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The aim of the study is research of the influence of the sampling interval on the precision of the parameter estimations of a dual-mass DC electromechanical system (DMDCEMS) via Symlet wavelet. Four options are discussed in which the same wavelet function and time of observation are used and only the sampling period is different. The system parameters are evaluated. The relative output error and the summary relative parameter error are also obtained. The results are compared and analyzed.

Leakage diagnosis model of water supply network was established by SVM. ACO is used to search and optimize the penalty coefficient c of SVM and the important parameter error E of kernel function in order to improve the speed and accuracy of SVM. The water leakage characteristic data of the water supply network with single leakage point was measured by simulation test. The sample data was verified based on ACO- SVM model, which can effectively locate the leakage point and accurately predict the leakage rate.

Convolutional Neural Networks (CNNs) are widely used in image classification tasks. To fit the application of CNNs on resource-limited embedded systems, pruning is a popular technique to reduce the complexity of the network. In this paper, the robustness of the pruned network against errors on the network parameters is examined with VGG16 as a case study. The effects of errors on the weights, bias, and batch normalization (BN) parameters are evaluated for the network with different pruning rates based on error injection experiments. The results show that in general networks with more weights pruned are more robust for a given error rate. The effect of multiple errors on bias or BN parameters is almost the same for the networks with different pruning rates that are lower than 90%. Further experiments are performed to explain the bimodal phenomenon of the network performance with errors on the parameters, to find that only errors on 6% of the parameter bits will cause large degradation of the neural network performance.

This paper discusses the influence of geometric error parameters and measuring error parameters on the positioning accuracy of an industrial robot. Firstly, the kinematic model of a 6-DOF industrial robot based on the modified Denavit-Hartenberg (D-H) parameter method was established. Secondly, the influence of the geometric error parameters on the positioning accuracy of the robot’s end-effector is analyzed by simulation experiments in detail, including the link length errors, link twist errors, link offset errors and joint angle errors. And then the influence of measuring error parameters is also discussed. The results show that under individual effect of the linear errors, the length of positioning error of the robot’s end-effector is equal to the value of the corresponding error parameter. However, the angle errors have remarkable amplification effects on the accuracy of the robot. Moreover, it should be noted that the influence of measuring error parameters on the positioning accuracy of the robot is on the same order of magnitude as that of the geometric error parameters. Therefore, the measuring errors parameters should be taken into account during kinematic calibration.

For target location behind wall, the non-line-of-sight (NLOS) propagation caused by wall effect is the main factor that influences the localization performance. Based on the signal transmitting model, the NLOS is only determined by wall thickness and dielectric constant. In this paper, we use generalized Cramer-Rao lower bound (G-CRLB) as a basic tool to analyze target location performance. We deduced the G-CRLB of target localization behind wall. The Fisher information matrix (FIM) is divided into two parts: the observation matrix and prior information matrix, and the former corresponding to the measuring error, while the latter corresponding to wall parameter error. Simulation results evaluated the effect of wall parameters and measuring error on G-CRLB qualitatively.

This paper presents an optimized predictive current control (PCC) for a Halbach array-linear linear vernier permanent magnet machine(H-LVPMM). The stability and robustness of the PCC are affected by parameter variation. Toward this, a modified proportional integral disturbance observer (PIDO) is proposed to estimate the disturbance voltage value caused by parameter error and inject the correspondent observed component into the control system. Experimental results show the validity of the introduced control scheme.

The Extended Electromotive Force(EEMF) has a high potential because that the model enables position estimation by utilizing not only electromotive force but also saliency of inductance. In particular, the EEMF consists of two components, excited by rotor speed and high-frequency signal injection. The characteristic of EEMF excited by speed has been proposed sufficiently. This paper describes analysis of robustness of parameter error in two types of Extended Electromotive Force (EEMF) exited by high frequency signal injection for position sensorless control. Finally, formula evaluation indicates that each EEMF model has different robustness of parameter error.

Static branch parameter errors existing in power system model have seriously affected the accuracy of state estimation and the practical level of other applications. In order to identify the parameter errors, the paper proposed a method using PSO to optimize and identify error parameter of power network based on local state estimation. According to make state estimation in the local area which included the suspicious branch parameters, making the suspicious branch parameters as optimization variables, and the objective function is formed to make the Weighted-Sum Squared Residual minimum. Then use the particle swarm algorithm (PSO) to optimize the objective function so as to obtain the optimal parameters of the current conditions. The effectiveness of the proposed algorithm is verified by the IEEE-118 node model and the actual system.

It was previously shown that normalized Lagrange multipliers in state estimation could be effectively used for network parameter error detection and identification. However, it was also observed that there could exist a number of “insensitive” normalized Lagrange multipliers corresponding to certain parameters which might lead to detection failure. This paper derives the relationship between Lagrange multipliers and parameter errors and explains the reasons behind detection failures. Furthermore, it facilitates the identification of insensitive normalized Lagrange multipliers. These results are then used to clarify the significance of incorporating multiple measurement scans as a way of avoiding detection failure and to derive a new approach for estimating the necessary number of scans to guarantee parameter error detection. Simulation results using the IEEE 14-bus system are presented to verify the proposed solution.

