Parameter Estimation of a Valve-Controlled Cylinder System Model Based on Bench Test and Operating Data Fusion

The accurate estimation of parameters is the premise for establishing a high-fidelity simulation model of a valve-controlled cylinder system.Bench test data are easily obtained,but it is challenging to emulate actual loads in the research on parameter estimation of valve-controlled cylinder system.Despite the actual load information contained in the operating data of the control valve,its acquisition remains challenging.This paper proposes a method that fuses bench test and operating data for parameter estimation to address the aforementioned problems.The proposed method is based on Bayesian theory,and its core is a pool fusion of prior information from bench test and operating data.Firstly,a system model is established,and the parameters in the model are analysed.Secondly,the bench and operating data of the system are collected.Then,the model parameters and weight coefficients are estimated using the data fusion method.Finally,the estimated effects of the data fusion method,Bayesian method,and particle swarm optimisation(PSO)algorithm on system model parameters are compared.The research shows that the weight coefficient represents the contribution of different prior information to the parameter estimation result.The effect of parameter estimation based on the data fusion method is better than that of the Bayesian method and the PSO algorithm.Increasing load complexity leads to a decrease in model accuracy,highlighting the crucial role of the data fusion method in parameter estimation studies.

Parameter estimation in n-dimensional massless scalar field

Quantum Fisher information(QFI)associated with local metrology has been used to parameter estimation in open quantum systems.In this work,we calculated the QFI for a moving Unruh-DeWitt detector coupled with massless scalar fields in n-dimensional spacetime,and analyzed the behavior of QFI with various parameters,such as the dimension of spacetime,evolution time,and Unruh temperature.We discovered that the QFI of state parameter decreases monotonically from 1 to 0 over time.Additionally,we noted that the QFI for small evolution times is several orders of magnitude higher than the QFI for long evolution times.We also found that the value of QFI decreases at first and then stabilizes as the Unruh temperature increases.It was observed that the QFI depends on initial state parameterθ,and Fθis the maximum forθ=0 orθ=π,Fφis the maximum forθ=π/2.We also obtain that the maximum value of QFI for state parameters varies for different spacetime dimensions with the same evolution time.

Coarse-fine joint target parameter estimation method based on AN-RSC in OFDM passive radar

In this paper,we study the accuracy of delay-Doppler parameter estimation of targets in a passive radar using orthogonal frequency division multiplexing(OFDM)signal.A coarse-fine joint estimation method is proposed to achieve better estimation accuracy of target parameters without excessive computational burden.Firstly,the modulation symbol domain(MSD)method is used to roughly estimate the delay and Doppler of targets.Then,to obtain high-precision Doppler estimation,the atomic norm(AN)based on the multiple measurement vectors(MMV)model(MMV-AN)is used to manifest the signal sparsity in the continuous Doppler domain.At the same time,a reference signal compensation(RSC)method is presented to obtain highprecision delay estimation.Simulation results based on the OFDM signal show that the coarse-fine joint estimation method based on AN-RSC can obtain a more accurate estimation of target parameters compared with other algorithms.In addition,the proposed method also possesses computational advantages compared with the joint parameter estimation.

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

Design of a Multifrequency Signal Parameter Estimation Method for the Distribution Network Based on HIpST

The application of traditional synchronous measurement methods is limited by frequent fluctuations of electrical signals and complex frequency components in distribution networks.Therefore,it is critical to find solutions to the issues of multifrequency parameter estimation and synchronous measurement estimation accuracy in the complex environment of distribution networks.By utilizing the multifrequency sensing capabilities of discrete Fourier transform signals and Taylor series for dynamic signal processing,a multifrequency signal estimation approach based on HT-IpDFT-STWLS(HIpST)for distribution networks is provided.First,by introducing the Hilbert transform(HT),the influence of noise on the estimation algorithm is reduced.Second,signal frequency components are obtained on the basis of the calculated signal envelope spectrum,and the interpolated discrete Fourier transform(IpDFT)frequency coarse estimation results are used as the initial values of symmetric Taylor weighted least squares(STWLS)to achieve high-precision parameter estimation under the dynamic changes of the signal,and the method increases the number of discrete Fourier.Third,the accuracy of this proposed method is verified by simulation analysis.Data show that this proposed method can accurately achieve the parameter estimation of multifrequency signals in distribution networks.This approach provides a solution for the application of phasor measurement units in distribution networks.

