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This article investigates the identification issue of the bilinear system in the presence of the impulsive noise. The bilinear system based on the observer canonical form is translated into a regressive form, and a bilinear state observer is established to estimate the state variables. To overcome the effects of the impulsive noise to parameter estimation, the proposed algorithms employ a generalized continuous mixed p$$ p $$‐norm cost function, which can generate an adjustable gain that control the proportions of the error norms without resorting to a priori knowledge of the noise. Moreover, a sliding window is designed to update the dynamical data by removing the oldest data and adding the newest measurement data. An numerical example exhibits that the proposed algorithms can reduce the impact of the impulsive noise to parameter estimation and improve the parameter estimation accuracy compared with the conventional algorithms. [ABSTRACT FROM AUTHOR]

Summary: This article studies the parameter identification problem for bilinear state space models with time‐varying time delays. Considering the correlation of time delays, the Markov chain switching mechanism is adopted to model the delay sequence. Based on the observer canonical form, the bilinear state space model is transformed into a regressive form. A bilinear state observer is designed to estimate the state variables. Under the variational Bayesian scheme, the system parameters, discrete delays, and the Markov transition probabilities are identified by using the measurement data. A numerical example and a continuous stirred tank reactor simulation are employed to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Ranging error is known to degrade significantly the target node localization accuracy. This paper investigates the use of computationally efficient positioning solution of least square (LS) in closed-form, to reduce localization accuracy loss caused by ranging error. For range-based node localization, the LS solution based on least square criterion has been confirmed to exhibit capability of optimum estimation but extensively achieve at a very complex calculation. In this paper we consider the problem how to acquire such LS solution provided with estimation performance at low complex calculation. In this paper, we use the Gauss noise model and use the weighted least squares criterion and the effective calculation method to solve the linearized equation derived from the RSS measurement, and put forward a new approach to estimate the performance of the target node location estimation. Based on the Fisher information matrix, the Cramér-Rao lower bound of target position estimation is derived based on received signal strength. We obviously indicate that the proposed algorithm can approximately achieve the LS solution in estimation performance at a markedly low complex calculation. Simulations are performed to show the improvement of the proposed algorithm. [ABSTRACT FROM AUTHOR]

A new approach for modeling and monitoring of the multivariate processes in presence of faulty and missing observations is introduced. It is assumed that operating modes of the process can transit to each other following a Markov chain model. Transition probabilities of the Markov chain are time varying as a function of the scheduling variable. Therefore, the transition probabilities will be able to vary adaptively according to different operating modes. In order to handle the problem of missing observations and unknown operating regimes, the expectation maximization algorithm is used to estimate the parameters. The proposed method is tested on two simulations and one industrial case studies. The industrial case study is the abnormal operating condition diagnosis in the primary separation vessel of oil-sand processes. In comparison to the conventional methods, the proposed method shows superior performance in detection of different operating conditions of the process. © 2014 American Institute of Chemical Engineers AIChE J, 61: 477-493, 2015 [ABSTRACT FROM AUTHOR]

This paper is concerned with theidentification problems of thelinear parameter varying (LPV) system with missing output in the presenceof the time-delay. A multiple-model approach is adopted. Local modelsvarying from one operating point to another are first described byfinite impulse response (FIR) models. To handle missing output andtime-delay, the expectation-maximization (EM) algorithm is utilizedto estimate the unknown parameters and the time-delay simultaneously.Output Error (OE) models are widely used in controller design. Therefore,the auxiliary model principle is employed to recover the OE modelsbased on the initially identified FIR models. The EM algorithm isthen used again to refine the unknown parameters of the OE modelswith the complete data set to obtain the final global model. Simulationexamples are presented to demonstrate the performance of the proposedmethod. [ABSTRACT FROM AUTHOR]

Abstract: This letter presents an iterative estimation algorithm for modeling a class of output nonlinear systems. The basic idea is to derive an estimation model and to solve an optimization problem using the gradient search. The proposed iterative numerical algorithm can estimate the parameters of a class of Wiener nonlinear systems from input–output measurement data. The proposed algorithm has faster convergence rates compared with the stochastic gradient algorithm. The numerical simulation results indicate that the proposed algorithm works well. [Copyright &y& Elsevier]

This paper considers parameter estimation for linear parameter varying (LPV) systems with randomly missing output data. The multi-model LPV model is adopted and the identification problem is formulated under the scheme of the generalized expectation–maximization (GEM) algorithm. In order to deal with the missing output data, the local models are firstly taken to have the finite impulse response (FIR) model structure. To alleviate potential overparameterization problem, a prior on FIR model coefficients is imposed and the GEM algorithm is modified to derive the maximum a posterior (MAP) estimates of the multi-mode LPV FIR model parameters. Since the FIR model is not suitable for general control applications, a multi-mode LPV output error (OE) model is then identified by applying the GEM algorithm to the same identification data set with parameters initialized based on the estimated FIR models. One simulation example and two experiments are presented to demonstrate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]