The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Wusheng Lu - One of the best experts on this subject based on the ideXlab platform.
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roundoff noise minimization for 2 d separable denominator digital filters using jointly optimal high order error feedback and realization
International Symposium on Circuits and Systems, 2017Co-Authors: Takao Hinamoto, Wusheng LuAbstract:The joint Optimization Problem of high-order error feedback and realization for minimizing roundoff noise at the filter output subject to-scaling constraints for two-dimensional (2-D) separable-denominator digital filters is investigated. Linear algebraic techniques that convert the Problem at hand into an Unconstrained Optimization Problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the Unconstrained Optimization Problem iteratively. Closed-form formulas for fast and accurate gradient evaluation are derived. A numerical example is presented to demonstrate the validity and effectiveness of the proposed technique.
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jointly optimal error feedforward high order error feedback and realization for roundoff noise minimization in iir digital filters
International Symposium on Circuits and Systems, 2014Co-Authors: Takao Hinamoto, Wusheng LuAbstract:Joint Optimization of error feedforward, high-order error feedback and state-space realization for minimizing roundoff noise at filter output subject to l 2 -scaling constraints for state-space digital filters is investigated. Linear algebraic techniques that convert the Problems at hand into an Unconstrained Optimization Problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the Unconstrained Optimization Problem iteratively. In this connection, closed-form formulas are derived for fast and accurate gradient evaluation. Finally, a numerical example is presented to demonstrate that the high-order error feedback does offer much improved performance and that the proposed joint Optimization is superior relative to a sequentially optimized system where the state-space coordinate transformation as well as error feedforward and high-order error feedback matrices are optimized separately.
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jointly optimal high order error feedback and realization for roundoff noise minimization in 1 d and 2 d state space digital filters
IEEE Transactions on Signal Processing, 2013Co-Authors: Takao Hinamoto, Wusheng LuAbstract:Joint Optimization of high-order error feedback and state-space realization for minimizing roundoff noise at filter output subject to l2-scaling constraints is investigated for one- and two-dimensional state-space digital filters. Linear algebraic techniques that convert the Problems at hand into an Unconstrained Optimization Problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the Unconstrained Optimization Problem iteratively. In this connection, closed-form formulas are derived for fast and accurate gradient evaluation. Finally, case studies are presented to demonstrate that the high-order error feedback does offer much improved performance and that the proposed joint Optimization is superior relative to a sequentially optimized system where the state-space coordinate transformation and high-order error feedback matrices are optimized separately.
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jointly optimal high order error feedback and realization for roundoff noise minimization in 2 d state space digital filters
European Conference on Circuit Theory and Design, 2013Co-Authors: Takao Hinamoto, Wusheng LuAbstract:The joint Optimization Problem of high-order error feedback and realization for minimizing roundoff noise at filter output subject to l2-scaling constraints is investigated for two-dimensional (2-D) state-space digital filters. Linear algebraic techniques that convert the Problem at hand into an Unconstrained Optimization Problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the Unconstrained Optimization Problem iteratively. In this connection, closed-form formulas are derived for fast and accurate gradient evaluation. Finally a numerical example is presented to illustrate the validity and effectiveness of the proposed algorithm.
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joint Optimization of high order error feedback and realization for roundoff noise minimization in the fornasini marchesini second model
International Midwest Symposium on Circuits and Systems, 2012Co-Authors: Takao Hinamoto, Wusheng LuAbstract:For two-dimensional (2-D) state-space digital filters described by the Fornasini-Marchesini second local state-space model, the joint Optimization of high-order error feedback and realization for minimizing roundoff noise at filter output subject to l 2 -scaling constraints is investigated. We present linear-algebraic techniques that convert the Problem at hand into an Unconstrained Optimization Problem, and present an efficient quasi-Newton algorithm to solve the Unconstrained Optimization Problem iteratively, in which closed-form formulas are derived for fast and accurate gradient evaluation. A numerical example is presented to illustrate the utility and effectiveness of the proposed algorithm.
Takao Hinamoto - One of the best experts on this subject based on the ideXlab platform.
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roundoff noise minimization for 2 d separable denominator digital filters using jointly optimal high order error feedback and realization
International Symposium on Circuits and Systems, 2017Co-Authors: Takao Hinamoto, Wusheng LuAbstract:The joint Optimization Problem of high-order error feedback and realization for minimizing roundoff noise at the filter output subject to-scaling constraints for two-dimensional (2-D) separable-denominator digital filters is investigated. Linear algebraic techniques that convert the Problem at hand into an Unconstrained Optimization Problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the Unconstrained Optimization Problem iteratively. Closed-form formulas for fast and accurate gradient evaluation are derived. A numerical example is presented to demonstrate the validity and effectiveness of the proposed technique.
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jointly optimal error feedforward high order error feedback and realization for roundoff noise minimization in iir digital filters
International Symposium on Circuits and Systems, 2014Co-Authors: Takao Hinamoto, Wusheng LuAbstract:Joint Optimization of error feedforward, high-order error feedback and state-space realization for minimizing roundoff noise at filter output subject to l 2 -scaling constraints for state-space digital filters is investigated. Linear algebraic techniques that convert the Problems at hand into an Unconstrained Optimization Problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the Unconstrained Optimization Problem iteratively. In this connection, closed-form formulas are derived for fast and accurate gradient evaluation. Finally, a numerical example is presented to demonstrate that the high-order error feedback does offer much improved performance and that the proposed joint Optimization is superior relative to a sequentially optimized system where the state-space coordinate transformation as well as error feedforward and high-order error feedback matrices are optimized separately.