Numerical Simulation and Parameter Estimation of Fractional-Order Dynamic Epidemic Model for COVID-19

The outbreak of COVID-19 in 2019 resulted in numerous infections and deaths. In order to better study the transmission of COVID-19, this article adopts an improved fractional-order SIR model. Firstly, the properties of the model are studied, including the feasible domain and bounded solutions of the system. Secondly, the stability of the system is discussed, among other things. Then, the GMMP method is introduced to obtain numerical solutions for the COVID-19 system and combined with the improved MH-NMSS-PSO parameter estimation method to fit the real data of Delhi, India from April 1, 2020 to June 30, 2020. The results show that the fitting effect is quite ideal. Finally, long-term predictions were made on the number of infections. We accurately estimate that the peak number of infections in Delhi, India, can reach around 2.1 million. This paper also compares the fitting performance of the integer-order COVID-19 model and the fractional-order COVID-19 model using the real data from Delhi. The results indicate that the fractional-order model with different orders, as we proposed, performs the best.

Multi Parameter Adaptive Estimation of Reaction-Diffusion Equation

This study addresses the problem of parameter estimation for a one-dimensional reaction-diffusion equation, involving both unknown domain parameters and unknown boundary parameters. The proposed approach utilizes the least-squares method to design an adaptive law for parameter estimation. The convergence analysis demonstrates that under persistent excitation conditions, the adaptive law converges exponentially to zero, indicating that the estimated parameters converge exponentially to their true values. Numerical simulations confirm the effectiveness. Furthermore, it is shown that within a certain range of the reaction coefficient, the auxiliary system acts as a state observer, providing an accurate estimate of the system state at an exponential rate. .

An Immune Self-adaptive Differential Evolution Algorithm with Application to Estimate Kinetic Parameters for Homogeneous Mercury Oxidation

A new version of differential evolution （DE） algorithm, in which immune concepts and methods are applied to determine the parameter setting, named immune self-adaptive differential evolution （ISDE）, is proposed to improve the performance of the DE algorithm. During the actual operation, ISDE seeks the optimal parameters arising from the evolutionary process, which enable ISDE to alter the algorithm for different optimization problems and improve the performance of ISDE by the control parameters＇ self-adaptation. The .performance of the proposed method is studied with the use of nine benchmark problems and compared with original DE algorithm ~nd-other well-known self-adaptive DE algorithms. The experiments conducted show that the ISDE clearly outperforms the other DE algorithms in all benchmark functions. Furthermore, ISDE is applied to develop the kinetic model for homogeneous mercury. （Hg） oxidation in flue gas, and satisfactory results are obtained.

