Solving Different Numerical Linear Algebra Problems with a New General Fast Neurodynamics

The scientific research on issues related to numerical linear algebra constitutes an interesting topic for study in the last decades. In this manuscript, a general and fast recurrent neural network (RNN) model based on an extended activation function (AF) will be designed and applied for the solution of different numerical linear algebra problems. Theoretical convergence analysis and simulation examples, in simulink will show the efficiency and the accelerated convergence ability of the new dynamical system.