A novel quaternion linear matrix equation solver through zeroing neural networks with applications to acoustic source tracking

Abstract

Due to its significance in science and engineering, time-varying linear matrix equation (LME) problems have received a lot of attention from scholars. It is for this reason that the issue of finding the minimum-norm least-squares solution of the time-varying quaternion LME (ML-TQ-LME) is addressed in this study. This is accomplished using the zeroing neural network (ZNN) technique, which has achieved considerable success in tackling time-varying issues. In light of that, two new ZNN models are introduced to solve the ML-TQ-LME problem for time-varying quaternion matrices of arbitrary dimension. Two simulation experiments and two practical acoustic source tracking applications show that the models function superbly.

Keywords

• quaternion
• linear matrix equation
• minimum-norm least-squares solution
• dynamical system
• zeroing neural network
• acoustic source tracking