Papers by Keyword: Parameter Estimation

Abstract: Based on the global orthogonal polynomial algorithm, a global-piecewise fitting method for eliminating affections of modes outside of fitting bands is proposed. Both lower and higher modes outside of the fitting band are analyzed and processed. The frequency response data are revised by means of modes in two frequency bands close to the fitting band, and a curve fitting model is derived. Simulation results indicate that the proposed method possesses higher precision than the general rational fraction polynomial algorithm.

Abstract: Residual life prediction is a critical and difficult problem in condition-based maintenance decision-making. Aiming to deal with the problems that practical data is limited and the estimation of initial parameters is not accurate in maintenance practice, a residual life prediction method for gearbox based on stochastic filtering (SF) is proposed. In this method, recursive expectation maximization (REM) algorithm is introduced to update the parameters, and a maximum likelihood estimation method is designed to update the unknown parameters. Finally, the validity and practicability of the model are validated by an example.

Abstract: The parameter estimation of the Polynomial Phase Signals (PPS) is one of the core issues. In this paper, UKF-based algorithm is proposed to estimate the parameter of PPS embedded in Gaussian noise. The algorithm constructs an adequate state-space model to represent the PPS and the model can also be implied in real radar signal. Unscented Kalman filtering is applied to estimate the signal parameters. The method achieves the lower SNR threshold, the faster convergence speed, the higher accuracy and more stable estimation performance compared with the existing methods. Simulation also verifies the efficiency of the proposed method.

Abstract: A new data-aided algorithm for parameter estimation of the co-channel AIS signal transmitted over the additive white Gaussian noise channel is proposed in this paper. The co-channel signal consists of a strong signal with high power and a weak signal with low power. The parameters of the strong signal are estimated by searching the ambiguity function of the co-channel signal in two dimensions. A reference signal is therefore reconstructed with the estimated parameters and the aided data. By removing the ambiguity function of the reconstructed reference signal from that of the original co-channel signal, a new co-channel signal ambiguity function is obtained, from which the parameters of the weak signal are estimated. The simulation results illustrate that the proposed algorithm can estimate the parameters of the co-channel AIS signal effectively.

Abstract: This manuscript discusses about the Parameter estimation of Induction motor by utilizing the soft computing methodologies that is by using evolutionary algorithms such as Genetic algorithm, Particle swarm optimization, Artificial immune algorithm to overcome the difficulties in the conventional method where we calculating the per phase equivalent circuit parameters from the No load test and Blocked rotor test which compromises in result in terms of accuracy of the result and also evaluated the accuracy of the different algorithm in estimating the parameters of the induction motor.

Abstract: A graphing method is presented in this paper for estimating the parameters of solute transport in soils based on the convection-dispersion equation (CDE). The ratio of flux concentration change with time (dc/dt) is obtained on data of breakthrough curve (BTC) of solute transport in a semi-infinite soil column. Using graphing software, for example, the Microsoft Excel, can draw two curves of t^1.5dc/dt and dc/dt respect to time. Each curve has single peak. The two curves are used to estimate the retardation factor and dispersion coefficient in the CDE. Hypothetical examples and displacement experimental data of two soils were used to validate the new graphing method for its accuracy and stability for estimating the two parameters. The stability of parameter estimation is evaluated by the standard deviations of estimation parameters to their average value. The accuracy of the new method is assessed by comparing it with other two most used methods that are the CXTFIT and the slope methods. The results show that the method has both high accuracy and good stability. The new method is particularly good for the estimation of parameter R. The method is a deterministic method and it is simple in terms of calculations. It also has the advantages of uniqueness and no initial guess of the parameters over the CXTFIT method. The new method provides an alternative approach for estimating parameters of solute transport in soils. It is simple, accurate, stable, and saves time.

Abstract: In this paper, the rotational invariance technique (ESPRIT) based on the cross-correlation matrix is used to estimate the power quality indices (PQI) . This method enables the alleviation of the effects of additive noise, and improves the computational efficiency. Computer simulation and experimental were performed on synthesized signals to assess the performance of the method.

Abstract: It is difficult to estimate the parameters of Weibull distribution model using Maximum Likelihood Estimation based on Ant Colony Algorithm (ACA) or Particle Swarm Optimization theory (PSO) for which is easy to fall into premature and needs more variables, thus Fruit Fly Optimization Algorithm (FOA) theory is introduced into maximum likelihood estimation, and a parameter estimation method based on FOA theory is proposed, an example has been simulated to verify the feasibility and effectiveness of this method by comparing with ACA and PSO.

Abstract: In the past, various methods have been used to estimate the parameters in the nonlinear three-parameter Muskingum model to allow the model to more closely approximate a nonlinear relation compared to the original two-parameter Muskingum model. In this study, the particle swarm optimization algorithm based on the organizational evolutionary (OEPSO), which the evolutional operations are acted on organizations directly in the algorithm, and gained the global convergence ends through competition and cooperation, and overcome the shortcomings of the traditional PSO, is introduced. The OEPSO is proposed for the purpose of estimating the parameters of nonlinear Muskingum routing model. The performance of this approach is compared with other reported parameter estimation techniques. Results of the application of this approach to an example with high nonlinearity between storage and weighted-flow, show that the OEPSO approach is efficient in estimating parameters of the nonlinear routing models.

Abstract: The Chirp signal has been used widely in radar signal, radar echo wave can established to be Chirp model. The estimation of radar echo wave parameter is a important task in radar signal processing. In this paper, we introduced three theories and algorithms of detection and estimation of Chirp signal: 2D peak searching algorithm, two steps searching of maximum value algorithm and pre-estimation algorithm firstly. The parameter estimation precision and computation complexity in low SNR was simulated for these three algorithms. The final simulation indicate that the two steps searching algorithm of maximum value take on nice estimation accuracy and low computation complexity in contrast.