This paper addresses the issue of training feedforward neural networks by global optimization. The main contributions include characterization of global optimality of a network error function, and formulation of a glo...
详细信息
This paper addresses the issue of training feedforward neural networks by global optimization. The main contributions include characterization of global optimality of a network error function, and formulation of a global descent algorithm to solve the network training problem. A network with a single hidden-layer and a single-output unit is considered. By means of a monotonic transformation, a sufficient condition for global optimality of a network error function is presented. Based on this, a penalty-based algorithm is derived directing the search towards possible regions containing the global minima. Numerical comparison with benchmark problems from the neural network literature shows superiority of the proposed algorithm over some local methods, in terms of the percentage of trials attaining the desired solutions. The algorithm is also shown to be effective for several pattern recognition problems.
The aim of this brief paper is to study the output feedback, robust stabilization problem for a class of SISO interval plants with unknown constant disturbance. For interval plants of minimum phase and known high-freq...
详细信息
The aim of this brief paper is to study the output feedback, robust stabilization problem for a class of SISO interval plants with unknown constant disturbance. For interval plants of minimum phase and known high-frequency gain, it shows that a simple controller with integral action is able to reject the disturbance. A procedure to compute the parameters of the controller is discussed.
All isolated solutions of the cyclic-n polynomial equations are not known for larger dimensions than 11. We exploit two types of symmetric structures in the cyclic-n polynomial to compute all isolated nonsingular solu...
详细信息
All isolated solutions of the cyclic-n polynomial equations are not known for larger dimensions than 11. We exploit two types of symmetric structures in the cyclic-n polynomial to compute all isolated nonsingular solutions of the equations efficiently by the polyhedral homotopy continuation method and to verify the correctness of the generated approximate solutions. Numerical results on the cyclic-8 to the cyclic-12 polynomial equations, including their solution information, are given. (C) 2002 Elsevier Science B.V. All rights reserved.
This paper proposes a multi-echelon integrated just-in-time (JIT) inventory model with random delivery lead times for a serial supply chain in which members exchange information to jointly make purchase, production, a...
详细信息
This paper proposes a multi-echelon integrated just-in-time (JIT) inventory model with random delivery lead times for a serial supply chain in which members exchange information to jointly make purchase, production, and delivery decisions. The performance of the whole supply chain depends on whether or not uncertain delivery can be handled properly. In this paper, a time buffer and emergency borrowing policies are used simultaneously to deal effectively with random delivery lead times and to protect a JIT system against shortages of goods. To construct easily the proposed multi-echelon integrated JIT inventory (MEIJITI) model, a duplication methodology is created. Since optimizing the proposed model is equivalent to solving an extremely complex mixed nonlinear integer programming problem, three propositions used to reduce the solution space and simplify the solution procedure are derived. Accordingly, a proposed search method for finding the optimal solution and a simulated annealing algorithm used successfully to obtain a near-optimal solution are developed. Two phases of an experiment were conducted, and some important observations are drawn.
This paper modifies Powell's conjugate direction method for unconstrained, continuous, local optimization problems to adapt to the stochastic environment in simulation response optimization. The main idea underlyi...
详细信息
This paper modifies Powell's conjugate direction method for unconstrained, continuous, local optimization problems to adapt to the stochastic environment in simulation response optimization. The main idea underlying the proposed method is to conduct several replications at each trial point to obtain reliable estimate of the theoretical response. To avoid misjudging the real difference between two points due to the stochastic nature, a t-test of the statistical hypothesis is employed to replace the simple comparison of the mean responses. In an experimental comparison, the proposed method outperforms the Nelder-Mead simlex method, a quasi-Newton method, and several other methods in solving a stochastic Watson function with nine variables, a queueing problem with two variables, and an inventory problem with two variables. Scope and purpose In decision making there are many situations that the problem is so complicated that the conventional optimization methods are unable to apply. In this case, embedding the simulation technique with certain optimization method has been demonstrated to be very promising in solving the problem. There exist many optimization methods, of which Powell's conjugate direction method has been valued for its sound theoretical properties and the derivative-free nature in practice. The purpose of this paper is to embed Powell's method to the simulation technique to solve the unconstrained, continuous, local optimization problems in a stochastic sense. (C) 2002 Elsevier Science Ltd. All rights reserved.
A new trust-region active-set algorithm for solving minimizing a nonlinear function subject to nonlinear equality and inequality constraints is described. In this algorithm, an active set strategy is used together wit...
