This paper describes a robust glottal source estimation method based on a joint source-filter separation technique. In this method, the glottal flow derivative is modelled as the Liljencrants-Fant (LF) model and the v...
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This paper describes a robust glottal source estimation method based on a joint source-filter separation technique. In this method, the glottal flow derivative is modelled as the Liljencrants-Fant (LF) model and the vocal tract is described as a time-varying ARX model. Since the joint estimation problem is a multi-parameter nonlinear optimization procedure, we separate the optimization procedure into two passes. The first pass initializes the glottal source and vocal tract models providing robust initial parameters to the following joint optimization procedure. The joint estimation determines the accuracy of model estimation, which is implemented with a trust-region descent optimization algorithm. Experiments with synthetic and real voices show the proposed method is a robust glottal source parameter estimation method with a considerable degree of accuracy.
A discussion is made on Lagrange method for nonlinear programming problems, including unconstrained grey nonlinear programming, constrained grey nonlinear programming and nonlinear programming in a grey system. Analyt...
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A discussion is made on Lagrange method for nonlinear programming problems, including unconstrained grey nonlinear programming, constrained grey nonlinear programming and nonlinear programming in a grey system. Analytical methods are given for solving various problems of grey nonlinear programming.
Modern industrial enterprises have invested significant resources for collecting and distributing data, with the expectation that it will enhance profitability via better decision making. Due to the complexity of thes...
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Modern industrial enterprises have invested significant resources for collecting and distributing data, with the expectation that it will enhance profitability via better decision making. Due to the complexity of these problems, existing approaches tend to make convenient, but invalid assumptions so that tractable solution may be found. For example, for estimation in nonlinear dynamic systems, extended Kalman filtering (EKF) relies on Gaussian approximation and local linearization to find a closed-form solution. Moving horizon based least-squares estimation (MHE) also relies on Gaussian approximation, but the use of nonlinear models and constraints eliminates most of the computational benefits of this approximation, but can provide more accurate estimates than EKF. Unfortunately, in most practical nonlinear dynamic systems, the posterior distributions are often far from Gaussian, and continually change their shape. Our previous work has developed rigorous Bayesian methods for estimation in nonlinear dynamic systems with constraints. These methods rely on recent theoretical developments in Sequential Monte Carlo Sampling (SMC). It does not rely on assumptions about the shape of the distributions, or nature of the models. Furthermore, this approach is expected to be computationally more efficient due to its recursive formulation that does not rely on nonlinear programming. These claims have been supported via applications to relatively small scale CSTR case studies. However, there are no illustrations or theoretical proofs to indicate how SMC performs for high-dimensional systems. This paper applies our previous work on Bayesian rectification by SMC to large scale nonlinear dynamic systems and compares the computational efficiency and accuracy with MHE and EKF. The model of the selected polymerization reactor contains eight variables, exhibit significant linear and nonlinear dynamics under different operating conditions, and requires satisfaction of process constraint
The conjugate point is an important global concept in the calculus of variations and optimal control. In these extremal problems, the variable is not a vector in R-n but a function. So a simple and natural question ar...
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ISBN:
(纸本)0780370619
The conjugate point is an important global concept in the calculus of variations and optimal control. In these extremal problems, the variable is not a vector in R-n but a function. So a simple and natural question arises. Is it possible to establish a conjugate points theory for a nonlinear programming problem, Min f(x) on x is an element of R-n? This paper positively answers this question. We introduce the Jacobi equation and conjugate points for the nonlinear programming problem, and we describe necessary and sufficient optimality conditions in terms of conjugate points.
A new approach for convolutive blind source separation (BSS) using penalty functions is proposed in this paper. Motivated by nonlinear programming techniques for the constrained optimization problem, it converts the c...
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Determining the most efficient machinery system for each fanri struchjres being present nowadays would be of great importance. It is necessary to elaborate a mathematical method for planning machinery system for all p...
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Determining the most efficient machinery system for each fanri struchjres being present nowadays would be of great importance. It is necessary to elaborate a mathematical method for planning machinery system for all plant growing fanns, which encourages agricultural production and contributes to establish basic principles of sustainable farming. The determination of the sinicture and the utiiizatlon of machines in a low-utilization-cost machinery park applicable for any sized farms tums economic information available on mechanization of arable plant growing.
作者：
Teel, A.R.Popović, D.CCEC
Electrical and Computer Eng. Dept. University of California Santa Barbara CA 93106-9560 United States
This paper contains an analysis of the dynamics associated with the interconnection of a dynamical system with a discrete-time approximate nonlinear programming algorithm designed to locate an extremum on the steady-s...
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This paper contains an analysis of the dynamics associated with the interconnection of a dynamical system with a discrete-time approximate nonlinear programming algorithm designed to locate an extremum on the steady-state output map (readout map) of the dynamical system. Very few assumptions on the dynamical system, the readout map, and the nonlinear programming algorithm are imposed. Taking a nonlinear programming approach to the extremum seeking problem readily allows 1) readout maps that depend on many input parameters in a highly coupled manner, 2) nonsmooth readout maps, 3) nonexponential convergence to attractors that determine the steady-state, 4) attractors in infinite dimensions. Several simulation examples are provided to illustrate the theory and demonstrate the flexibility of the approach.
A parametric H2 optimal control problem for linear continuous time-invariant systems is discussed. The control setup allows that any matrix coefficient of a system state space description be subjected to parameter unc...
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In this paper, we propose a novel trajectory-based methodology for systematically computing multiple optimal solutions of general nonlinear programming problems. The objective functions are assumed to be twice-differe...
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ISBN:
(纸本)0780366859
In this paper, we propose a novel trajectory-based methodology for systematically computing multiple optimal solutions of general nonlinear programming problems. The objective functions are assumed to be twice-differentiable and the feasible region may be non-convex and disconnected. A theoretical foundation of the methods is made on the basis of the theory of differential topology and the qualitative theory of dynamical systems. Our proposed method begins with an arbitrary initial point and consists of two distinct main phases: Phase I systematically finds several or all of the different connected feasible regions from the initial point. Phase II then finds multiple or all of the local minima in each feasible region obtained in Phase I. A numerical example is shown to illustrate the proposed method.
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