The Experts below are selected from a list of 114 Experts worldwide ranked by ideXlab platform
S. A. Billings - One of the best experts on this subject based on the ideXlab platform.
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on line identification of nonlinear systems using volterra Polynomial Basis Function neural networks
Neural Networks, 1998Co-Authors: V. Kadirkamanathan, S. A. BillingsAbstract:An on-line identification scheme using Volterra Polynomial Basis Function (VPBF) neural networks is considered for nonlinear control systems. This comprises a structure selection procedure and a recursive weight learning algorithm. The orthogonal least-squares algorithm is introduced for off-line structure selection and the growing network technique is used for on-line structure selection. An on-line recursive weight learning algorithm is developed to adjust the weights so that the identified model can adapt to variations of the characteristics and operating points in nonlinear systems. The convergence of both the weights and the estimation errors is established using a Lyapunov technique. The identification procedure is illustrated using simulated examples.
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On-line identification of nonlinear systems using volterra Polynomial Basis Function neural networks
1997 European Control Conference (ECC), 1997Co-Authors: V. Kadirkamanathan, S. A. BillingsAbstract:An on-line identification scheme using Volterra Polynomial Basis Function (VPBF) neural networks is considered for nonlinear control systems. This comprises of a structure selection procedure and a recursive weight learning algorithm. The orthogonal least squares algorithm is introduced for off-line structure selection and the growing network technique is used for on-line structure selection. An on-line recursive weight learning algorithm is developed to adjust the weights so that the identified model can adapt to variations of the characteristics and operating points in nonlinear systems. The convergence of both the weights and estimation errors is established using a Lyapunov technique. The identification procedure is illustrated using a simulated example.
Mingren Chen - One of the best experts on this subject based on the ideXlab platform.
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solving backward heat conduction problems using a novel space time radial Polynomial Basis Function collocation method
Applied Sciences, 2020Co-Authors: Chengyu Ku, Jingen Xiao, Mingren ChenAbstract:The novel space–time radial Polynomial Basis Function collocation method is first proposed for solving the backward heat conduction problems in this study. The proposed method can be applied to inverse problems with remarkably high accuracy; even severely ill–posed inverse problems under large noises are considered.
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Solving Backward Heat Conduction Problems Using a Novel Space–Time Radial Polynomial Basis Function Collocation Method
Applied Sciences, 2020Co-Authors: Chengyu Ku, Jingen Xiao, Mingren ChenAbstract:The novel space–time radial Polynomial Basis Function collocation method is first proposed for solving the backward heat conduction problems in this study. The proposed method can be applied to inverse problems with remarkably high accuracy; even severely ill–posed inverse problems under large noises are considered.
V. Kadirkamanathan - One of the best experts on this subject based on the ideXlab platform.
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on line identification of nonlinear systems using volterra Polynomial Basis Function neural networks
Neural Networks, 1998Co-Authors: V. Kadirkamanathan, S. A. BillingsAbstract:An on-line identification scheme using Volterra Polynomial Basis Function (VPBF) neural networks is considered for nonlinear control systems. This comprises a structure selection procedure and a recursive weight learning algorithm. The orthogonal least-squares algorithm is introduced for off-line structure selection and the growing network technique is used for on-line structure selection. An on-line recursive weight learning algorithm is developed to adjust the weights so that the identified model can adapt to variations of the characteristics and operating points in nonlinear systems. The convergence of both the weights and the estimation errors is established using a Lyapunov technique. The identification procedure is illustrated using simulated examples.
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On-line identification of nonlinear systems using volterra Polynomial Basis Function neural networks
1997 European Control Conference (ECC), 1997Co-Authors: V. Kadirkamanathan, S. A. BillingsAbstract:An on-line identification scheme using Volterra Polynomial Basis Function (VPBF) neural networks is considered for nonlinear control systems. This comprises of a structure selection procedure and a recursive weight learning algorithm. The orthogonal least squares algorithm is introduced for off-line structure selection and the growing network technique is used for on-line structure selection. An on-line recursive weight learning algorithm is developed to adjust the weights so that the identified model can adapt to variations of the characteristics and operating points in nonlinear systems. The convergence of both the weights and estimation errors is established using a Lyapunov technique. The identification procedure is illustrated using a simulated example.
