Generalized Correntropy Criterion Research Articles

Extraction of High Time-Frequency Energy Concentration of Sensor’s EMI Signals Based on Generalized Correntropy Criterion and VMD

Extraction of High Time-Frequency Energy Concentration of Sensor’s EMI Signals Based on Generalized Correntropy Criterion and VMD

Generalized Correntropy Criterion-Based Performance Assessment for Non-Gaussian Stochastic Systems.

Control loop performance assessment (CPA) is essential in the operation of industrial systems. In this paper, the shortcomings of existing performance assessment methods and indicators are summarized firstly, and a novel evaluation method based on generalized correntropy criterion (GCC) is proposed to evaluate the performance of non-Gaussian stochastic systems. This criterion could characterize the statistical properties of non-Gaussian random variables more fully, so it can be directly used as the assessment index. When the expected output of the given system is unknown, generalized correntropy is used to describe the similarity of two random variables in the joint space neighborhood controlled and take it as the criterion function of the identification algorithms. To estimate the performance benchmark more quickly and accurately, a hybrid-EDA (H-EDA) combined with the idea of “wading across the stream algorithm” is proposed to obtain the system parameters and disturbance noise PDF. Through the simulation of a single loop feedback control system under different noise disturbances, the effectiveness of the improved algorithm and new indexes are verified.

Sparsity Constrained Recursive Generalized Maximum Correntropy Criterion With Variable Center Algorithm

This brief developed a novel sparsity constrained recursive adaptive filtering algorithm via the generalized correntropy criterion with variable center (GMCC-VC) for sparse system parameters estimation. In order to improve the performance of the adaptive filtering algorithm with GMCC under the non-Gaussian and non-zero mean noise environments, the GMCC is extended by adding a center which can be located at any position in the generalized Gaussian kernel function. Specifically, we proposed a novel adaptive online update method to optimize the kernel method and center. In addition, we mainly derived a recursive GMCC-VC algorithm based on the GMCC with the sparse constraint to exploit sparsity. Numerical simulation results are given to show that the proposed algorithm is efficient for sparse system parameter estimation problems in non-Gaussian and non-mean noise environments.

Robust Tensor Decomposition for Image Representation Based on Generalized Correntropy.

Traditional tensor decomposition methods, e.g., two dimensional principal component analysis and two dimensional singular value decomposition, that minimize mean square errors, are sensitive to outliers. To overcome this problem, in this paper we propose a new robust tensor decomposition method using generalized correntropy criterion (Corr-Tensor). A Lagrange multiplier method is used to effectively optimize the generalized correntropy objective function in an iterative manner. The Corr-Tensor can effectively improve the robustness of tensor decomposition with the existence of outliers without introducing any extra computational cost. Experimental results demonstrated that the proposed method significantly reduces the reconstruction error on face reconstruction and improves the accuracies on handwritten digit recognition and facial image clustering.

Robust Maximum Mixture Correntropy Criterion-Based Semi-Supervised ELM With Variable Center

The recent correntropy criterion based semi-supervised random neural network extreme learning machine (RC-SSELM) achieved outstanding performance in dealing with datasets with large outliers and non-Gaussian noises. To further improve the effectiveness and flexibility of the algorithm in combating large and complex outliers, we explore a more effective semi-supervised ELM data learning algorithm with the robust maximum mixture correntropy criterion (MMCC) based optimization scheme in this brief. Meanwhile, the generalized correntropy criterion kernel function with the variable kernel center is applied to MMCC, and the resultant novel semi-supervised learning algorithm is abbreviated as MC-SSELM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vc</sup> . The fixed-point iteration learning algorithm is adopted for the output weight optimization of MC-SSELM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vc</sup> . Experiment conducted on many benchmark datasets are given to show the effectiveness of MC-SSELM <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">vc</sup> and comparisons to several state-of-the-art semi-supervised learning algorithms are provided for the superiority demonstration.

A novel space target-tracking method based on generalized Gaussian distribution for on-orbit maintenance robot in Tiangong-2 space laboratory

Accurate target tracking based on visual images is the key for intelligent robots to assist or replace astronauts to work in space station. However, the special space environment such as non-uniform illumination and high-energy particle radiation is a huge challenge, which may lead to complex noise coupling in vision image. This paper proposes a novel method for accurate target tracking, the essence of which is the Retinex image enhancement algorithm in CIELAB color space (LAB-GRetinex) and the generalized maximum correntropy Kalman filter (GMCKF) which are all based on generalized Gaussian distribution. The LAB-GRetinex algorithm chooses the CIELAB color space, which is closer to the human vision, as the processing color space, and the generalized Gaussian distribution can estimate the light image accurately, so the influence of non-uniform illumination can be reduced effectively. Meanwhile, the GMCKF algorithm adopts the generalized correntropy criterion based on the generalized Gaussian distribution to replace the minimum mean square error (MMSE) criterion to realize the optimal filtering effect under the complex non-Gaussian noise, which can improve the target tracking accuracy. Sufficient ground simulation experiments and application experiments in the Tiangong-2 space laboratory verify the effectiveness of the proposed algorithm which can track the target accurately in the special space environment and provide the precise pose information for on-orbit robot maintenance verification. This research lays a technical foundation for the application of intelligent robot in the construction and operation on space station in the future.

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods.

Generalized Correntropy Filter-Based Fault Diagnosis and Tolerant Control for Non-Gaussian Stochastic Systems Subject to Sensor Faults

In this paper, a generalized correntropy filter-based fault diagnosis (FD) and fault tolerant control (FTC) strategy is proposed for stochastic systems with heavy-tailed distributed noises. In order to deal with the non-Gaussian noises involved in the stochastic systems, a generalized correntropy criterion (GCC) is reviewed first. Then, based on this criterion, an output feedback controller is designed for the healthy stochastic system to make the system output track the set-point as closely as possible. Considering the sensor fault, a GCC filter-based FD method is established, where residuals are used to detect and isolate the sensor fault. Moreover, when the sensor fault is diagnosed, normal signals are used to reconstruct the system so that the system can maintain run normally. Finally, the wind energy conversion system with three types of sensor faults is used to verify the feasibility and efficiency of the proposed FD and FTC method.