A neural network least-square estimator

Problems in which the arguments of objective functions are real numbers are considered. Based on the concept of the Hopfield network, a neural network that solves the least-square estimation problem is derived. With this network, the objective function can converge to any inner point of a hypercube, giving a real-valued solution with very great speed. Because of the convex nature of the chosen energy function, the problem of convergence to a local minimum does not arise. Also introduced is a space iterative search technique for finding the optimum solution that can exist at any point within the space. Finally, simulation results are given for solving problems of linear systems and parameter estimations