The Normalized Lagrange Multiplier (NLM) method was shown to be useful in network parameter error detection, but suffered from occasional failure in certain cases. This paper exploits the advantages of PMUs and proposes a solution to the detection failure issue. The relationship between NLMs and parameter errors is first derived then used to identify insensitive NLMs which account for the issue of missing parameter error. Subsequently, a PMU placement strategy is proposed to improve the performance of the NLM method. Simulation results in the IEEE 14-bus test system show that the increased redundancy and accuracy gained by the incorporation of PMUs significantly facilitate the detection of parameter errors which was not possible by using only SCADA measurements.

In order to improve the accuracy performance of a six degree-of-freedom (DOF) redundantly actuated parallel mechanism (RAPM), a self-calibration method based on sensor redundancy is presented. The kinematic model of the RAPM is built and its 44 identifiable parameters are determined by QR decomposition of the identification matrix. Consequently, the self-calibration algorithm is formulated and the least-squares method is used to correct kinematic parameters. A guideline for selection of measurement configurations is presented. Numerical simulations are performed to confirm the validity of the proposed calibration algorithm. Simulation results show that the average parameter error of 2.23 mm is reduced to 0.12 mm, and the position and orientation accuracy of the platform can reach 0.05 mm and 0.16 mrad after calibration.

Adaptive control has recently been extended to bilateral teleoperators to synchronize the local and remote positions and velocities. However, precise parameter estimation has not been achieved via conventional adaptations. In this paper, we propose an modified adaptive control method for teleoperation systems by introducing a new adaptive law, which uses the parameter error as new leakage terms. Consequently, we prove that the position and velocity synchronization and the parameter estimation can be achieved simultaneously. It is found that the parameter estimation convergence can help to improve the motion synchronization. Simulation results validate the feasibility and efficacy of the proposed method.

The paper proposes a robust three-view matching method to produce quasi-dense 3D points in multi-view stereo. In order to overcome the limitations of traditional wide baseline match propagation methods, the proposed method simultaneously models illumination variations and perspective distortions in images, and performs an effective affine parameter error detection and rectification mechanism in the process of match propagation. Experiments show the proposed method can effectively enhance the stability and robustness of match propagation, and achieve better performance in both accuracy and quantity of the quasi-dense 3D points.

In this paper it is proposed an approach to detect, identify and correct series and shunt branch parameter errors of a transmission line using a geometrical view. Firstly it is tested if any of the measurement equations have a gross error using the J(x) index, but with the measurement error as the objective function. Next, to conclude for transmission line i-j parameter error, the corresponding measurements associated to that line should have the Composed Normalized Error CNE i-j superior to the chosen threshold value. Also, the error will spread to the measurements of the neighborhood. The correction in the parameter is made using the corresponding measurement CNE. The proposed approach uses only one measurement snapshot. Using the geometrical view, two kinds of topological errors are analyzed: (i) very large parameter errors; (ii) short circuit of a line. To test the procedure efficiency the IEEE 39-bus network will be used.

Recently a practical and efficient off-line approach for network branch parameter (series and shunt admittances) errors detection, identification and correction was developed. In comparison with other methodologies already proposed for branch parameters validation, the novel features of this off-line approach are the identification procedure of suspicious branches and the way the augmented state-parameter estimation problem is solved. Several simulation results (with IEEE bus systems) have demonstrated the high correctness and reliability of that off-line approach to deal with single and multiple parameter errors in adjacent (those having a terminal bus in common) and non-adjacent branches. Moreover the approach was also demonstrated on tests performed on the Hydro-Quebec Trans-Energie networks. One of these tests shows that approach enables the validation of series and shunt admittances of parallel transmission lines with series compensation. However, that offline approach does not have the desired property of distinguishing between bad data (bad analog measurements) and branch parameter errors. This paper presents a simple yet effective improvement by creating new indexes that enable the distinction between bad data and branch parameter errors, even when both appear simultaneously. Simulation results have sown the effectiveness of including the new indexes in that off-line approach.

The variable precision covering rough set model is promoted by extending the range of error parameter in this paper. Some properties of the promoted variable precision covering rough set model are discussed. Furthermore, we get some meaningful conclusion by means of researching on the power effect of covering variable precision approximation operators for the promoted variable precision covering rough set model.

Mining frequent itemsets over online data streams, where the new data arrive and the old data will be removed with high speed, is a challenge for the computational complexity. Existing approximate mining algorithms suffer from explosive computational complexity when decreasing the error parameter, ∈, which is used to control the mining accuracy. We propose a new approximate mining algorithm using an approximate frequent itemset tree (abbreviated as AFI-tree), called AFI algorithm, to mine approximate frequent itemsets over online data streams. The AFI-tree based on prefix tree maintains only frequent itemsets, so the number of nodes in the tree is very small. All the infrequent child nodes of any frequent node are pruned and the maximal support of the pruned nodes is estimated to detect new frequent itemsets. In order to guarantee the mining accuracy, when the estimated maximal support of the pruned nodes is a bit more than the minimum support, their supports will be re-computed and the frequent nodes among them will be inserted into the AFI-tree. Experimental results show that the AFI algorithm consumes much less memory space than existing algorithms, and runs much faster than existing algorithms in most occasions.

