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

Abstract

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.

Investigation of the performance of multi-input multi-output detectors based on deep learning in non-Gaussian environments

“…The square root of the differences between the corresponding elements of the two vectors is the Euclidean distance, or L2 distance. Very helpful: becoming infected [39]- [47]. The best model is found using MCC criteria by maximizing the correlation between the model's output and the variable target:…”