ABSTRACT
This paper will discuss a straight-forward approach to process control learning and generalization which is based on the geometric mapping of monitored inputs. It will be shown that when output thresholds or goals have been defined and are used as criteria for storing information, unique process shapes are learned. These shapes may be used to automatically derive the appropriate set of control adjustments for each generalized state of the problem.
