Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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TL;DR: Several ways for obtaining aerodynamic parameters of an aircraft from flight data are presented with the emphasis on present problem areas and a rather detailed treatment of two more often used techniques for parameter estimation.
158 citations
TL;DR: The proposed iterative method enables simultaneous estimation of both the linear block parameters and all the parameters characterizing the nonlinearity, i.e., the slopes of linear segments and the constants determining the partition of domain.
158 citations
TL;DR: A spectral-approximation-based intelligent modeling approach is proposed for the distributed thermal processing of the snap curing oven that is used in semiconductor packaging industry and can be applied to a class of nonlinear DPSs in industrial thermal processing.
Abstract: A spectral-approximation-based intelligent modeling approach is proposed for the distributed thermal processing of the snap curing oven that is used in semiconductor packaging industry. The snap curing oven can be described by a nonlinear parabolic distributed parameter system (DPS) in the time-space domain. After finding a proper approximation of the complex boundary conditions of the system, the spectral methods can be applied to time-space separation and model reduction, and neural networks (NNs) can be used for state estimation and system identification. With the help of model reduction techniques, the dynamics of the curing process derived from physical laws can be described by a model of low-order nonlinear ordinary differential equations with a few uncertain parameters and unknown nonlinearities. A neural observer can then be designed to estimate the states of the ordinary differential equation model from measurements taken at specified locations in the field. Using the estimated states, a hybrid general regression NN is trained to be a nonlinear model of the curing process in state-space formulation, which is suitable for the further application of traditional control techniques. Real-time experiments on the snap curing oven show that the proposed modeling method is effective. This modeling methodology can be applied to a class of nonlinear DPSs in industrial thermal processing.
157 citations
01 Jan 2011
TL;DR: Two evolutionary computing approaches namely differential evolution and opposition based differential evolution combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification.
Abstract: This paper addresses the effectiveness of soft computing approaches such as evolutionary computation (EC) and neural network (NN) to system identification of nonlinear systems. In this work, two evolutionary computing approaches namely differential evolution (DE) and opposition based differential evolution (ODE) combined with Levenberg Marquardt algorithm have been considered for training the feed-forward neural network applied for nonlinear system identification. Results obtained envisage that the proposed combined opposition based differential evolution neural network (ODE-NN) approach to identification of nonlinear system exhibits better model identification accuracy compared to differential evolution neural network (DE-NN) approach. The above method is finally tested on a one degree of freedom (1DOF) highly nonlinear twin rotor multi-input-multi-output system (TRMS) to verify the identification performance.
157 citations