Periodic solutions for systems of coupled nonlinear Schrödinger equations (CNLS) are established by the Hirota bilinear method and elliptic functions. The interesting feature is the choice of theta functions in t...
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Periodic solutions for systems of coupled nonlinear Schrödinger equations (CNLS) are established by the Hirota bilinear method and elliptic functions. The interesting feature is the choice of theta functions in the formulation. The sum of moduli of the components or the total intensity of the beam in physical terms, will now be a rational function, instead of a polynomial, of elliptic functions. Each component of the CNLS may have multiple peaks within one period.
The structural design of a curing drum for a rubber flat-belt press is discussed in this article. On the basis of the optimal target with minimal materials its production, a mathematical model for the optimal design o...
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The structural design of a curing drum for a rubber flat-belt press is discussed in this article. On the basis of the optimal target with minimal materials its production, a mathematical model for the optimal design of a curing drum was established. The model satisfies the structural requirements and also fulfills the requirements of curing technology for rubber products. The sequential unconstrained minimization technique (SUMT) was applied to the optimal design of a curing drum. The curing drum is used is a China-made Phi700 x 1250 flat-belt press. The result shows that the optimal design can save from 13.64% to 24.7% of materials compared to the usual designs.
The available computer power recently increased so fast that problems that previously can not be handled are now solvable by straight numerical methods. The research attempts to solve control design problems without t...
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The available computer power recently increased so fast that problems that previously can not be handled are now solvable by straight numerical methods. The research attempts to solve control design problems without the added design expertise. The paper overviews some of the optimization algorithms that can be used in practical applications. First it shows how to solve a problem with the application of nonlinear optimization algorithms, then a simulation is presented to demonstrate how the different design objectives effects the systems behavior. The effect of parameter change is also examined.
This paper presents a method to determine the optimal operational parameters of a combined cycle power plant. The proposed method combines a dynamic simulation with an optimization calculation based on the nonlinear p...
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This paper presents a method to determine the optimal operational parameters of a combined cycle power plant. The proposed method combines a dynamic simulation with an optimization calculation based on the nonlinear programming method. The reason for the optimization of the plant start-up scheduling is being able to reduce the start-up time by keeping the thermal stresses in the thick parts of the heat recovery steam generator and in the steam turbine under their allowable values.
Comments on a study conducted by Hua Wei, Hiroshi Sasaki, et al. which presented a step toward the solution of large scale hydrothermal optimal power flow problems based on interior point nonlinear programming. Argume...
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Comments on a study conducted by Hua Wei, Hiroshi Sasaki, et al. which presented a step toward the solution of large scale hydrothermal optimal power flow problems based on interior point nonlinear programming. Arguments on the reduced equation of the solution; Contention on the mismatches of the equation; Response of the study's authors to the commentary.
A nonlinear optimization-based identification procedure for fully parameterized multivariable state-space models is presented. The method can be used to identify linear time-invariant, linear parameter-varying, compos...
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A nonlinear optimization-based identification procedure for fully parameterized multivariable state-space models is presented. The method can be used to identify linear time-invariant, linear parameter-varying, composite local linear, bilinear. Hammerstein and Wiener systems. The nonuniqueness of the full parameterization is dealt with by a projected gradient search to solve the nonlinear optimization problem. Both white and nonwhite measurement noise at the output can be dealt with in a maximum likelihood setting. It is proposed to use subspace identification methods to initialize the nonlinear optimization problem. A computationally efficient and numerically reliable implementation of the procedure is discussed in detail.
This article considers a class of continuous-variable optimization problems in which the objective function cannot be computed exactly but must instead be estimated by using, typically, a simulation. As the simulation...
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This article considers a class of continuous-variable optimization problems in which the objective function cannot be computed exactly but must instead be estimated by using, typically, a simulation. As the simulation output is stochastic, iterative optimization algorithms for these problems are often augmented with noise-removal features to ensure convergence to an optimal solution. Two broad noise-removal approaches are considered: stepsize-control, which involves a decreasing stepsize, or sampling-control, which involves an increasing sample size. Of these two, stepsize-control predates sampling-control by several decades and is the most commonly used approach, although, as we show, sampling-control has some advantages over stepsize-control. Our particular focus is on optimization problems for which direct gradient estimates are not available, and that instead must be approximated using estimates of the objective function. The classic Kiefer-Wolfowitz algorithm, using stepsize-control, is one such algorithm that estimates a divided difference approximation of the gradient. This article presents a sampling-controlled version of this algorithm that also uses divided difference estimates and has the benefit of being easily parallelizable. A convergence proof and some simulation results are also included. Note that one of the advantages of the sampling-controlled method is that it is better suited to estimators that have small-sample bias but are asymptotically unbiased: because the sampling rate increases, estimator bias is gradually decreased as the number of samples increases.
The paper discusses an optimal control problem, where both the time and the energy are to be minimized with the prescribed weights. Utilizing minimum principle the possible values of optimal control are derived in an ...
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The paper discusses an optimal control problem, where both the time and the energy are to be minimized with the prescribed weights. Utilizing minimum principle the possible values of optimal control are derived in an analytic form. To obtain the complete solution, the constrained mathematical programming problem is formulated and solved. There are finally discussed some numerical calculations to show an interesting dependence of the obtained results on a choice of weight parameters.
This work is a modified version of an earlier work that was based on ellipsoidal type feasible sets. Unlike the earlier work, polyhedral types of invariant and feasible sets are adopted to deal with input constraints....
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This work is a modified version of an earlier work that was based on ellipsoidal type feasible sets. Unlike the earlier work, polyhedral types of invariant and feasible sets are adopted to deal with input constraints. The use of polyhedral sets enables the formulation of on-line algorithm in terms of QP (Quadratic programming), which can be solved more efficiently than semi-definite algorithms. A simple numerical example shows that the proposed method yields larger stabilizable sets with greater bounds on disturbances than is the case in the earlier approach.
Traditional portfolio theory assumes that the return rate of portfolio follows normality. However, this assumption is not true when derivative assets are incorporated. In this paper a portfolio selection model is deve...
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Traditional portfolio theory assumes that the return rate of portfolio follows normality. However, this assumption is not true when derivative assets are incorporated. In this paper a portfolio selection model is developed based on utility function which can capture asymmetries in random variable distributions. Other realistic conditions are also considered, such as liabilities and integer decision variables. Since the resulting model is a complex mixed integer nonlinear programming problem, simulated annealing algorithm is applied for its solution. A numerical example is given and sensitivity analysis is conducted for the model.
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