The Experts below are selected from a list of 23829 Experts worldwide ranked by ideXlab platform
Er-wei Bai - One of the best experts on this subject based on the ideXlab platform.
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Block Oriented Nonlinear System Identification
2010Co-Authors: Fouad Giri, Er-wei BaiAbstract:Block-oriented Nonlinear System Identification deals with an area of research that has been very active since the turn of the millennium. The book makes a pedagogical and cohesive presentation of the methods developed in that time. These include: * iterative and over-parameterization techniques; * stochastic and frequency approaches; * support-vector-machine, subspace, and separable-least-squares methods; * blind Identification method; * bounded-error method; and * decoupling inputs approach. The Identification methods are presented by authors who have either invented them or contributed significantly to their development. All the important issues e.g., input design, persistent excitation, and consistency analysis, are discussed. The practical relevance of block-oriented models is illustrated through biomedical/physiological System modeling. The book will be of major interest to all those who are concerned with Nonlinear System Identification whatever their activity areas. This is particularly the case for educators in electrical, mechanical, chemical and biomedical engineering and for practising engineers in process, aeronautic, aerospace, robotics and vehicles control. Block-oriented Nonlinear System Identification serves as a reference for active researchers, newcomers, industrial and education practitioners and graduate students alike.
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An Interactive Term Approach to Non-Parametric FIR Nonlinear System Identification
IEEE Transactions on Automatic Control, 2010Co-Authors: Er-wei Bai, M. DeistlerAbstract:In this technical note, a framework for designing specially structured input sequences for non-parametric Nonlinear System Identification is presented so that interaction terms which describe interactions among variables can be identified separately. In a sense, the approach decomposes a general difficult Nonlinear Identification problem into a number of problems that are of lower orders. Corresponding Identification algorithms are proposed.
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non parametric Nonlinear System Identification an asymptotic minimum mean squared error estimator
Conference on Decision and Control, 2009Co-Authors: Er-wei BaiAbstract:This paper studies the problem of the minimum mean squared error estimator for non-parametric Nonlinear System Identification. It is shown that for a wide class of Nonlinear Systems, the local linear estimator is a linear (in outputs) asymptotic minimum mean squared error estimator. The class of the Systems allowed is characterized by a stability condition that is related to many well studied stability notions in the literature. Numerical simulations support the analytical analysis.
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A Data-Driven Orthogonal Basis Function Approach for Non-Parametric Nonlinear System Identification
IFAC Proceedings Volumes, 2009Co-Authors: Er-wei BaiAbstract:Abstract A data driven orthogonal basis function approach is proposed for non-parametric FIR Nonlinear System Identification. This eliminates the problem of blindly choosing the basis functions without a priori structural information. Extension to deterministic inputs are also presented. Further, based on the proposed basis functions, approaches are proposed for model order determination and regressor selection along with their theoretical justifications.
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Non-Parametric Nonlinear System Identification: A Data-Driven Orthogonal Basis Function Approach
IEEE Transactions on Automatic Control, 2008Co-Authors: Er-wei BaiAbstract:In this paper, a data driven orthogonal basis function approach is proposed for non-parametric FIR Nonlinear System Identification. The basis functions are not fixed a priori and match the structure of the unknown System automatically. This eliminates the problem of blindly choosing the basis functions without a priori structural information. Further, based on the proposed basis functions, approaches are proposed for model order determination and regressor selection along with their theoretical justifications.
Liu Li-qiang - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear System Identification Based On Evolution Particle Swarm Optimization
Computer Simulation, 2010Co-Authors: Liu Li-qiangAbstract:Nonlinear System Identification is one of the most important topics of modern Identification.A novel approach for complex Nonlinear System Identification is proposed based on evolution particle swarm optimization(EPSO) algorithm.In order to increase the diversity of particle,a new evolutionary strategy in the standard particle swarm optimization(PSO) algorithm is introduced.Firstly,in the iterations of algorithm optimization process,Evolution of PSO algorithm is constructed to improve the capacity of global search algorithms by controlling groups of particles in the selection,variation,such as evolutionary operation.Secondly,the problems of Nonlinear System Identification are converted to Nonlinear optimization problems in continual space,and then the EPSO algorithm is used to search the parameter concurrently and efficiently to find the optimal estimation of the System parameters.The feasibility of the proposed method is demonstrated by the Identification of a multi-input and single-output Wiener-Hammerstein model.
Jingyi Song - One of the best experts on this subject based on the ideXlab platform.
