Balanced realizations via gradient flow techniques

Abstract

The task of finding a class of balanced minimal realizations is shown to be equivalent to finding limiting solutions of certain gradient flow differential equations. By viewing such algebraic tasks in the context of calculus, they are amenable to analog computational solutions, or parallel processing machines, perhaps even neural networks. The convergence rates of the differential equations is exponential, and consequentially convergence is rapid and numerical stability properties are attractive.