ABSTRACT
In the present chapter we conduct a qualitative analysis of analog and discrete-time neural networks which constitute linear systems oper ating on a closed hypercube in real n-space. Such networks, which were developed in Section 2.6, are described by linear differential equations, or by linear difference equations, defined on a closed hy percube. In implementations of such systems, all the state variables are constrained by saturation nonlinearities.
