Performance evaluation of the maximum complex correntropy criterion with adaptive kernel width update

Abstract

The complex correntropy is a recently defined similarity measure that extends the advantages of conventional correntropy to complex-valued data. As in the real-valued case, the maximum complex correntropy criterion (MCCC) employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the method. However, determining the optimal value for such parameter is not always a trivial task. Within this context, several works have introduced adaptive kernel width algorithms to deal with this free parameter, but such solutions must be updated to manipulate complex-valued data. This work reviews and updates the most recent adaptive kernel width algorithms so that they become capable of dealing with complex-valued data using the complex correntropy. Besides that, a novel gradient-based solution is introduced to the Gaussian kernel and its respective convergence analysis. Simulations compare the performance of adaptive kernel width algorithms with different fixed kernel sizes in an impulsive noise environment. The results show that the iterative kernel adjustment improves the performance of the gradient solution for complex-valued data.

Adaptively Equalized Bandwidth Optimization Model using SCADA-DWWAN based Neural Network

“…𝑅 𝑘𝑘 −1 (𝑛 − 1). hk (n) (13) To estimate the Affinity Correlation margins in order to iteratively determine the Minimum Entropy distances between each constellation propagation path, let us first derive the reduced correntropy determinants for Kalman-LMS/KRLS traced AP/SGD as we obtain the adaptive training state model,-…”

“…Since industrial supervisory and control networks mostly focus and rely upon precise controllability as well as high observation accuracy, hence Artificial Neural Networks (ANN's) utilizing adaptive learning, training and decision models are potentially likely to provide reliable solutions [1] [16]. In our study, we have tested two popular ADALINE (Adaptive Linear Neuron) [6][9] [13] based neural network models for application in 'spectrum sensing' [5] as well as 'spectrum allocation' [12] [15] which are operationally semi- Finally, generic training and learning performances have been obtained, visualized and then virtually tested with reference to the overall improvement in bandwidth equalization [17][20] as well as spectrum sensing [8] over the entire multiple access transmission channel [1] [15].…”