Robustly stable fixed point assignment problems for dynamical neural networks

Deals with a problem of assigning a prescribed set of points as robustly asymptotically stable fixed points of a dynamical neural network, called a robustly stable fixed point assignment problem. More precisely, using systems and control theory techniques, we introduce a state feedback structure into a neural network and propose a method to construct a parameter set for the closed-loop system in such a way that the fixed points in consideration for the network without feedback are unchanged but their stability robustness is maximized.