Conditional inversion to estimate parameters from eddy-flux observations

Aims Data assimilation is a useful tool to extract information from large datasets of the net ecosystem exchange(NEE)of CO_(2) obtained by eddy-flux measurements.However,the number of parameters in ecosystem models that can be constrained by eddy-flux data is limited by conventional inverse analysis that estimates parameter values based on one-time inversion.This study aimed to improve data assimilation to increase the number of constrained parameters.Methods In this study,we developed conditional Bayesian inversion to maximize the number of parameters to be constrained by NEE data in several steps.In each step,we conducted a Bayesian inversion to constrain parameters.The maximum likelihood estimates of the constrained parameters were then used as prior to fix parameter values in the next step of inversion.The conditional inversion was repeated until there were no more parameters that could be further constrained.We applied the conditional inversion to hourly NEE data from Harvard Forest with a physiologically based ecosystem model.Important Findings Results showed that the conventional inversion method constrained 6 of 16 parameters in the model while the conditional inversion method constrained 13 parameters after six steps.The cost function that indicates mismatch between the modeled and observed data decreased with each step of conditional Bayesian inversion.The Bayesian information criterion also decreased,suggesting reduced information loss with each step of conditional Bayesian inversion.A wavelet analysis reflected that model performance under conditional Bayesian inversion was better than that under conventional inversion at multiple time scales,except for seasonal and half-yearly scales.In addition,our analysis also demonstrated that parameter convergence in a subsequent step of the conditional inversion depended on correlations with the parameters constrained in a previous step.Overall,the conditional Bayesian inversion substantially increased the number of parameters to be constrained by NEE data and can be a powerful tool to be used in data assimilation in ecology.

Improved cat swarm optimization for parameter estimation of mixed additive and multiplicative random error model

To estimate the parameters of the mixed additive and multiplicative(MAM)random error model using the weighted least squares iterative algorithm that requires derivation of the complex weight array,we introduce a derivative-free cat swarm optimization for parameter estimation.We embed the Powell method,which uses conjugate direction acceleration and does not need to derive the objective function,into the original cat swarm optimization to accelerate its convergence speed and search accuracy.We use the ordinary least squares,weighted least squares,original cat swarm optimization,particle swarm algorithm and improved cat swarm optimization to estimate the parameters of the straight-line fitting MAM model with lower nonlinearity and the DEM MAM model with higher nonlinearity,respectively.The experimental results show that the improved cat swarm optimization has faster convergence speed,higher search accuracy,and better stability than the original cat swarm optimization and the particle swarm algorithm.At the same time,the improved cat swarm optimization can obtain results consistent with the weighted least squares method based on the objective function only while avoiding multiple complex weight array derivations.The method in this paper provides a new idea for theoretical research on parameter estimation of MAM error models.

Runoff Modeling in Ungauged Catchments Using Machine Learning Algorithm-Based Model Parameters Regionalization Methodology

Model parameters estimation is a pivotal issue for runoff modeling in ungauged catchments.The nonlinear relationship between model parameters and catchment descriptors is a major obstacle for parameter regionalization,which is the most widely used approach.Runoff modeling was studied in 38 catchments located in the Yellow–Huai–Hai River Basin(YHHRB).The values of the Nash–Sutcliffe efficiency coefficient(NSE),coefficient of determination(R2),and percent bias(PBIAS)indicated the acceptable performance of the soil and water assessment tool(SWAT)model in the YHHRB.Nine descriptors belonging to the categories of climate,soil,vegetation,and topography were used to express the catchment characteristics related to the hydrological processes.The quantitative relationships between the parameters of the SWAT model and the catchment descriptors were analyzed by six regression-based models,including linear regression(LR)equations,support vector regression(SVR),random forest(RF),k-nearest neighbor(kNN),decision tree(DT),and radial basis function(RBF).Each of the 38 catchments was assumed to be an ungauged catchment in turn.Then,the parameters in each target catchment were estimated by the constructed regression models based on the remaining 37 donor catchments.Furthermore,the similaritybased regionalization scheme was used for comparison with the regression-based approach.The results indicated that the runoff with the highest accuracy was modeled by the SVR-based scheme in ungauged catchments.Compared with the traditional LR-based approach,the accuracy of the runoff modeling in ungauged catchments was improved by the machine learning algorithms because of the outstanding capability to deal with nonlinear relationships.The performances of different approaches were similar in humid regions,while the advantages of the machine learning techniques were more evident in arid regions.When the study area contained nested catchments,the best result was calculated with the similarity-based parameter regionalization scheme because of the high catchment density and short spatial distance.The new findings could improve flood forecasting and water resources planning in regions that lack observed data.

