  In this paper, the Parameter Switching (PS) algorithm is used to approximate
numerically attractors of a Hopfield Neural Network (HNN) system. The PS
algorithm is a convergent scheme designed for approximating attractors of an
autonomous nonlinear system, depending linearly on a real parameter. Aided by
the PS algorithm, it is shown that every attractor of the HNN system can be
expressed as a convex combination of other attractors. The HNN system can
easily be written in the form of a linear parameter dependence system, to which
the PS algorithm can be applied. This work suggests the possibility to use the
PS algorithm as a control-like or anticontrol-like method for chaos.
