Abstract: A neural network determines optimal control inputs for a linear quadratic discrete-time process at M sampling times, the process being characterized by a quadratic cost function, p state variables, and r control variables. The network includes N=(p+r)M neurons, a distinct neuron being assigned to represent the value of each state variable at each sampling time and a distinct neuron being assigned to represent the value of each control variable at each sampling time. An input bias connected to each neuron has a value determined by the quandratic cost function for the variable represented by the neuron.

Abstract: An apparatus and a method are disclosed for composing an imposition in terms of an arrangement of printing plates on selected of the image positions on selected units of a printing press to print a given edition, by first assigning each section of this edition to one of the press areas. Thereafter, each printing unit is examined to determine an utilization value thereof in terms of the placement of the printing plates on the image positions and the relative number of image positions to which printing plates are assigned with respect to the total number of image positions. Thereafter, a list of the image positions for each of the sections and its area, is constructed by examining one printing unit at a time in an order according to the placement of that printing unit in the array and examining its utilization value to determine whether or not to include a particular image position of that printing unit in the list.