Search results for "Mathematical optimization"
showing 10 items of 1300 documents
An improved sampling strategy based on trajectory design for application of the Morris method to systems with many input factors
2012
[EN] In this paper, a revised version of the Morris approach, which includes an improved sampling strategy based on trajectory design, has been adapted to the screening of the most influential parameters of a fuzzy controller applied to WWTPs. Due to the high number of parameters, a systematic approach has been proposed to apply this improved sampling strategy with low computational demand. In order to find out the proper repetition number of elementary effects of each input factor on model output (EEi) calculations, an iterative and automatic procedure has been applied. The results show that the sampling strategy has a significant effect on the parameter significance ranking and that rando…
An Analysis of Bilevel Linear Programming Solving Parameters Based on Factoraggregation Approach
2013
We introduce the notion of factoraggregation,which is a special construction of general aggregation operators, and apply it for an analysis of optimal solution parameters for bilevel linear programming problems. The aggregation observes lower level objective functions considering the classes of equivalence generated by an objective function on the upper level. The proposed method is illustrated with numerical and graphical examples.
Fast Convergence of Neural Networks by Application of a New Min-Max Algorithm
1992
Abstract The paper presents a new application of the min-max method, an original algorithm previously successfully applied in other areas and based on a combination of the quasi-Newton and steepest descent methods in order to find the weights minimising the error function of a feed forward neural networks. Preliminary results, obtained by applying the proposed method to a simple 2-2-1 architecture on small Boolean learning problems, are very promising.
A New Min-Max Optimisation Approach for Fast Learning Convergence of Feed-Forward Neural Networks
1993
One of the most critical aspect for a wide use of neural networks to real world problems is related to the learning process which is known to be computational expensive and time consuming.
Approximation of the Feasible Parameter Set in worst-case identification of Hammerstein models
2005
The estimation of the Feasible Parameter Set (FPS) for Hammerstein models in a worst-case setting is considered. A bounding procedure is determined both for polytopic and ellipsoidic uncertainties. It consists in the projection of the FPS of the extended parameter vector onto suitable subspaces and in the solution of convex optimization problems which provide Uncertainties Intervals of the model parameters. The bounds obtained are tighter than in the previous approaches. hes.
Conflict resolution in the multi-stakeholder stepped spillway design under uncertainty by machine learning techniques
2021
Abstract The optimal spillway design is of great significance since these structures can reduce erosion downstream of the dams. This study proposes a risk-based optimization framework for a stepped spillway to achieve an economical design scenario with the minimum loss in hydraulic performance. Accordingly, the stepped spillway was simulated in the FLOW-3D® model, and the validated model was repeatedly performed for various geometric states. The results were used to form a Multilayer Perceptron artificial neural network (MLP-ANN) surrogate model. Then, a risk-based optimization model was formed by coupling the MLP-ANN and NSGA-II. The concept of conditional value at risk (CVaR) was utilized…
A Novel Mathematical Model For TLCD: Theoretical And Experimental Investigations
2014
In this paper, a novel mathematical model for the Tuned Liquid Column Damper (TLCD) is presented. Taking advantages of fractional derivatives and related concepts, a new equation of motion of the liquid inside the TLCD is obtained. Experimental laboratory tests have been performed in order to validate the proposed linear fractional formulation. Comparison among experimental results, numerical obtained using the classical formulation and numerical with the new linear fractional formulation are reported. Results in frequency domain show how the new linear fractional formulation can predict the real behavior of such a passive vibration control system, more correctly than the classical mathemat…
Passivity-based output feedback control of Markovian jump systems with discrete and distributed time-varying delays
2013
In this article, we present a new method in designing mode-dependent passivity-based output feedback controllers for Markovian jump systems with time-varying delays. Both discrete and distributed delays are considered in the model. A Lyapunov–Krasovskii function is constructed to establish new required sufficient conditions for ensuring exponentially mean-square stability and the passivity criteria, simultaneously. The method produces linear matrix inequality formulation that allows obtaining controller gains based on a convex optimisation method. Finally, a numerical example is given to illustrate the effectiveness of our approach.
A passivity approach to control of Markovian jump systems with mixed time-varying delays
2013
This paper investigated the problem of control design for a class of stochastic systems with Markovian jump parameters and time-varying delays. For the model under consideration, a passivity-based approach is introduced for designing mode-dependent output feedback controllers with mixed discrete and distributed delays. A Lypunov-Krasovskii function (LKF) is defined to establish new required sufficient conditions for ensuring exponentially mean-square stability and the passivity criteria, simultaneously. Moreover, controller gains are calculated based on a convex optimization method by solving a Linear Matrix Inequality (LMI). Finally, simulation results are provided to illustrate the effect…
Delay-Range-Dependent Linear Matrix Inequality Approach to Quantized H∞ Control of Linear Systems with Network-Induced Delays and Norm-Bounded Uncert…
2010
This paper deals with a convex optimization approach to the problem of robust network-based H∞ control for linear systems connected over a common digital communication network with static quantizers. Both the polytopic and the norm-bounded uncertainties are taken into consideration separately. First, the effect of both the output quantization levels and the network conditions under static quantizers is investigated. Second, by introducing a descriptor technique, using a Lyapunov—Krasovskii functional and a suitable change of variables, new required sufficient conditions are established in terms of delay-range-dependent linear matrix inequalities for the existence of the desired network-bas…