针对一类非线性系统的稳定控制器设计问题, 根据广义模糊双曲正切模型的万能逼近性质, 提出一种带有可调参数的广义模糊双曲正切模型的自适应控制器设计方法. 该设计方法的优点是使得自适应律的个数不依赖于广义模糊双曲正切模型的线性基函数的输出形式, 可以有效减少在线估计的参数数目, 并且能够保证被控系统的状态一致终极有界. 最后通过数值算例表明了所提出的设计方法的有效性.
相似文献Topology optimization of mechanical structures often leads to efficient designs which resemble statically determinate structures. These economical structures are especially vulnerable to local loss of stiffness due to material failure. This paper therefore addresses local failure of continuum structures in topology optimization in order to design fail-safe structures which remain operable in a damaged state.
A simplified model for local failure in continuum structures is adopted in the robust approach. The complex phenomenon of local failure is modeled by removal of material stiffness in patches with a fixed shape. The damage scenarios are taken into account by means of a minimax formulation of the optimization problem which minimizes the worst case performance.
The detrimental influence of local failure on the nominal design is demonstrated in two representative examples: a cantilever beam optimized for minimum compliance and a compliant mechanism. The robust approach is applied successfully in the design of fail-safe alternatives for the structures in these examples.
相似文献Fuel cells, batteries, and thermochemical and other energy conversion devices involve the transport of a number of (electro-) chemical species through distinct materials so that they can meet and react at specified multi-material interfaces. Therefore, morphology or arrangement of these different materials can be critical in the performance of an energy conversion device. In this paper, we study a model problem motivated by a solar-driven thermochemical conversion device that splits water into hydrogen and oxygen. We formulate the problem as a system of coupled multi-material reaction-diffusion equations where each species diffuses selectively through a given material and where the reaction occurs at multi-material interfaces. We introduce a phase-field formulation of the optimal design problem and numerically study selected examples.
相似文献Two problems of optimum topological design of grillages are discussed: (1) the Equilibrium Linear Programming (ELP), where the analysis model is based only on equilibrium conditions and (2) the Nonlinear Program (NLP), where the ELP formulation is extended to include compatibility conditions. The structural topology is optimized by allowing elimination of elements. Three different force method formulations are presented for each of the problems. It is shown that the optimal topology for the NLP problem might correspond to a singular point in the design space. The optimal topology for the ELP problem is obtained by solving a linear program (LP).
Conditions for selecting a geometry of Multiple Optimal Topologies (MOT) are derived. The objective function for the MOT geometry is shown to be independent of the redundant forces, and some of the optimal topologies are usually statically determinate structures. In such cases the lower bound on the optimal value obtained by the ELP solution is equal to the final global optimum. Examples are given to illustrate how the optimal topology and its corresponding load path change with the geometric parameters. Design procedures that combine automated optimization and CAD techniques are most suitable for solving the presented problems.
相似文献Traditional portfolio selection (PS) models are based on the restrictive assumption that the investors have precise information necessary for decision-making. However, the information available in the financial markets is often uncertain. This uncertainty is primarily the result of unquantifiable, incomplete, imprecise, or vague information. The uncertainty associated with the returns in PS problems can be addressed using random-rough (Ra-Ro) variables. We propose a new PS model where the returns are stochastic variables with rough information. More precisely, we formulate a Ra-Ro mathematical programming model where the returns are represented by Ra-Ro variables and the expected future total return maximized against a given fractile probability level. The resulting change-constrained (CC) formulation of the PS optimization problem is a non-linear programming problem. The proposed solution method transforms the CC model in an equivalent deterministic quadratic programming problem using interval parameters based on optimistic and pessimistic trust levels. As an application of the proposed method and to show its flexibility, we consider a probability maximizing version of the PS problem where the goal is to maximize the probability that the total return is higher than a given reference value. Finally, a numerical example is provided to further elucidate how the solution method works.
相似文献Material design is a critical development area for industries dealing with lightweight construction. Trying to respond to these industrial needs topology optimization has been extended from structural optimization to the design of material microstructures to improve overall structural performance. Traditional formulations based on compliance and volume control result in stiffness-oriented optimal designs. However, strength-oriented designs are crucial in engineering practice. Topology optimization with stress control has been applied mainly to (macro) structures, but here it is applied to material microstructure design. Here, in the context of density-based topology optimization, well-established techniques and analyses are used to address known difficulties of stress control in optimization problems. A convergence analysis is performed and a density filtering technique is used to minimize the risk of results inaccuracy due to coarser finite element meshes associated with highly non-linear stress behavior. A stress-constraint relaxation technique (qp-approach) is applied to overcome the singularity phenomenon. Parallel computing is used to minimize the impact of the local nature of the stress constraints and the finite difference design sensitivities on the overall computational cost of the problem. Finally, several examples test the developed model showing its inherent difficulties.
