System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
Performance analysis of multi-innovation gradient type identification methods
Automatica (Journal of IFAC)
Reconstruction of continuous-time systems from their non-uniformly sampled discrete-time systems
Automatica (Journal of IFAC)
Nonlinear parameter estimation by weighted linear associative memory with nonzero interception
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Interval fuzzy modeling applied to Wiener models with uncertainties
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Modeling emotional content of music using system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Predictive Control-Based Approach to Networked Hammerstein Systems: Design and Stability Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neighborhood Detection for the Identification of Spatiotemporal Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of nonlinear systems using random amplitude Poisson distributed input functions
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fuzzy bounded least-squares method for the identification of linear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
State estimation with biased observations
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Parameter Identification and Intersample Output Estimation for Dual-Rate Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Combined parameter and output estimation of dual-rate systems using an auxiliary model
Automatica (Journal of IFAC)
Identification of Hammerstein nonlinear ARMAX systems
Automatica (Journal of IFAC)
Several multi-innovation identification methods
Digital Signal Processing
LSMS/ICSEE'10 Proceedings of the 2010 international conference on Life system modeling and and intelligent computing, and 2010 international conference on Intelligent computing for sustainable energy and environment: Part I
Computers & Mathematics with Applications
Identification methods for Hammerstein nonlinear systems
Digital Signal Processing
Parameter estimation with scarce measurements
Automatica (Journal of IFAC)
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Mathematics and Computers in Simulation
Observable state space realizations for multivariable systems
Computers & Mathematics with Applications
Mathematical and Computer Modelling: An International Journal
Auxiliary model based multi-innovation algorithms for multivariable nonlinear systems
Mathematical and Computer Modelling: An International Journal
Identification for the second-order systems based on the step response
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Mathematical and Computer Modelling: An International Journal
Parameter estimation for nonlinear dynamical adjustment models
Mathematical and Computer Modelling: An International Journal
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A multiinnovation least-squares (MILS) identification algorithm is presented for linear regression models with unknown parameter vectors by expanding the innovation length in the traditional recursive least-squares (RLS) algorithm from the viewpoint of innovation modification. Because the proposed MILS algorithm uses p innovations (not only the current innovation but also past innovations) at each iteration (with the integer p 1 being an innovation length), the accuracy of parameter estimation is improved, compared with that of the RLS algorithm. Performance analysis and simulation results show that the proposed MILS algorithm is consistently convergent. Moreover, a new interval-varying MILS algorithm is proposed, for which the key is to dynamically change the interval in order to deal with cases where some measurement data are missing. Furthermore, an auxiliary-model-based MILS algorithm is derived for pseudolinear models corresponding to output error moving average systems with colored noises. Finally, the proposed algorithms are applied to model an experimental water level control system.