A Dynamic Gain Fixed-Time Robust ZNN Model for Time-Variant Equality Constrained Quaternion Least Squares Problem...

Area of Science:

• Neural Networks
• Control Systems
• Numerical Analysis

Background:

• Conventional algorithms struggle with time-variant problems.
• Quaternion least squares problems require specialized solutions.
• Existing zeroing neural network (ZNN) models have limitations.

Purpose of the Study:

• To propose a dynamic gain fixed-time (FXT) robust zeroing neural network (DFTRZNN) model.
• To address the time-variant equality constrained quaternion least squares (TV-EQLS) problem.
• To enhance stability and robustness in solving complex dynamic systems.

Main Methods:

• Development of a novel DFTRZNN model incorporating a dynamic gain parameter and a novel activation function (NAF).
• Comprehensive theoretical derivation and analysis of the FXT stability and robustness.
• Simulation of the DFTRZNN model for solving TV-EQLS.

Main Results:

• The DFTRZNN model effectively solves the TV-EQLS problem.
• Demonstrated superiority over conventional numerical algorithms for time-variant problems.
• Validated FXT stability and robustness through theoretical analysis and simulations.

Conclusions:

• The proposed DFTRZNN model is a superior approach for TV-EQLS.
• The model's design scheme has practical applications in multiagent systems consensus.
• The DFTRZNN model offers enhanced performance and applicability in dynamic systems.