The Experts below are selected from a list of 416568 Experts worldwide ranked by ideXlab platform
Wang Jun - One of the best experts on this subject based on the ideXlab platform.
-
Nonlinear System Identification using Scaling Kernel Support Vector Regression
Computer Simulation, 2006Co-Authors: Wang JunAbstract:A new scaling kernel support vector regression was proposed for Nonlinear System identification problem . Using linear programming technique and scaling kernel function, the support vector regression model was obtained. The kernel function of support vector regression doesn’t need to meet Mercer condition so as to offer more flexibility for selecting support kernel in practice application. The simulation results show that the scaling kernel support vector regression method can become the powerful tool for the Nonlinear System identification.
Zhang Yuan - One of the best experts on this subject based on the ideXlab platform.
-
APPLICATION OF SUPPORT VECTOR REGRESSION TO Nonlinear System IDENTIFICATION
Information & Computation, 2003Co-Authors: Zhang YuanAbstract:This paper applies Support Vector Regression (SVR) to Nonlinear System identification problem. Using the basic idea of Gaussian SVR and e insensitive loss function, we propose a new algorithm for Nonlinear System identification and compare the Gaussian SVR with the radial basis function (RBF) network for System identification. The performance of the SVR is illustrated by a simulation example involving a benchmark Nonlinear System.
Lennart Ljung - One of the best experts on this subject based on the ideXlab platform.
-
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.
-
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.
-
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.
-
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 ...
Liu Li-qiang - One of the best experts on this subject based on the ideXlab platform.
-
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.
Woonchul Ham - One of the best experts on this subject based on the ideXlab platform.
-
adaptive fuzzy sliding mode control of Nonlinear System
IEEE Transactions on Fuzzy Systems, 1998Co-Authors: Byungkook Yoo, Woonchul HamAbstract:In this paper, the fuzzy approximator and sliding mode control (SMC) scheme are considered. We propose two methods of adaptive SMC schemes that the fuzzy logic Systems (approximators) are used to approximate the unknown System functions in designing the SMC of Nonlinear System. In the first method, a fuzzy logic System is utilized to approximate the unknown function f of the Nonlinear System x/sup n=/f(x, t)+b(x, t)u and the robust adaptive law is proposed to reduce the approximation errors between the true Nonlinear functions and fuzzy approximators. In the second method, two fuzzy logic Systems are utilized to approximate the f and b, respectively, and the control law, which is robust to approximation error is also designed. The stabilities of proposed control schemes are proved and these schemes are applied to an inverted pendulum System. The comparisons between the proposed control schemes are shown in simulations.