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Complex Nonlinear System Identification based on cellular particle swarm optimization
2013 IEEE International Conference on Mechatronics and Automation, 2013Co-Authors: Yuntao Dai, Liqiang Liu, Jingyi SongAbstract:Nonlinear System Identification is one of the most important topics of modern Identification. A novel approach for complex Nonlinear System Identification is proposed based on cellular automata particle swarm optimization (CAPSO) algorithm in this paper. The problems of Nonlinear System Identification are converted to Nonlinear optimization problems in continual space, and then the PSO algorithm is used to search the parameter concurrently and efficiently to find the optimal estimation of the System parameters. In order to enhance the performance of the PSO Identification, an improved PSO based on cellular automata is proposed by combining cellular automata (CA) with PSO. In the proposed CAPSO, each particle of particle swarm is considered as cellular automata, and distributes in the two-dimensional grid, and the state update of each cell is not only related to its own state and the state of neighbors, but also considers the state of the optimal cell. If the state is too close with the optimal cell, then re-update the cell state. The simulation results show the effectiveness and the feasibility of the proposed method.
Lennart Ljung - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear System Identification: A User-Oriented Road Map
IEEE Control Systems Magazine, 2019Co-Authors: Johan Schoukens, Lennart LjungAbstract:Nonlinear System Identification is an extremely broad topic, since every System that is not linear is Nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld. For this reason, the selection of topics and the organization of the discussion are strongly colored by the personal journey of the authors in this Nonlinear universe.
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Nonlinear System Identification: A User-Oriented Roadmap
arXiv: Systems and Control, 2019Co-Authors: Johan Schoukens, Lennart LjungAbstract:The goal of this article is twofold. Firstly, Nonlinear System Identification is introduced to a wide audience, guiding practicing engineers and newcomers in the field to a sound solution of their data driven modeling problems for Nonlinear dynamic Systems. In addition, the article also provides a broad perspective on the topic to researchers that are already familiar with the linear System Identification theory, showing the similarities and differences between the linear and Nonlinear problem. The reader will be referred to the existing literature for detailed mathematical explanations and formal proofs. Here the focus is on the basic philosophy, giving an intuitive understanding of the problems and the solutions, by making a guided tour along the wide range of user choices in Nonlinear System Identification. Guidelines will be given in addition to many examples, to reach that goal.
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USING MANIFOLD LEARNING FOR Nonlinear System Identification
IFAC Proceedings Volumes, 2007Co-Authors: Henrik Ohlsson, Jacob Roll, Torkel Glad, Lennart LjungAbstract:Abstract A high-dimensional regression space usually causes problems in Nonlinear System Identification. However, if the regression data are contained in (or spread tightly around) some manifold, the dimensionality can be reduced. This paper presents a use of dimension reduction techniques to compose a two-step Identification scheme suitable for high-dimensional Identification problems with manifold-valued regression data. Illustrating examples are also given.
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A general direct weight optimization framework for Nonlinear System Identification
IFAC Proceedings Volumes, 2005Co-Authors: Jacob Roll, Alexander Nazin, Lennart LjungAbstract:The direct weight optimization (DWO) approach is a method for finding optimal function estimates via convex optimization, applicable to Nonlinear System Identification. In this paper, an extended v ...
Johan Schoukens - One of the best experts on this subject based on the ideXlab platform.
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Nonlinear System Identification: A User-Oriented Road Map
IEEE Control Systems Magazine, 2019Co-Authors: Johan Schoukens, Lennart LjungAbstract:Nonlinear System Identification is an extremely broad topic, since every System that is not linear is Nonlinear. That makes it impossible to give a full overview of all aspects of the fi eld. For this reason, the selection of topics and the organization of the discussion are strongly colored by the personal journey of the authors in this Nonlinear universe.
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Nonlinear System Identification: A User-Oriented Roadmap
arXiv: Systems and Control, 2019Co-Authors: Johan Schoukens, Lennart LjungAbstract:The goal of this article is twofold. Firstly, Nonlinear System Identification is introduced to a wide audience, guiding practicing engineers and newcomers in the field to a sound solution of their data driven modeling problems for Nonlinear dynamic Systems. In addition, the article also provides a broad perspective on the topic to researchers that are already familiar with the linear System Identification theory, showing the similarities and differences between the linear and Nonlinear problem. The reader will be referred to the existing literature for detailed mathematical explanations and formal proofs. Here the focus is on the basic philosophy, giving an intuitive understanding of the problems and the solutions, by making a guided tour along the wide range of user choices in Nonlinear System Identification. Guidelines will be given in addition to many examples, to reach that goal.
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Nonlinear System Identification—Application for industrial hydro-static drive-line
Control Engineering Practice, 2016Co-Authors: Julian Stoev, Johan SchoukensAbstract:Abstract The goal of the paper is to describe the added value and complexities of Nonlinear System Identification applied to a large scale industrial test setup. The additional important insights provided by the frequency domain Nonlinear approach are significant and for such Systems the Nonlinear System Identification is important, for example to estimate the noise and non-linearities levels, which can indicate mechanical and configuration issues. It is not the goal to provide a final full-scale model, but to explore what is the applicability of the Nonlinear System Identification theories for a complex multi-physical non-academic test-case.
