Noise-Tolerant Zeroing Neural Dynamics for Solving Hybrid Multilayered Time-Varying Linear Equation System

Abstract

Resource allocation problem in a wireless sensor network can be formulated as time-varying optimization, which can be further converted as time-varying linear equation system (TVLES). Hybrid multilayered time-varying linear equation system (HMTVLES) involving hybrid multilayers and time-variation characteristic is a complicated and challenging problem. Recently it has been solved by zeroing neural dynamics (ZND) method under ideal conditions, i.e., without noises. However, noises are ubiquitous, immanent and unavoidable in real-time systems. In this work, we propose a noise-tolerant zeroing neural dynamics (NTZND) model for solving HMTVLES. It can deal with different kinds of noises such as constant noise, linear-increasing noise, and random noise. Theoretical analyses guarantee the precision of NTZND model in the presence of different kinds of noises. In addition, a general NTZND model is proposed based on general activation function. Besides, classical ZND method and gradient neural dynamics (GND) method are also investigated and compared. Numerical experimental results are presented to verify the theoretical results of proposed models.

Multilayered hybrid time-varying problem solving based on integrated-enhanced zeroing neural network for robust manipulator control

“…In [29] , integrated-enhanced ZNN was employed to solve a mathematical problem of hybrid linear equation system, and the corresponding solution is robust. Aiming at enhancing the robustness of multi-task algorithm, inspired by [29] , a new solution method was used in this study.…”

“…In [29] , integrated-enhanced ZNN was employed to solve a mathematical problem of hybrid linear equation system, and the corresponding solution is robust. Aiming at enhancing the robustness of multi-task algorithm, inspired by [29] , a new solution method was used in this study. This method constructs the error function in integral form when solving multilayered hybrid time-varying problems and uses the integrated-enhanced ZNN to ensure that the final algorithm has strong robustness.…”

Recurrent Neural Network Models Based on Optimization Methods

“…A gradient-based neural dynamics for solving the matrix equation AXB = D was investigated in [8]. Gradient and zeroing dynamical systems aimed to solve the system of linear equations in both the TV and constant environment are investigated in [9][10][11]. GNN neural-dynamic schemes are substantially designed for solving equations with time-invariant coefficient matrices [11].…”

Retracted: Noise-Tolerant Zeroing Neural Dynamics for Solving Hybrid Multilayered Time-Varying Linear Equation System

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”