Differences in parameter estimates derived from various methods for the ORYZA(v3) Model

Parameter estimation is always a difficult issue for crop model users, and inaccurate parameter values will result in deceptive model predictions. Parameter values may vary with different inversion methods due to equifinality and differences in the estimating processes. Therefore, it is of great importance to evaluate the factors which may influence parameter estimates and to make a comparison of the current widely-used methods. In this study, three popular frequentist methods(SCE-UA, GA and PEST) and two Bayesian-based methods(GLUE and MCMC-AM) were applied to estimate nine cultivar parameters using the ORYZA(v3) Model. The results showed that there were substantial differences between the parameter estimates derived by the different methods, and they had strong effects on model predictions. The parameter estimates given by the frequentist methods were obviously sensitive to initial values, and the extent of the sensitivity varied with algorithms and objective functions. Among the frequentist methods, the SCE-UA was recommended due to the balance between stable convergence and high efficiency. All the parameter estimates remarkably improved the goodness of model-fit, and the parameter estimates derived from the Bayesian-based methods had relatively worse performance compared to the frequentist methods. In particular, the parameter estimates with the highest probability density of posterior distributions derived from the MCMC-AM method(MCMC_P_(max)) led to results equivalent to those derived from the frequentist methods, and even better in some situations. Additionally, model accuracy was greatly influenced by the values of phenology parameters in validation.

Parameter estimation of LFM signals based on time reversal

In this paper,parameter estimation of linear frequency modulation(LFM)signals containing additive white Gaussian noise is studied.Because the center frequency estimation of an LFM signal is affected by the error propagation effect,resulting in a higher signal to noise ratio(SNR)threshold,a parameter estimation method for LFM signals based on time reversal is proposed.The proposed method avoids SNR loss in the process of estimating the frequency,thus reducing the SNR threshold.The simulation results show that the threshold is reduced by 5 dB compared with the discrete polynomial transform(DPT)method,and the root-mean-square error(RMSE)of the proposed estimator is close to the Cramer-Rao lower bound(CRLB).

Parameters estimate of recurrent quantum stochastic filter for time variant frequency periodic signals

Designing optimal time and spatial difference step size is the key technology for quantum-random filtering(QSF)to realize time-varying frequency periodic signal filtering.In this paper,it was proposed to use the short-time Fourier transform(STFT)to dynamically estimate the signal to noise ratio(SNR)and relative frequency of the input time-varying frequency periodic signal.Then the model of time and space difference step size and signal to noise ratio(SNR)and relative frequency of quantum random filter is established by least square method.Finally,the parameters of the quantum filter can be determined step by step by analyzing the characteristics of the actual signal.The simulation results of single-frequency signal and frequency time-varying signal show that the proposed method can quickly and accurately design the optimal filter parameters based on the characteristics of the input signal,and achieve significant filtering effects.

Mathematical Analysis of Two Approaches for Optimal Parameter Estimates to Modeling Time Dependent Properties of Viscoelastic Materials

Mathematical models for phenomena in the physical sciences are typically parameter-dependent, and the estimation of parameters that optimally model the trends suggested by experimental observation depends on how model-observation discrepancies are quantified. Commonly used parameter estimation techniques based on least-squares minimization of the model-observation discrepancies assume that the discrepancies are quantified with the L2-norm applied to a discrepancy function. While techniques based on such an assumption work well for many applications, other applications are better suited for least-squared minimization approaches that are based on other norm or inner-product induced topologies. Motivated by an application in the material sciences, the new alternative least-squares approach is defined and an insightful analytical comparison with a baseline least-squares approach is provided.

