Spyridon D. Mourtas

About

Spyridon D. Mourtas is a leading researcher in computational mathematics and neural dynamics, specializing in the development of advanced zeroing neural network (ZNN) models for solving complex, time-varying matrix problems. His work bridges theoretical innovation and practical application, with major contributions to solving complex-valued time-varying linear matrix equations and quaternion matrix inverses. Notably, his 2021 paper on QR decomposition-based solutions for complex-valued time-varying linear matrix equations has garnered 77 citations, demonstrating significant impact in the field. This work has direct applications in robotic motion tracking and angle-of-arrival localization, showcasing the real-world relevance of his research. Mourtas has further advanced the field by introducing higher-order ZNN architectures for calculating quaternion matrix inverses (2023, 16 citations) and pseudoinverses (2023, 6 citations), addressing critical challenges in engineering, physics, and computer science. His innovative approaches to time-varying quaternion problems have established him as a key figure in neural network-based matrix computation, with his methods offering efficient solutions to complex dynamic systems. Mourtas’s work continues to influence both theoretical research and practical implementations in robotics and signal processing.

Research Focus

Key Achievements

Top Papers

1. 1
   Solving Complex-Valued Time-Varying Linear Matrix Equations via QR Decomposition With Applications to Robotic Motion Tracking and on Angle-of-Arrival Localization

   77 citations · 2021
2. 2
   Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking

   16 citations · 2023
3. 3
   Computing quaternion matrix pseudoinverse with zeroing neural networks

   6 citations · 2023

Key Collaborators

Contact & Links