Nonlinear modeling of physiological systems with multiple inputs

Effective modeling of nonlinear dynamic systems can be achieved by employing Laguerre expansions and feedforward artificial neural networks in the form of the Laguerre-Volterra network (LVN). In this paper an extension of the LVN methodology to multiple-input systems is presented. Results from simulated systems show that this method can yield accurate nonlinear models of multiple-input Volterra systems, even when considerable noise is present.