Super-resolution parameter estimation of monopulse radar by wide-narrowband joint processing

The angular resolution of radar is of crucial signifi-cance to its tracking performance.In this paper,a super-resolu-tion parameter estimation algorithm based on wide-narrowband joint processing is proposed to improve the angular resolution of wideband monopulse radar.The range cells containing resolv-able scattering points are detected in the wideband mode,and these range cells are adopted to estimate part of the target parameters by algorithms of low computational requirement.Then,the likelihood function of the echo is constructed in the narrow-band mode to estimate the rest of the parameters,and the parameters estimated in the wideband mode are employed to reduce computation and enhance estimation accuracy.Simu-lation results demonstrate that the proposed algorithm has higher estimation accuracy and lower computational complexity than the current algorithm and can avoid the risk of model mis-match.

A NEW ESTIMATE OF SHAPE PARAMETER IN THE FAMILY OF GAMMA DISTRIBUTION

In this paper, a new estimator of the shape parameter in the family of Gamma distribution is constructed by using the moment idea, and it is proved that this estimator is strongly consistent and asymptotically normal.

Parameter estimation method for a linear frequency modulation signal with a Duffing oscillator based on frequency periodicity

In view of the complexity of existing linear frequency modulation(LFM)signal parameter estimation methods and the poor antinoise performance and estimation accuracy under a low signal-to-noise ratio(SNR),a parameter estimation method for LFM signals with a Duffing oscillator based on frequency periodicity is proposed in this paper.This method utilizes the characteristic that the output signal of the Duffing oscillator excited by the LFM signal changes periodically with frequency,and the modulation period of the LFM signal is estimated by autocorrelation processing of the output signal of the Duffing oscillator.On this basis,the corresponding relationship between the reference frequency of the frequencyaligned Duffing oscillator and the frequency range of the LFM signal is analyzed by the periodic power spectrum method,and the frequency information of the LFM signal is determined.Simulation results show that this method can achieve high-accuracy parameter estimation for LFM signals at an SNR of-25 dB.

Algorithms for automatic measurement of SIS-type hysteretic underdamped Josephson junction’s parameters by current-voltage characteristics

Some electrical parameters of the SIS-type hysteretic underdamped Josephson junction(JJ)can be measured by its current-voltage characteristics(IVCs).Currents and voltages at JJ are commensurate with the intrinsic noise level of measuring instruments.This leads to the need for multiple measurements with subsequent statistical processing.In this paper,the digital algorithms are proposed for the automatic measurement of the JJ parameters by IVC.These algorithms make it possible to implement multiple measurements and check these JJ parameters in an automatic mode with the required accuracy.The complete sufficient statistics are used to minimize the root-mean-square error of parameter measurement.A sequence of current pulses with slow rising and falling edges is used to drive JJ,and synchronous current and voltage readings at JJ are used to realize measurement algorithms.The algorithm performance is estimated through computer simulations.The significant advantage of the proposed algorithms is the independence from current source noise and intrinsic noise of current and voltage meters,as well as the simple implementation in automatic digital measuring systems.The proposed algorithms can be used to control JJ parameters during mass production of superconducting integrated circuits,which will improve the production efficiency and product quality.

An improved bidirectional generative adversarial network model for multivariate estimation of correlated and imbalanced tunnel construction parameters

Estimation of construction parameters is crucial for optimizing tunnel construction schedule.Due to the influence of routine activities and occasional risk events,these parameters are usually correlated and imbalanced.To solve this issue,an improved bidirectional generative adversarial network(BiGAN)model with a joint discriminator structure and zero-centered gradient penalty(0-GP)is proposed.In this model,in order to improve the capability of original BiGAN in learning imbalanced parameters,the joint discriminator separately discriminates the routine activities and risk event durations to balance their influence weights.Then,the self-attention mechanism is embedded so that the discriminator can pay more attention to the imbalanced parameters.Finally,the 0-GP is adapted for the loss of the discrimi-nator to improve its convergence and stability.A case study of a tunnel in China shows that the improved BiGAN can obtain parameter estimates consistent with the classical Gauss mixture model,without the need of tedious and complex correlation analysis.The proposed joint discriminator can increase the ability of BiGAN in estimating imbalanced construction parameters,and the 0-GP can ensure the stability and convergence of the model.