This paper combines with Kafang’s engineering practice of Xinshan mining area, makes crater tests, and then determines the blasting parameters under experimental conditions. Train the key stakeholders blasting parameters both at home and abroad based on the BP artificial neural network (ANN) model. On the basis that the best charge depth is 1.09m which under the experimental conditions of blasting crater test. Conduct optimizing calculation of blasting parameters by using EasyNN-plus software. Through a comprehensive analysis of optimization ways and parameter error, recommend blasting parameters under experimental conditions: charge depth L=1.09m, the best crater radius Rj=0.77-0.79m, the best crater volume Vj=0.5-0.6m3, and explosive consumption 1.0-1.1kg/t.

A novel error parameter analysis approach for synthesis of an artificial transmission line (ATL) from the ABCD parameters of a uniform transmission line section is presented. An MMIC ATL delay line with 180deg phase shift is synthesized to demonstrate this approach. The MMIC is fabricated using a conventional GaAs process and test results are validated with those of the simulated results.

The adaptive control of feedback linearizable discrete-time systems is studied. The nonlinearities in the system are linear combinations of known nonlinear functions. Although the unknown parameters appear linearly, the convergence result obtained is not global. The maximum allowable parameter error depends on the system characteristics and on the initial system states. Simulations show that the system diverges when the initial parameter error is too large.

Some influencing factors on the parameter estimation of satellite interference location is discussed in this paper. For every factor, quantitative analysis was carried out to show the influence on the parameter estimation. The analyses concerning the parameter error are backed up with an estimation experiment using actual signals relayed by two satellites.

A new adaptation algorithm is presented for adaptive control of robot manipulators. The passivity property of the proposed algorithm is first established, then the stability is proved based on the passivity properties of the plant and those of the proposed algorithm. Because of the use of the past information and averaging effect, this algorithm gives a smoother tracking and parameter error in the presence of disturbances, and a parameter convergence under a weaker excitation condition. Computer simulations are presented to illustrate this effect. >

An identification method for dynamic models of robots that improves on the sequential estimation method of Mayeda, K. Osuka and A. Kangawa (1984) is proposed. This method has the following advantages over the sequential estimation method: the parameter error propagation is reduced, because parameters directly estimated from the data are used instead of parameters indirectly calculated; and analysis calculation for identification is reduced, because at most four degrees of freedom of the robot to be identified are considered. The method is applied to a 6 d.o.f. direct drive robot. >

This article deals with the algorithm of measurement and design of probe sensors of conductivity for determination of parameters of plasma and melt of the heat-protecting antenna material within the framework of solving the problem of maintenance of steady communication with a satellite on a flight descent segment. The problem of the measured parameter error is considered separately.

We model a load using an artificial neural network (ANN) to improve an on-wafer line-reflect-match (LRM) calibration of a vector network analyzer (VNA). The ANN is trained with measurement data obtained from a thru-reflect-line (TRL) calibration. The accuracy of the LRM calibration using the ANN-modeled load compares favorably to a benchmark multiline TRL calibration with an average worst-case scattering parameter error bound of 0.017 over a 40-GHz bandwidth.

This paper presents a new automation algorithm for parameter commissioning of AC motors, which is based upon the method using the characteristics of the controller and Walsh function analysis. By investigating the distortion of controller output resulting from parameter error and evaluating through Walsh function analysis, the proposed adaptation process can achieve the commissioning of AC motors automatically. In this paper, a waveform distortion index (WDI) according to type of AC motors and their parameters are decided. To prove the effectiveness of the proposed automatic parameter commissioning algorithm to real industrial fields experimental verifications are achieved.

This paper examines robustness issues in Model Reference Adaptive Control systems in the presence of unmodeled dynamics and output disturbances. We present an approximate technique, trend analysis, by which we can study the evolution of the parameter error trajectory under periodic excitation. This analysis provides new insights upon the size and spectral content of the excitation sufficient to guarantee local stability.

A complete procedure for generating and analysing robust model reference adaptive control schemes for multivariable plants is developed. The procedure consists of two parts: the first part involves the characterization of the integral structure of the modelled part of the plant, and the associated parametrization of the controller structure and the second part involves the development of a general robust adaptive law for adjusting the controller parameters so that the closed‐loop plant is globally stable despite the presence of unmodelled dynamics and bounded disturbances. The use of dominantly rich signals for improving convergence and the bounds for the tracking and parameter error is also analysed. Copyright © 1988 John Wiley & Sons, Ltd. 2 3 217 248 Cited By :40

Parameter-error equilibrium for MRAS identification is examined. For a particular discrete-time MRAS design, both geometric and algebraic representations for the equilibrium subspace are obtained in terms of the unknown plant parameters and the input sequence.