USING MANIFOLD LEARNING FOR NONLINEAR SYSTEM IDENTIFICATION

A high-dimensional regression space usually causes problems in nonlinear system identification. However, if the regression data are contained in (or spread tightly around) some manifold, the dimensionality can be reduced. This paper presents a use of dimension reduction techniques to compose a two-step identification scheme suitable for high-dimensional identification problems with manifold-valued regression data. Illustrating examples are also given.