An exhaustive review of adaptive signal processing schemes which are robust, sparsity-
aware and robust as well as sparsity-aware has been carried out in this paper. Conventional …

This paper is concerned with the distributed filtering issue under the Cauchy-kernel-based
maximum correntropy for large-scale systems subject to randomly occurring cyber-attacks in …

For multilayer perceptron (MLP), the initial weights will significantly influence its
performance. Based on the enhanced fractional derivative extend from convex optimization …

In this manuscript, we describe a novel methodology for nearfield acoustic holography
(NAH). The proposed technique is based on convolutional neural networks, with …

This work revisits the problem of distributed adaptive filtering in multi-agent sensor networks.
In contrast to classical approaches, the formulation relaxes the Gaussian assumption on the …

This brief proposes an enhanced fractional derivative that can prevent the tap weight
coefficients from destroying the gradient information, solve the problem caused by the …

Employing the traditional least-mean-square (LMS) algorithm to estimate the weight vector
of an unknown system will result in an estimation bias when the input signal of the adaptive …

Recent years have brought to attention filtering and state estimation paradigms in systems
that exhibit rapidly changing states. Dynamics of such systems falls beyond what can be …

In an increasing number of modern filtering applications, the encountered signals consist of
frequent sharp spikes, that cannot be accurately modeled using Gaussian random …

As a widely used method in the errors-in-variables (EIV) model, total least square (TLS) can
work well for both input and output signals disturbed with noises. The TLS based adaptive …