NARX Model Identification Using Correntropy Criterion in the Presence of Non-Gaussian Noise
- Computer ScienceJournal of Control, Automation and Electrical Systems
A novel identification method called simulation correntropy maximization with pruning (SCMP) based on information theoretic learning is introduced by this paper and has shown increased accuracy and robustness for three different experiments.
Identification of Nonlinear Dynamic Systems Using Fuzzy Hammerstein-Wiener Systems
- Engineering2019 19th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering (STA)
In this paper, a new fuzzy Hammerstein-Wiener model (FHWM) is developed in order to identify a nonlinear dynamic system operating in a stochastic environment. Wherein more general aspect is…
Nonparametric system identification of a cantilever beam model with local nonlinearity in the presence of artificial noise
- Engineering, Physics
ARTICLE INFORMATION ABSTRACT Original Research Paper Received 01 August 2016 Accepted 08 October 2016 Available Online 30 October 2016 In this paper the effect of artificial noise on the performance…
Data driven NARMAX modeling for PEMFC air compressor
A Conceptual Approach to Complex Model Management with Generalized Modelling Patterns and Evolutionary Identification
- Computer ScienceComplex.
A general evolutionary computation approach is proposed which enables managing of complexity and uncertainty of various kinds of complex models and is based on an evolutionary investigation of model phase space to identify the best model’s structure and parameters.
Identification of Nonlinear Modal Interactions in a Beam-Mass-Spring-Damper System based on Mono-Frequency Vibration Response
- EngineeringJournal of Computational Methods In Engineering
In this paper, nonlinear modal interactions caused by one-to-three internal resonance in a beam-mass-spring-damper system are investigated based on nonlinear system identification. For this purpose,…
Modified genetic crossover and mutation operators for sparse regressor selection in NARMAX brain connectivity modeling
- Biology2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)
The number of generations required to achieve optimal nonlinear regressor models with 99.9% confidence is reduced by 52% by a strategic modification to the crossover and mutation operators within the NSGA-II genetic algorithm.
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System models that are linear in parametric structure, but arbitrarily nonlinear in signal operations, are identified using an approach with two novel components using a set-theoretic analysis of the data to deduce feasible sets of solutions in light of certain model assumptions.
Evolutionary model selection for identification of nonlinear parametric systems
- Computer Science2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP)
This work opens the door to the use of a broadly generalized class of models with applicability to many contemporary signal processing problems, particularly the evolutionary model determination.
A New Deterministic Identification Approach to Hammerstein Systems
- MathematicsIEEE Transactions on Signal Processing
A new deterministic identification approach is presented, which blindly identifies the linear dynamic part followed by the estimation of the nonlinear function and can obtain the true values of the system parameters in the noise-free case and an asymptotically consistent estimate in the presence of noise.
Nonlinear black-box modeling in system identification: a unified overview
- Computer ScienceAutom.
Unified Set Membership theory for identification, prediction and filtering of nonlinear systems
- Computer ScienceAutom.
An affine projection-based algorithm for identification of nonlinear Hammerstein systems
- Computer ScienceSignal Process.
Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains
The NARMAX (nonlinear autoregressive moving average with exogenous inputs) model The orthogonal least squares algorithm that allows models to be built term by term where the error reduction ratio…
Efficient adaptive identification of linear-in-the-parameters nonlinear filters using periodic input sequences
- Engineering, Computer ScienceSignal Process.
Least-square identification with error bounds for real-time signal processing and control
- Computer Science
The optimal bounding ellipsoid (OBE) algorithms are interpreted as a blending of the classical least-square error minimization approach with knowledge of bounds on model errors arising from SM considerations, and a general framework embracing all currently used OBE algorithms is developed.
Set-membership identification and filtering for signal processing applications
- Computer Science
The underlying set-theoretic concepts are introduced, the various published OBE algorithms are compared and contrasts, and some illustrations of OBE algorithm performance are shown.