Abstract
Linear and non-linear controllable systems are commonly found in many engineering problems and are examples of models that involve complex high-dimensional spatially-related data sets. Such systems range in size and complexity from small-scale systems of a few state variables to large-scale systems comprising many state variables that evolve in complex ways. Particular evolution trajectories and regionalized evolutionary behavior of such complex systems are often non-intuitive and may reflect bifurcations in the system. A state space discretization approach leading to a novel structure termed Orthogonal Organized Finite State Machines (OOFSM) is proposed as a modeling technique to support visualizations of the properties of these types of complex systems.