Chengyu Ku - One of the best experts on this subject based on the ideXlab platform.
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solving backward heat conduction problems using a novel space time radial Polynomial Basis Function collocation method
Applied Sciences, 2020Co-Authors: Chengyu Ku, Jingen Xiao, Mingren ChenAbstract:The novel space–time radial Polynomial Basis Function collocation method is first proposed for solving the backward heat conduction problems in this study. The proposed method can be applied to inverse problems with remarkably high accuracy; even severely ill–posed inverse problems under large noises are considered.
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Solving Backward Heat Conduction Problems Using a Novel Space–Time Radial Polynomial Basis Function Collocation Method
Applied Sciences, 2020Co-Authors: Chengyu Ku, Jingen Xiao, Mingren ChenAbstract:The novel space–time radial Polynomial Basis Function collocation method is first proposed for solving the backward heat conduction problems in this study. The proposed method can be applied to inverse problems with remarkably high accuracy; even severely ill–posed inverse problems under large noises are considered.
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A Novel Meshfree Approach with a Radial Polynomial for Solving Nonhomogeneous Partial Differential Equations
Mathematics, 2020Co-Authors: Chengyu Ku, Jingen XiaoAbstract:In this article, a novel radial–based meshfree approach for solving nonhomogeneous partial differential equations is proposed. Stemming from the radial Basis Function collocation method, the novel meshfree approach is formulated by incorporating the radial Polynomial as the Basis Function. The solution of the nonhomogeneous partial differential equation is therefore approximated by the discretization of the governing equation using the radial Polynomial Basis Function. To avoid the singularity, the minimum order of the radial Polynomial Basis Function must be greater than two for the second order partial differential equations. Since the radial Polynomial Basis Function is a non–singular series Function, accurate numerical solutions may be obtained by increasing the terms of the radial Polynomial. In addition, the shape parameter in the radial Basis Function collocation method is no longer required in the proposed method. Several numerical implementations, including homogeneous and nonhomogeneous Laplace and modified Helmholtz equations, are conducted. The results illustrate that the proposed approach may obtain highly accurate solutions with the use of higher order radial Polynomial terms. Finally, compared with the radial Basis Function collocation method, the proposed approach may produce more accurate solutions than the other.
V. Kadirkamanathan - One of the best experts on this subject based on the ideXlab platform.
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Multiobjective criteria for neural network structure selection and identification of nonlinear systems using genetic algorithms
IEE Proceedings - Control Theory and Applications, 1999Co-Authors: V. KadirkamanathanAbstract:An approach to model selection and identification of nonlinear systems via neural networks and genetic algorithms is presented based on multiobjective performance criteria. It considers three performance indices or cost Functions as the objectives, which are the Euclidean distance (L/sub 2/-norm) and maximum difference (L/spl infin/-norm) measurements between the real nonlinear system and the nonlinear model, and the complexity measurement of the nonlinear model, instead of a single performance index. An algorithm based on the method of inequalities, least squares and genetic algorithms is developed for optimising over the multiobjective criteria. Genetic algorithms are also used for model selection in which the structure of the neural networks is determined. The Volterra Polynomial Basis Function network and the Gaussian radial Basis Function network are applied to the identification of a liquid-level nonlinear system.
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Learning with multi-objective criteria
1995 Fourth International Conference on Artificial Neural Networks, 1995Co-Authors: V. KadirkamanathanAbstract:The paper presents a new algorithm for learning with neural networks based on multi objective performance criteria. It considers three performance indices (or cost Functions) as the objectives, which are the Euclidean distance and maximum difference measurements between the real nonlinear system and the nonlinear model (L/sub 2/, L/sub /spl infin// norms), and the complexity measure of the nonlinear model, instead of a single performance index. An algorithm based on the method of inequalities, least squares and genetic algorithms is developed for optimising over the multi objective criteria. Genetic algorithms are also used simultaneously for model selection in which the structure of the neural networks are determined. The Volterra Polynomial Basis Function network and the Gaussian radial Basis Function network are applied to the identification of a liquid level nonlinear system.