  Motivated by the advantages achieved by implicit analogue net for solving
online linear equations, a novel implicit neural model is designed based on
conventional explicit gradient neural networks in this letter by introducing a
positive-definite mass matrix. In addition to taking the advantages of the
implicit neural dynamics, the proposed implicit gradient neural networks can
still achieve globally exponential convergence to the unique theoretical
solution of linear equations and also global stability even under no-solution
and multi-solution situations. Simulative results verify theoretical
convergence analysis on the proposed neural dynamics.

Neural and Evolutionary Computing;Systems and Control