Dynamical neural network for solving a class of nonlinear equation systems

This paper presents a dynamical neural network for solving a class of nonlinear equation systems, and shows that it globally converges to an exact solution. In hardware implementations, the network architecture does not contain the complex structure which implements the Jacobian matrix. Finally, an application of the neural network approach to two-point boundary value problems is given.