Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: An algorithm for constructing models on the basis of fuzzy and nonfuzzy data with the aid of fuzzy discretization and clustering techniques is proposed.
524 citations
TL;DR: In this paper, a method for system identification is proposed which is based on the error-correcting training procedure in learning machines, and is referred to as learning identification, which is applicable to cases where the input signal is random and nonstationary, and can be completed within a short time, so that it may be used to identify linear quasi-time-invariant systems in which some parameters vary slowly in comparison with the time required for identification.
Abstract: A method for system identification is proposed which is based on the error-correcting training procedure in learning machines, and is referred to as "learning identification." This learning identification is nondisturbing, is applicable to cases where the input signal is random and nonstationary, and can be completed within a short time, so that it may be used to identify linear quasi-time-invariant systems in which some parameters vary slowly in comparison with the time required for identification. This merit also makes it possible to eliminate noise disturbances by means of the moving average method. Computer simulation of the learning identification was carried out and the times required for identification were obtained for various cases. Some modifications of the learning identification were also investigated together with their computer simulations.
523 citations
TL;DR: In this article, a weighted global iteration procedure with an objective function is proposed for stable estimation, being incorporated into the extended Kalman filter algorithm, which is applied to system identification problems of seismic structural systems.
Abstract: The extended Kalman filter is applied to system identification problems of seismic structural systems. In order to obtain the stable and convergent solutions, a weighted global iteration procedure with an objective function is proposed for stable estimation, being incorporated into the extended Kalman filter algorithm. For the effectiveness of this present proposal, the identification problems are investigated for multiple degree-of-freedom linear systems, bilinear hysteretic systems, and equivalent linearization of bilinear hysteretic systems. As numerically shown examples, the weighted global iteration procedure may be useful to identification problems.
521 citations
TL;DR: This presentation aims at giving an overview of the “science” side of System identification, i.e. basic principles and results and at pointing to open problem areas in the practical, “art”, side of how to approach and solve a real problem.
520 citations
TL;DR: In this article, an optimal two-stage identification algorithm is presented for Hammerstein-Wiener systems, where two static nonlinear elements surround a linear block, and the algorithm is shown to be convergent in the absence of noise and convergent with probability one in the presence of white noise.
519 citations