相似文献研究以低碳为目标的集装箱拖车运输问题. 该问题需同时调度隐含的运输资源和具有双重时间窗限制的运输任务. 基于扩展的确定的活动在顶点上(DAOV) 的图建立该问题的具有双时间窗约束的混合整数非线性规划模型,设计一个基于时间窗离散化的求解算法, 并将该模型转化为纯整数线性规划模型. 实验结果表明, 所提出的方法有很好的求解速度和精度, 与给定车辆行驶速度情形的对比进一步验证了所提出模型的有效性.
相似文献In this paper, a hybrid system for wind power ramp events (WPREs) detection is proposed. The system is based on modeling the detection problem as a binary classification problem from atmospheric reanalysis data inputs. Specifically, a hybrid neuro-evolutionary algorithm is proposed, which combines artificial neural networks such as extreme learning machine (ELM), with evolutionary algorithms to optimize the trained models and carry out a feature selection on the input variables. The phenomenon under study occurs with a low probability, and for this reason the classification problem is quite unbalanced. Therefore, is necessary to resort to techniques focused on providing a balance in the classes, such as the synthetic minority over-sampling technique approach, the model applied in this work. The final model obtained is evaluated by a test set using both ELM and support vector machine algorithms, and its accuracy performance is analyzed. The proposed approach has been tested in a real problem of WPREs detection in three wind farms located in different areas of Spain, in order to see the spatial generalization of the method.
相似文献Sorptive barrier technology is a recently developed tool to separate hazardous contaminants from friendly environment. The design of sorptive barrier refers to configuring different amendments with sorptive ability of organic pollutant, which is an integer programming problem and a relatively time consuming problem as well. In this paper, sorptive barrier design is newly modeled in a biobjective optimization approach, in which the dual problem of sorptive barrier design is deduced. The objectives are to minimize the financial cost and the amount of pollutant leaking through barriers. Then an opposition-based adaptive multiobjective differential evolution algorithm (MODEA-OA) is applied to handle the proposed model. The Pareto optimal front obtained by MODEA-OA spreads accurately and evenly in all three instances tested. To select extreme optimal solutions, the original and dual sorptive barrier design problems can be solved simultaneously. This study suggests that modeling barrier design as a multiobjective optimization problem is an effective approach.
相似文献Pipeline infrastructures, carrying either gas or oil, are often affected by internal corrosion, which is a dangerous phenomenon that may cause threats to both the environment (due to potential leakages) and the human beings (due to accidents that may cause explosions in presence of gas leakages). For this reason, predictive mechanisms are needed to detect and address the corrosion phenomenon. Recently, we have seen a first attempt at leveraging Machine Learning (ML) techniques in this field thanks to their high ability in modeling highly complex phenomena. In order to rely on these techniques, we need a set of data, representing factors influencing the corrosion in a given pipeline, together with their related supervised information, measuring the corrosion level along the considered infrastructure profile. Unfortunately, it is not always possible to access supervised information for a given pipeline since measuring the corrosion is a costly and time-consuming operation. In this paper, we will address the problem of devising a ML-based predictive model for internal corrosion under the assumption that supervised information is unavailable for the pipeline of interest, while it is available for some other pipelines that can be leveraged through Transfer Learning (TL) to build the predictive model itself. We will cover all the methodological steps from data set creation to the usage of TL. The whole methodology will be experimentally validated on a set of real-world pipelines.
相似文献In real-time situations such as airports, railway stations, and shopping complexes, etc. people walk in a group, and such a group of walking persons termed as multi-gait (MG). In these situations, occlusion is a serious issue that affects gait recognition performance. This issue of occlusion of body regions affects the extraction of gait features for the correct recognition of an object. The objective of this article is to reconstruct occluded regions at the preprocessing stage, which can be used for human recognition in the MG scenario. The article is divided into two folds. Firstly, we segment five regions of interest such as ankle, knee, wrist, elbow, and shoulder. We propose a particle swarm optimization (PSO) based neural network (NN) called hybrid NN to solve this problem. The performance of the proposed model is validated on our constructed dataset (SMVDU-MG), considering two view directions i.e. lateral (left to right) and oblique (left to right diagonal). Experimental results show that the proposed model gives better performance compared to an artificial neural network and alternating least square (ALS) method based on mean square error (MSE) and mean absolute percentage error (MAPE) as a performance measure function.
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