详细信息
A new trust-region active-set algorithm for solving minimizing a nonlinear function subject to nonlinear equality and inequality constraints is described. In this algorithm, an active set strategy is used together with a projected Hessian technique to compute the trial step. A convergence theory for this algorithm is presented. Under important assumptions, it is shown that the algorithm is globally convergent. In particular, it is shown that a subsequence of the iteration sequence is not bounded away from either Fritz-John points or KKT points. (C) 2002 Elsevier Science Inc. All rights reserved.
In a recent paper, the authors introduced a method to estimate optical parameters of thin films using transmission data. The associated model assumes that the film is deposited on a completely transparent substrate. I...
详细信息
In a recent paper, the authors introduced a method to estimate optical parameters of thin films using transmission data. The associated model assumes that the film is deposited on a completely transparent substrate. It has been observed, however, that small absorption of substrates affect in a nonnegligible way the transmitted energy. The question arises of the reliability of the estimation method to retrieve optical parameters in the presence of substrates of different thicknesses and absorption degrees. In this paper, transmission spectra of thin films deposited on non-transparent substrates are generated and, as a first approximation, the method based on transparent substrates is used to estimate the optical parameters. As expected, the method is good when the absorption of the substrate is very small, but fails when one deals with less transparent substrates. To overcome this drawback, an iterative procedure is introduced, that allows one to approximate the transmittance with transparent substrate, given the transmittance with absorbent substrate. The updated method turns out to be almost as efficient in the case of absorbent substrates as it was in the case of transparent ones. (C) 2002 Elsevier Science B.V. All rights reserved.
This paper presents a nonlinear programming model with multi-constraints of inequality to solve inverse viscoelasticity problems. By utilizing an aggregate function approach, multi-constraints are converted into a sin...
详细信息
This paper presents a nonlinear programming model with multi-constraints of inequality to solve inverse viscoelasticity problems. By utilizing an aggregate function approach, multi-constraints are converted into a single smooth constraint. The optimization with a single constraint is realized by using a technique of multiplier penalty functions, and a standard BFGS algorithm is employed in the solution process. Results with time dependent and independent noise data are given. (C) 2003 Published by Elsevier Science Ltd.
With technology scaling, the trend for high-performance integrated circuits is toward ever higher operating frequency, lower power supply voltages, and higher power dissipation. This causes a dramatic increase in the ...
详细信息
With technology scaling, the trend for high-performance integrated circuits is toward ever higher operating frequency, lower power supply voltages, and higher power dissipation. This causes a dramatic increase in the currents being delivered through the on-chip power grid and is recognized in the 2001 International Technology Roadmap for Semiconductors as one of the difficult challenges. The addition of decoupling capacitances (decaps) is arguably the most powerful degree of freedom that a designer has for power-grid noise abatement and is becoming more important as technology scales. In this paper, we propose and demonstrate an algorithm for the automated placement and sizing of decaps in application specific integrated circuit (ASIC)-like circuits. The problem is formulated as one of nonlinear optimization and is solved using a sensitivity-based quadratic programming (QP) solver. The adjoint sensitivity method is applied to calculate the first-order sensitivities. We propose a fast convolution technique based on piecewise linear, (PWL) compressions of the original and adjoint waveforms. Experimental results show that power grid noise can be significantly reduced after a judicious optimization of decap placement, with little change in the total chip area.
This paper presents an efficient enumerative approach to solve general linearly constrained optimization problems. This class of optimization problems includes fractional, nonlinear network models, quadratic, convex a...
详细信息
This paper presents an efficient enumerative approach to solve general linearly constrained optimization problems. This class of optimization problems includes fractional, nonlinear network models, quadratic, convex and non-convex programs. The unified approach is accomplished by converting the constrained optimization problem to an unconstrained optimization problem through a parametric representation of its feasible region. The proposed solution algorithm consists of three phases. In phase 1 it finds all interior critical points. In phase 2 the parametric representation of the feasible region is constructed to identify any critical points on the edges and faces of the feasible region. This is done by a modified version of an algorithm for finding the V-representation of the polyhedron. Then, in phase 3, the global optimal value of the objective function is found by evaluating the objective function at the critical points as well as at the vertices. For an illustration of the algorithm and a comparison with the existing methods small-size numerical examples are presented. (C) 2002 Elsevier Science Inc. All rights reserved.
暂无评论