Quantized minimum error entropy criterion(QMEE)QuantizationAdaptive filtering algorithms have been widely used in many areas, among which the minimum error entropy (MEE) algorithm is a superior choice, due to its excellent performance in the non-Gaussian noise situations. However, the computational ...
Comparing with traditional learning criteria, such as mean square error, the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing and machine learning. The argument of the logarithm in Renyi's entropy estimator, called information potential (IP), is a ...
Quantized generalized minimum error entropy for kernel recursive least squares adaptive filtering The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic le... J He,G Wang,K Zhang,... - 《Arxiv》 被...
Following the framework of the proposed structure, the Laguerre filtered-s maximum correntropy criterion (MCC) (LFsMCC) and Laguerre filtered-s minimum error entropy criterion (LFsMEE) algorithm are proposed. Further, the online vector quantization (VQ) method is adopted. The VQ was proposed for ...
minimum error entropyconvergenceThe broad learning system(BLS) based on the minimum mean square error(MMSE) criterion can achieve outstanding performance without spending too much time in various machine learning tasks.However, when data are polluted by non-Gaussian noise, the stability of BLS may ...
The recently proposed quantized minimum error entropy (QMEE) criterion is applied to structure a new cost function instead of the L₂-norm in the conventional CSP. Quantization is utilized to reduce the computational complexity. The new objective function is optimized by a gradient-based iterative...
The Pauta criterion, a method for identifying outliers in sample data, utilizes the mean and standard deviation as thresholds to detect significant errors. If the error exceeds this threshold, it is deemed a significant error rather than a random error. These outliers should be removed as they ...
To address the issues in conventional inversion control, a first-order low-pass filter and an adaptive parameter minimum learning law are introduced in the control system design process. This method has the following features: (1) it solves the problem of repeated differentiation of the virtual ...