In this paper, a tensegrity structure (TS) design is formulated as a nonlinear programming (NLP) problem, and a winner-take-all artificial emotional neural network (WTA-ENN) is proposed to solve the resulting NLP. The...
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ISBN:
(纸本)9781509006212
In this paper, a tensegrity structure (TS) design is formulated as a nonlinear programming (NLP) problem, and a winner-take-all artificial emotional neural network (WTA-ENN) is proposed to solve the resulting NLP. The main feature of proposed WTA-ENN is related to low number of learning weights and simplicity of its learning rules that make it a suitable model for complicated TS design problems. Numerical results indicate that WTA-ENN can effectively solve NLP problem obtained from basic module of a typical TS Tower. The proposed method can be effectively used in architectural, structural and robotics design.
Analytic Hierarchy Process is one of the most known multicriteria decision aid methods. Nevertheless, as it relies on decision makers (DM) pairwise comparisons, a problem may occur if some comparisons are not well don...
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Analytic Hierarchy Process is one of the most known multicriteria decision aid methods. Nevertheless, as it relies on decision makers (DM) pairwise comparisons, a problem may occur if some comparisons are not well done. This issue, known as inconsistency, appears when an inconsistency threshold is violated. One way to deal with inconsistency is to redo all judgments, as many times as needed, in order to reach acceptable levels. This work proposes a nonlinear programming model that reduces inconsistency to zero or near zero, without needing to redo all judgments. The reduction is achieved by adjusting the original judgments in a minimum way, keeping the DM's decisions within a tolerable range. Only discrete values are generated, so the solution respects the limits of the Saaty scale (1-9). To illustrate the efficiency of the nonlinear model, a comparison between the proposed model and other models taken from recent literature was made. The results show that the proposed model performed better, since the original judgments were changed in a minimum way, also the inconsistency was completely removed. Alternatively, if some inconsistency is allowed more original judgments can be preserved.
NASA Technical Reports Server (Ntrs) 19990063726: Structural Optimization for Reliability Using nonlinear Goal programming by NASA Technical Reports Server (Ntrs); published by
NASA Technical Reports Server (Ntrs) 19990063726: Structural Optimization for Reliability Using nonlinear Goal programming by NASA Technical Reports Server (Ntrs); published by
A model is presented that utilizes nonlinear binary mathematical programming and the predictive crash methodology found in the AASHTO Highway Safety Manual to prioritize the selection of intersection safety projects. ...
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A model is presented that utilizes nonlinear binary mathematical programming and the predictive crash methodology found in the AASHTO Highway Safety Manual to prioritize the selection of intersection safety projects. This research improves upon methods employed by prioritization tools such as the Interactive Highway Data Safety Design Model (IHSDM) and AASHTO Safety Analyst by the ability to implement multiple countermeasures at individual intersections. This expansion is advantageous in that it that it allows greater flexibility in determining mitigation strategies. For real life applications, implementation of multiple countermeasures can also decrease overall project expense by reducing construction mobilization and oversight costs. A sample set of intersections is analyzed using the nonlinear binary method and results are compared to a similar model utilizing a single countermeasure or a single set of predetermined countermeasures.
We explore properties of nonlinear programming problems (NLPs) that arise in the formulation of NMPC subproblems and show their influence on stability and robustness of NMPC. NLPs that satisfy linear independence cons...
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We explore properties of nonlinear programming problems (NLPs) that arise in the formulation of NMPC subproblems and show their influence on stability and robustness of NMPC. NLPs that satisfy linear independence constraint qualification (LICQ), second order sufficient conditions (SOSC) and strict complementarity (SC), have solutions that are continuous and differentiable with perturbations of the problem data. As a result, they are important prerequisites for nominal and ISS stability of NMPC controllers. Moreover, we show that ensuring these properties is possible through reformulation of the NLP subproblem for NMPC, through the addition of (1 penalty and barrier terms. We show how these properties also establish ISS of related sensitivity-based NMPC controllers, such as asNMPC and amsNMPC. Finally, we demonstrate the impact of our reformulated NLPs on several examples that have shown nonrobust performance on earlier NMPC strategies. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Applying fuzzy principles and approaches to the queue systems will provide more extensive and realistic applications of them. A queue system of M/M/c is studied based on fuzzy approach in this paper. This system was p...
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This paper presents an exact penalty method for solving optimization problems with very general constraints covering, in particular, nonlinear programming (NLP), semidefinite programming (SDP), and second-order cone p...
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This paper presents an exact penalty method for solving optimization problems with very general constraints covering, in particular, nonlinear programming (NLP), semidefinite programming (SDP), and second-order cone programming (SOCP). The algorithm is called the sequential linear cone method (SLCM) because for SDP and SOCP the main cost of computation amounts to solving at each iteration a linear cone program for which efficient solvers are available. Restricted to NLP, SLCM is exactly a sequential quadratic program method. Under two basic conditions which concern only the data, it is proved that the sequence of iterates is bounded. Furthermore, in particular, when the feasible set is nonempty, under two additional constraint qualification conditions, it is proved that the cluster points are stationary points. In that case, it is established also that the sequence of penalty parameters eventually stays constant, and for a particular class of data it is proved that a unit step length can be obtained.
The optimal solution, as well as the objective of stochastic programming problems vary with the underlying probability measure. This paper addresses stability with respect to the underlying probability measure and sta...
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The optimal solution, as well as the objective of stochastic programming problems vary with the underlying probability measure. This paper addresses stability with respect to the underlying probability measure and stability of the objective. The techniques presented are employed to make problems numerically tractable, which are formulated by involving numerous scenarios, or even by involving a continuous probability measure. The results justify clustering techniques, which significantly reduce computation times while guaranteeing a desired approximation quality. The second part of the paper highlights Newton's method to solve the reduced stochastic recourse problems. The techniques presented exploit the particular structure of the recourse function of the stochastic optimization problem. The tools are finally demonstrated on a benchmark problem, which is taken from electrical power flows. (C) 2016 Elsevier B.V. All rights reserved.
In this paper, we consider a nonlinear programming problem for which the constraint set may be infeasible. We propose an algorithm based on a large family of augmented Lagrangian functions and analyze its global conve...
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In this paper, we consider a nonlinear programming problem for which the constraint set may be infeasible. We propose an algorithm based on a large family of augmented Lagrangian functions and analyze its global convergence properties taking into account the possible infeasibility of the problem. We show that, in a finite number of iterations, the algorithm stops detecting the infeasibility of the problem or finds an approximate feasible/optimal solution with any required precision. We illustrate, by means of numerical experiments, that our algorithm is reliable for different Lagrangian/penalty functions proposed in the literature.
An attempt is made to model a gas lift allocation problem as a nonlinear optimization form with a wide range of constraints covering deficiencies carried out by past studies. In this article, a rigorous nonlinear prog...
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An attempt is made to model a gas lift allocation problem as a nonlinear optimization form with a wide range of constraints covering deficiencies carried out by past studies. In this article, a rigorous nonlinear programming approach is used to maximize daily cash flow of a group of production wells under gas lift operation. First, an appropriate model is prepared for gas lift performance curve of each well by use of nonlinear logarithmic and polynomial regression. Afterward, a model is constructed and solved for daily cash flow under capacity and pressure constraints. Results show a significant increase in cash flow for an optimized case compared with the current gas allocation plan. Moreover, sensitivity analysis was performed for different variables showing that oil price and compressing cost must be considered in long-term gas lift allocation optimization.
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