The Experts below are selected from a list of 159930 Experts worldwide ranked by ideXlab platform
Feng Ding - One of the best experts on this subject based on the ideXlab platform.
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Decomposition- and Gradient-Based Iterative Identification Algorithms for Multivariable Systems Using the Multi-innovation Theory
Circuits Systems and Signal Processing, 2019Co-Authors: Lijuan Wan, Feng DingAbstract:This paper is concerned with the Identification problem for multivariable equation-error systems with autoregressive moving average noise using the hierarchical Identification principle and the multi-innovation Identification theory. We propose a hierarchical gradient-based iterative (HGI) Identification Algorithm and give a gradient-based iterative (GI) Identification Algorithm for comparison. Meanwhile, the multi-innovation theory is used to derive the hierarchical multi-innovation gradient-based iterative (HMIGI) Identification Algorithm. The analysis shows that the HGI Algorithm has smaller computational burden and can give more accurate parameter estimates than the GI Algorithm and the HMIGI Algorithm can track time-varying parameters. Finally, a simulation example is provided to verify the effectiveness of the proposed Algorithms.
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modeling a nonlinear process using the exponential autoregressive time series model
Nonlinear Dynamics, 2019Co-Authors: Feng Ding, Erfu YangAbstract:The parameter estimation methods for the nonlinear exponential autoregressive (ExpAR) model are investigated in this work. Combining the hierarchical Identification principle with the negative gradient search, we derive a hierarchical stochastic gradient Algorithm. Inspired by the multi-innovation Identification theory, we develop a hierarchical-based multi-innovation Identification Algorithm for the ExpAR model. Introducing two forgetting factors, a variant of the hierarchical-based multi-innovation Identification Algorithm is proposed. Moreover, to compare and demonstrate the serviceability of these Algorithms, a nonlinear ExpAR process is taken as an example in the simulation.
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maximum likelihood recursive extended least squares estimation for multivariate equation error moving average systems
International Conference on Modelling Identification and Control, 2018Co-Authors: Lijuan Liu, Feng DingAbstract:In this paper, the parameter Identification of the multivariate equation-error moving average system is studied. The system is decomposed into several subsystems and a maximum likelihood recursive extended least squares Identification Algorithm is presented for estimating the parameter vector in each subsystem. The numerical simulation results indicate that the maximum likelihood recursive extended least squares Identification Algorithm is effective and get more accurate parameter estimates.
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decomposition based least squares iterative Identification Algorithm for multivariate pseudo linear arma systems using the data filtering
Journal of The Franklin Institute-engineering and Applied Mathematics, 2017Co-Authors: Feng Ding, Ling Xu, Feifei Wang, Minghu WuAbstract:Abstract This paper develops a decomposition based least squares iterative Identification Algorithm for multivariate pseudo-linear autoregressive moving average systems using the data filtering. The key is to apply the data filtering technique to transform the original system to a hierarchical Identification model, and to decompose this model into three subsystems and to identify each subsystem, respectively. Compared with the least squares based iterative Algorithm, the proposed Algorithm requires less computational efforts. The simulation results show that the proposed Algorithms can work well.
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hierarchical estimation Algorithms for multivariable systems using measurement information
Information Sciences, 2014Co-Authors: Feng DingAbstract:Abstract With the development of industry information technology, many modelling methods have been focusing on the estimation problems of multivariable systems, especially for the multivariable systems with output error autoregressive noises, from input–output measurement information. Since such a system includes both a parameter vector and a parameter matrix, the conventional methods cannot be applied to parameter estimation and modelling. In order to solve this difficulty, a hierarchical least squares based iterative Identification Algorithm and a hierarchical generalized least squares Identification Algorithm are proposed. The basic idea is to decompose the system into two fictitious subsystems, to estimate the parameters of each subsystem, and to coordinate the associated items between the two subsystems. The simulation results indicate that the proposed Algorithm is effective.
Karl Mechtler - One of the best experts on this subject based on the ideXlab platform.
