THE IDENTIFICATION OF NONLINEAR BIOLOGICAL-SYSTEMS - VOLTERRA KERNEL APPROACHES

Representation, identification, and modeling are investigated for nonl inear biomedical systems. We begin by considering the conditions under which a nonlinear system can be represented or accurately approximate d by a Volterra series (or functional expansion). Next, we examine sys tem identification through estimating the kernels in a Volterra functi onal expansion approximation for the system. A recent kernel estimatio n technique that has proved to be effective in a number of biomedical applications is investigated as to running time and demonstrated on bo th clean and noisy data records, then it is used to illustrate identif ication of cascades of alternating dynamic linear and static nonlinear systems, both single-input single-output and multivariable cascades. During the presentation, we critically examine some interesting biolog ical applications of kernel estimation techniques.