral indices of HRV and wavelet packet parameters of EEG. The second step is to calculate the wavelet packet features of all EEG data segments, including relative wavelet packet energy parameters in four frequency bands, wavelet packet entropy and three ratio indices. Finally, kernel principal component analysis (KPCA) and support vector machine (SVM) are jointly applied to differentiate two mental fatigue states. The investigation suggests that the joint KPCA-SVM method is able to effectively reduce the dimensionality of the feature vectors, speed up the convergence in the training of SVM and achieve higher classification accuracy (88%) of the mental fatigue state. Hence KPCA-SVM could be a promising model for the estimation of mental fatigue.
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