Specific features of ICs parametric optimisation problem are considered in this paper. These specific features are mainly determined by the kind of optimisation model, which consists of different types of optimisation...
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(纸本)9665532340
Specific features of ICs parametric optimisation problem are considered in this paper. These specific features are mainly determined by the kind of optimisation model, which consists of different types of optimisation variables, objective functions and functions of constraints. The detailed analysis of combination of different types of optimisation model parts allows us to choose proper optimisation method.
This paper proposes robust M-estimators of dynamic linear models with a structural break of unknown location. Rates of convergence and limiting distributions for the estimated shift point and the estimated regression ...
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This paper proposes robust M-estimators of dynamic linear models with a structural break of unknown location. Rates of convergence and limiting distributions for the estimated shift point and the estimated regression parameters are derived. The analysis is carried out in the framework of possibly dependent observations and also with trending regressors. The asymptotic distribution of the break location estimator is obtained both for fixed magnitude of shift and for shift with magnitude converging to zero as the sample size increases. The latter is essential for the derivation of feasible confidence intervals for the break location. Monte Carlo simulations illustrate the performance of asymptotic inferences in practice.
We provide a new approach to the numerical computation of moments of the exit time distribution of Markov processes. The method relies on a linear programming formulation of a process exiting from a bounded domain. Th...
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We provide a new approach to the numerical computation of moments of the exit time distribution of Markov processes. The method relies on a linear programming formulation of a process exiting from a bounded domain. The LP formulation characterizes the evolution of the process through the moments of the induced occupation measure and naturally provides upper and lower bounds for the exact values of the moments. The conditions the moments have to satisfy are derived directly from the generator of the Markov process and are not based on some approximation of the process. Excellent software is readily available because the computations involve finite dimensional linear programs.
Three optimally designed three-phase squirrel-cage induction motors are compared with an industrial (conventional) motor having the same ratings. The Hooke-Jeeves search routine is used for optimization and three obje...
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Three optimally designed three-phase squirrel-cage induction motors are compared with an industrial (conventional) motor having the same ratings. The Hooke-Jeeves search routine is used for optimization and three objective functions, namely efficiency, efficiency-cost and cost, are considered. Comparison of the optimum designs with the initial and industrial design reveals that better than the proposed performance can be obtained by a simple optimization method. It is interesting to note that the industrial design satisfies some performance of the cost optimum design, and efficiency optimum design, of course with a lower efficiency. The most desirable variation of motor performance with line voltage and output power is due to the efficiency optimum design. (C) 2000 Elsevier Science Ltd. All rights reserved.
A number of methods for multiple-objective optimization problems (MOP) give as solution to MOP the set of optimal solutions for some single-objective optimization problems associated with it. Well-known examples of th...
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A number of methods for multiple-objective optimization problems (MOP) give as solution to MOP the set of optimal solutions for some single-objective optimization problems associated with it. Well-known examples of these single-objective optimization problems are the minsum and the minmax. In this note, we propose a new parametric single-objective optimization problem associated with MOP by means of Goal Programming ideas. We show that. the minsum and minmax are particular instances, so we are somehow combining minsum and minmax by means of a parameter. Moreover, such parameter has a clear meaning in the value space. Applications of this parametric problem to classical models in Locational Analysis are discussed.
I provide a systematic treatment of the asymptotic properties of weighted M-estimators under standard stratified sampling. Simple, consistent asymptotic variance matrix estimators are proposed for a broad class of pro...
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I provide a systematic treatment of the asymptotic properties of weighted M-estimators under standard stratified sampling. Simple, consistent asymptotic variance matrix estimators are proposed for a broad class of problems. When stratification is based on exogenous variables, I show that the usual, unweighted M-estimator is more efficient than the weighted estimator under a generalized conditional information matrix equality. Hausman tests for the exogeneity of the sampling scheme, including fully robust forms, are derived.
The allocation problem for multiprocessor systems. in essence to determine which task will be executed on which processor. is NP-hard. To cope with the complexity. two heuristic schemes to allocate communicating tasks...
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The allocation problem for multiprocessor systems. in essence to determine which task will be executed on which processor. is NP-hard. To cope with the complexity. two heuristic schemes to allocate communicating tasks on a multiprocessor system are proposed. The purpose of heuristic approaches is to find acceptable solutions by some simple strategies in order to avoid spending too much time in optimizing the objective functions. The new heuristic methods presented in this paper are based on list scheduling. To build the list of tasks. some concepts such as critical job and static deadline are introduced. In the task allocation process. tasks communicating with each other are allocated to the same processor whenever possible. With this method. deadlines are easier to meet when the communication costs for tasks residing on different processors are taken into consideration.
The first part of this paper introduced a procedure for rapidly calculating optimized cutting data for all the feasible tools for a given milling operation. Having produced this list of tools with associated optimized...
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The first part of this paper introduced a procedure for rapidly calculating optimized cutting data for all the feasible tools for a given milling operation. Having produced this list of tools with associated optimized cutting conditions, the preferred tool is selected by sorting the list by a composite objective function incorporating a combination of four desirable conditions: maximum metal removal rate, maximum tool life, minimum overall cost and minimum overall cutting time. These four criteria are normalized by a constant multiplier and prioritized by user-defined weighting coefficients. The tool selection procedure is implemented in software with a graphical user interface. The system includes material data for more than 750 ferrous alloys and specifications for 35988 possible holder/insert combinations. Several examples are presented to demonstrate the capability of the system and the subtle interplay of technological constraints that makes optimized tool selection a difficult process to perform manually. This automated procedure offers consistent selection of tools with efficient cutting data that can produce considerable reductions in machining cost when compared with non-optimal solutions. This tool selection procedure is designed to select tools and associated cutting conditions for single milling operations. As many machining centres have a limited number of tool positions available for automated tool changing, it is possible that the optimal set of tools for a given component is not the set of tools that are optimal for each operation considered singly. A post-processing method is presented which rationalizes a set of tools so as to reduce the number of unique tools with the minimal decrease in performance when compared with the set of individually optimized tools.
A significant improvement in motor efficiency is expected when substituting aluminum with copper die-cast rotor in squirrel cage induction motors due to a reduction in the rotor ohmic losses. Other performance criteri...
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A significant improvement in motor efficiency is expected when substituting aluminum with copper die-cast rotor in squirrel cage induction motors due to a reduction in the rotor ohmic losses. Other performance criteria, such as starting torque, power factor, and steady-state current, mall also change, and the motor may need to be redesigned to obtain, optimal performance. This paper compares the performance of optimally designed spuirrel-cage induction motors with aluminum and copper rotor bars with equivalent specifications. The design is optimized using three objective functions.
An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteratio...
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An inside-variance estimation method (IVEM) for binary interaction parameter regression in thermodynamic models is proposed. This maximum likelihood method involves the re-computation of the variance for each iteration of the optimization procedure, automatically re-weighting the objective function. Most of the maximum likelihood approaches currently used to regress the parameters of thermodynamic models fix the variances, converting the problem into a traditional weighted least squares minimization. However, such approaches lead to residual variances (between measured and calculated values) that are inconsistent with the fixed variances and, thus, do not necessarily produce optimum parameters for prediction purposes. The new method (IVEM) substantially improves fluid phase equilibria predictions (as shown by the examples presented) by maintaining consistency between the residual variances and the variance used in the objective function. This results in better parameter estimation and to a direct measure of the uncertainty in the model prediction. (C) 2000 Elsevier Science B.V. All rights reserved.
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