TY - JOUR
T1 - RKH space methods for low level monitoring and control of nonlinear systems II. A Vector-case example
T2 - The Lorenz system
AU - Cover, Alan
AU - Reneke, James
AU - Lenhart, Suzanne
AU - Protopopescu, Vladimir
PY - 1997/9
Y1 - 1997/9
N2 - By using techniques from the theory of reproducing kernel Hilbert (RKH) spaces, we continue the exploration of the stochastic linearization method for possibly unknown and/or noise corrupted nonlinear systems. The aim of this paper is twofold: (a) the stochastic linearization formalism is explicitly extended to the vector case; and (b) as an illustration, the performance of the stochastic linearization for monitoring and control is assessed in the case of the Lorenz system for which the dynamic behavior is known independently.
AB - By using techniques from the theory of reproducing kernel Hilbert (RKH) spaces, we continue the exploration of the stochastic linearization method for possibly unknown and/or noise corrupted nonlinear systems. The aim of this paper is twofold: (a) the stochastic linearization formalism is explicitly extended to the vector case; and (b) as an illustration, the performance of the stochastic linearization for monitoring and control is assessed in the case of the Lorenz system for which the dynamic behavior is known independently.
UR - http://www.scopus.com/inward/record.url?scp=0031508348&partnerID=8YFLogxK
U2 - 10.1142/S0218202597000426
DO - 10.1142/S0218202597000426
M3 - Article
AN - SCOPUS:0031508348
SN - 0218-2025
VL - 7
SP - 823
EP - 845
JO - Mathematical Models and Methods in Applied Sciences
JF - Mathematical Models and Methods in Applied Sciences
IS - 6
ER -