nonlinear system identification algorithms
Nonlinear System Identification Using Hammerstein-Wiener Neural Network and subspace algorithms
... in identification from input-output ...block-oriented nonlinear systems where the linear dynamic block is sandwiched in between two static nonlinear blocks, which appear in many engineering ...
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Parameters Identification of Nonlinear DC Motor Model Using Compound Evolution Algorithms
... genetic algorithms (GA) with global optimization character and the simplex method are combined and used into the application of the parameter ...The nonlinear dynamic model of an actual DC motor including ...
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IIR System Identification Using Improved Harmony Search Algorithm with Chaos
... generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such ...the system identification problem of IIR models is ...
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Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... for nonlinear system identification, which includes converting a least squares regression problem into the associated ultra least squares problem and solving the ultra least squares problem using the ...
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Development of an Efficient Face Recognition System Based on Linear and Nonlinear Algorithms
... recognition system is a very important area under discussion because of the wide range of applications in different spheres, such as video surveillance security systems, control of documents, forensics systems ...
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Sparse Bayesian Nonlinear System Identification using Variational Inference
... other algorithms (for 164 terms, it took ∼20 seconds per trial of 100,000 iterations but requiring multiple trials, in this case 100, ...other algorithms remained the ...tested algorithms were ...
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Review of rational (total) nonlinear dynamic system modelling, identification, and control
... (total) nonlinear dynamic modelling over the last two decades. Total nonlinear dynamic systems are defined as those where the model parameters and input (controller outputs) are subject to nonlinear ...
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Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing
... to algorithms such as Transitional MCMC (TMCMC) [17] and Asymptotically Independent Markov Sampling (AIMS) [18], the user has less control over the rate at which the influence of the likelihood is increased during ...
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Duct Modeling Using the Generalized RBF Neural Network for Active Cancellation of Variable Frequency Narrow Band Noise
... of nonlinear e ff ects in ANC complicates the use of the linear algorithm FX-LMS and similar ...purpose, identification systems with a nonlinear struc- ture are used where a neural network is among ...
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A new class of wavelet networks for nonlinear system identification
... to nonlinear static function approxima- tion and classification [17], [21]–[24], and dynamical system modeling [25], ...type algorithms are sensitive to initial conditions, that is, the ...
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Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing
... to algorithms such as Tran- sitional MCMC (TMCMC) [17] and Asymptotically Independent Markov Sampling (AIMS) [18], the user has less control over the rate at which the influence of the likelihood is increased ...
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An Adaptive Nonlinear Filter for System Identification
... In nonlinear system identification, input signals with high eigen value spread, ill-conditioned tap input autocorre- lation matrix can lead to divergence or poor performance of a fixed step-size ...
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Inference techniques for stochastic nonlinear system identification with application to the Wiener-Hammerstein models
... initialization algorithms are derived for the Wiener-Hammerstein model, i.e. a nonlinear model where a static nonlinearity is sandwiched between two linear dynamical ...the system provides a ...
152
Iterative algorithms approach to a general system of nonlinear variational inequalities with perturbed mappings and fixed point problems for nonexpansive semigroups
... iterative algorithms (3.15) below for finding a common element of the set of solutions of a general system of nonlinear variational inequalities with perturbed mappings (1.15) and the set of common ...
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Neighbourhood detection and indentification of spatio-temporal dynamical systems using a coarse-to-fine approach
... A novel approach to the identification of spatio-temporal dynamical systems has been introduced. It has been demonstrated that determining the neighbourhood for a real-value CML model can be significantly ...
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Identification of nonlinear systems
... the identification algorithm but rather are to be identified, nor is the identification algorithm provided with the information as to how the controller is designed, though the algorithm is provided with ...
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Sparse generalized kernel modeling for nonlinear systems
... Conclusions • A construction algorithm has been proposed for nonlinear system identification using the generalised kernel model – The algorithm has ability to tune the centre and covaria[r] ...
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A practical approach to parameter identification for a lightly damped, weakly nonlinear system
... fluidelastic system have been obtained using the application of the FREEVIB method described ...investigated system is considered to have the linear characteristics as shown in ...fluidelastic system ...
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Curriculum Vitae. Education
... at Université Paris-Est Marne-la-Vallée, supervised by C.Soize and entitled ’Identification of stochastic forces applied to a nonlinear dyna- mical system using an uncertain computationa[r] ...
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INCORPORATING DEOXYRIBONUCLEIC ACID IN AES SCHEME FOR ENHANCING SECURITY AND PRIVACY PROTECTION
... of system control has seen significant ...or nonlinear systems using the linear PID controller based on a constant ...For nonlinear systems, PID performance diminishes under a great deal of tuning ...
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