POISSON WAVELETS APPLIED TO MODEL IDENTIFICATION

作     者：KOSANOVICH, KA MOSER, AR PIOVOSO, MJ 

作者机构：DUPONT CO INCCENT RES & DEVEXPTL STNWILMINGTONDE 19880 UNIV S CAROLINASWEARINGTON ENGN CTRDEPT CHEM ENGNCOLUMBIASC 29208 

出 版 物：《JOURNAL OF PROCESS CONTROL》 (J Process Control)

年 卷 期：1995年第5卷第4期

页      面：225-234页

核心收录：

学科分类：0711[理学-系统科学] 07[理学] 0817[工学-化学工程与技术] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

主　　题：INTEGRAL TRANSFORMS MODULATING FUNCTIONS PARAMETER ESTIMATION 

摘      要：A novel wavelet transform is introduced based on the backward difference of the Poisson probability density function. This family of wavelets is a function of one discrete and two continuous variables. The Poisson wavelet transform is useful for system identification, parameter estimation and model validation. In particular, it is well-suited for linear time-invariant systems that are modelled as combinations of decaying exponentials with a single time delay. A fast computational algorithm for computing the Poisson wavelet transform is developed using cascaded first-order filters. The concepts are demonstrated on a three-tank process and a simplified heat exchanger.