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jointly optimal high order error feedback and realization for roundoff noise minimization in 1 d and 2 d state space digital filters
IEEE Transactions on Signal Processing, 2013Co-Authors: Takao Hinamoto, Wusheng LuAbstract:Joint Optimization of high-order error feedback and state-space realization for minimizing roundoff noise at filter output subject to l2-scaling constraints is investigated for one- and two-dimensional state-space digital filters. Linear algebraic techniques that convert the Problems at hand into an Unconstrained Optimization Problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the Unconstrained Optimization Problem iteratively. In this connection, closed-form formulas are derived for fast and accurate gradient evaluation. Finally, case studies are presented to demonstrate that the high-order error feedback does offer much improved performance and that the proposed joint Optimization is superior relative to a sequentially optimized system where the state-space coordinate transformation and high-order error feedback matrices are optimized separately.
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jointly optimal high order error feedback and realization for roundoff noise minimization in 2 d state space digital filters
European Conference on Circuit Theory and Design, 2013Co-Authors: Takao Hinamoto, Wusheng LuAbstract:The joint Optimization Problem of high-order error feedback and realization for minimizing roundoff noise at filter output subject to l2-scaling constraints is investigated for two-dimensional (2-D) state-space digital filters. Linear algebraic techniques that convert the Problem at hand into an Unconstrained Optimization Problem are explored, and an efficient quasi-Newton algorithm is then applied to solve the Unconstrained Optimization Problem iteratively. In this connection, closed-form formulas are derived for fast and accurate gradient evaluation. Finally a numerical example is presented to illustrate the validity and effectiveness of the proposed algorithm.
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joint Optimization of high order error feedback and realization for roundoff noise minimization in the fornasini marchesini second model
International Midwest Symposium on Circuits and Systems, 2012Co-Authors: Takao Hinamoto, Wusheng LuAbstract:For two-dimensional (2-D) state-space digital filters described by the Fornasini-Marchesini second local state-space model, the joint Optimization of high-order error feedback and realization for minimizing roundoff noise at filter output subject to l 2 -scaling constraints is investigated. We present linear-algebraic techniques that convert the Problem at hand into an Unconstrained Optimization Problem, and present an efficient quasi-Newton algorithm to solve the Unconstrained Optimization Problem iteratively, in which closed-form formulas are derived for fast and accurate gradient evaluation. A numerical example is presented to illustrate the utility and effectiveness of the proposed algorithm.
Jian Chu - One of the best experts on this subject based on the ideXlab platform.
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a new algorithm for Unconstrained Optimization Problem with the form of sum of squares minimization
Systems Man and Cybernetics, 2004Co-Authors: Jian ChuAbstract:In this paper, we present a new algorithm for Unconstrained Optimization Problem with the form of sum of squares minimization that is produced in the procedure of model parameter estimation for nonlinear systems. The new algorithm is composed of conventional BFGS and analytical exact line search where the line search step is calculated by an analytical equation in which the second derivative matrix called Hessian matrix is approximated by the product of Jacobian matrices of objective function. Two case studies show that the new algorithm exhibits excellent convergence performance in terms of computation time and initial values requirement.
Zabidin Salleh - One of the best experts on this subject based on the ideXlab platform.
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an efficient modified azprp conjugate gradient method for large scale Unconstrained Optimization Problem
Journal of Mathematics, 2021Co-Authors: Ahmad Alhawarat, Thoi Trung Nguyen, Ramadan Sabra, Zabidin SallehAbstract:To find a solution of Unconstrained Optimization Problems, we normally use a conjugate gradient (CG) method since it does not cost memory or storage of second derivative like Newton’s method or Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. Recently, a new modification of Polak and Ribiere method was proposed with new restart condition to give a so-call AZPRP method. In this paper, we propose a new modification of AZPRP CG method to solve large-scale Unconstrained Optimization Problems based on a modification of restart condition. The new parameter satisfies the descent property and the global convergence analysis with the strong Wolfe-Powell line search. The numerical results prove that the new CG method is strongly aggressive compared with CG_Descent method. The comparisons are made under a set of more than 140 standard functions from the CUTEst library. The comparison includes number of iterations and CPU time.
Chao Yang - One of the best experts on this subject based on the ideXlab platform.
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pfnn a penalty free neural network method for solving a class of second order boundary value Problems on complex geometries
Journal of Computational Physics, 2021Co-Authors: Hailong Sheng, Chao YangAbstract:Abstract We present PFNN, a penalty-free neural network method, to efficiently solve a class of second-order boundary-value Problems on complex geometries. To reduce the smoothness requirement, the original Problem is reformulated to a weak form so that the evaluations of high-order derivatives are avoided. Two neural networks, rather than just one, are employed to construct the approximate solution, with one network satisfying the essential boundary conditions and the other handling the rest part of the domain. In this way, an Unconstrained Optimization Problem, instead of a constrained one, is solved without adding any penalty terms. The entanglement of the two networks is eliminated with the help of a length factor function that is scale invariant and can adapt with complex geometries. We prove the convergence of the PFNN method and conduct numerical experiments on a series of linear and nonlinear second-order boundary-value Problems to demonstrate that PFNN is superior to several existing approaches in terms of accuracy, flexibility and robustness.
