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A Study on the System Identification based on Neural Network for Modeling of 5.1. Engines  

윤마루 (한양대학교 자동차공학과)
박승범 (한양대학교 자동차공학과)
선우명호 (한양대학교 자동차공학과)
이승종 (한양대학교 자동차공학과)
Publication Information
Transactions of the Korean Society of Automotive Engineers / v.10, no.5, 2002 , pp. 29-34 More about this Journal
Abstract
This study presents the process of the continuous-time system identification for unknown nonlinear systems. The Radial Basis Function(RBF) error filtering identification model is introduced at first. This identification scheme includes RBF network to approximate unknown function of nonlinear system which is structured by affine form. The neural network is trained by the adaptive law based on Lyapunov synthesis method. The identification scheme is applied to engine and the performance of RBF error filtering Identification model is verified by the simulation with a three-state engine model. The simulation results have revealed that the values of the estimated function show favorable agreement with the real values of the engine model. The introduced identification scheme can be effectively applied to model-based nonlinear control.
Keywords
System identification; Error filtering model; Neural Network; Radial basis function; Lyapunov stability; Engine model;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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