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MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra
2016Co-Authors: Viktoria Dorfer, Peter Pichler, Thomas Stranzl, Johannes Stadlmann, Thomas Taus, Stephan Winkler, Karl MechtlerAbstract:*S Supporting Information ABSTRACT: Today’s highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide Identification Algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring Algorithm particularly suitable for high mass accuracy. Our Algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The Algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence Identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available a
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MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra
Journal of proteome research, 2014Co-Authors: Viktoria Dorfer, Peter Pichler, Thomas Stranzl, Johannes Stadlmann, Thomas Taus, Stephan M. Winkler, Karl MechtlerAbstract:Today’s highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide Identification Algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring Algorithm particularly suitable for high mass accuracy. Our Algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The Algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence Identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at http://ms.imp.ac.at/?goto=msamanda, is provided free of charge both as standalone version for i...
Mostafa A. Rushdi - One of the best experts on this subject based on the ideXlab platform.
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Adaptive Flight Path Control of Airborne Wind Energy Systems
Energies, 2020Co-Authors: Tarek N. Dief, Uwe Fechner, Roland Schmehl, Shigeo Yoshida, Mostafa A. RushdiAbstract:In this paper, we applied a system Identification Algorithm and an adaptive controller to a simple kite system model to simulate crosswind flight maneuvers for airborne wind energy harvesting. The purpose of the system Identification Algorithm was to handle uncertainties related to a fluctuating wind speed and shape deformations of the tethered membrane wing. Using a pole placement controller, we determined the required locations of the closed-loop poles and enforced them by adapting the control gains in real time. We compared the path-following performance of the proposed approach with a classical proportional-integral-derivative (PID) controller using the same system model. The capability of the system Identification Algorithm to recognize sudden changes in the dynamic model or the wind conditions, and the ability of the controller to stabilize the system in the presence of such changes were confirmed. Furthermore, the system Identification Algorithm was used to determine the parameters of a kite with variable-length tether on the basis of data that were recorded during a physical flight test of a 20 kW kite power system. The system Identification Algorithm was executed in real time, and significant changes were observed in the parameters of the dynamic model, which strongly affect the resulting response.
Viktoria Dorfer - One of the best experts on this subject based on the ideXlab platform.
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MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra
2016Co-Authors: Viktoria Dorfer, Peter Pichler, Thomas Stranzl, Johannes Stadlmann, Thomas Taus, Stephan Winkler, Karl MechtlerAbstract:*S Supporting Information ABSTRACT: Today’s highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide Identification Algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring Algorithm particularly suitable for high mass accuracy. Our Algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The Algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence Identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available a
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MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra
Journal of proteome research, 2014Co-Authors: Viktoria Dorfer, Peter Pichler, Thomas Stranzl, Johannes Stadlmann, Thomas Taus, Stephan M. Winkler, Karl MechtlerAbstract:Today’s highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide Identification Algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring Algorithm particularly suitable for high mass accuracy. Our Algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The Algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence Identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at http://ms.imp.ac.at/?goto=msamanda, is provided free of charge both as standalone version for i...
D Q Wang - One of the best experts on this subject based on the ideXlab platform.
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brief paper lleast squares based recursive and iterative estimation for output error moving average systems using data filtering
Iet Control Theory and Applications, 2011Co-Authors: D Q WangAbstract:For parameter estimation of output error moving average (OEMA) systems, this study combines the auxiliary model Identification idea with the filtering theory, transforms an OEMA system into two Identification models and presents a filtering and auxiliary model-based recursive least squares (F-AM-RLS) Identification Algorithm. Compared with the auxiliary model-based recursive extended least squares Algorithm, the proposed F-AM-RLS Algorithm has a high computational efficiency. Moreover, a filtering and auxiliary model-based least squares iterative (F-AM-LSI) Identification Algorithm is derived for OEMA systems with finite measurement input-output data. Compared with the F-AM-RLS approach, the proposed F-AM-LSI Algorithm updates the parameter estimation using all the available data at each iteration, and thus can generate highly accurate parameter estimates.
