4_2_09_NonlinearSystemIdentification

# 4_2_09_NonlinearSystemIdentification - Outline of the...

This preview shows pages 1–7. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Outline of the Lecture Outline of the Lecture Nonlinear System Identification Nonlinear System Identification Nonlinear Kernels Volterra Series Wiener Nonlinear Kernels Volterra Series Wiener Series Series Volterra and Wiener Kernels Volterra and Wiener Kernels Characterize Linear and Characterize Linear and Nonlinear Systems Nonlinear Systems In a black-box model, we try to describe a system well enough to predict its responses without knowing what is inside the system. If the black box is linear, then we can describe the system fully with the impulse response, as any stimulus is a sum of impulses. The impulse response D(t) is the reaction to a very short stimulus at time zero. One can use this model to estimate (r est (t)) the response of a linear system to stimulus s(t): r e s t t ( 29 = ρ + δ τ ∆ τ ( 29 σ τ - τ ( 29 ∞ ∫ The impulse response L(t) is the reaction to a very short stimulus at time zero. One can stimulus at time zero....
View Full Document

## This note was uploaded on 06/08/2009 for the course BME 575L taught by Professor Grzywacz during the Spring '09 term at USC.

### Page1 / 23

4_2_09_NonlinearSystemIdentification - Outline of the...

This preview shows document pages 1 - 7. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online