Solving the inverse dynamics problem by self-organizing maps

In this paper a recursive computation procedure for recovering the inputs of a dynamic process based on a preliminary assumed number of measured discrete outputs is proposed and analyzed. A specially constructed self-organizing map is first trained in off-line mode and is further used in real-time as a tool for classification and revealing the existing cause-effect relations between the unknown discrete input and the measured outputs. The proposed computation procedure gives a discrete solution of the inverse problem constrained within the preliminary assumed discrete levels of the input. The number of these levels is directly connected to the final computation accuracy. The consistency of the proposed computation scheme for solving the inverse problem is extensively analyzed on a test dynamic process. Simulation results show its applicability for solving different backward tracking problems, including fault diagnosis problems that heavily rely on a robust and plausible solution of the inverse problem.