Displaying similar documents to “A comparative and experimental study on gradient and genetic optimization algorithms for parameter identification of linear MIMO models of a drilling vessel”

Nonlinear system identification using heterogeneous multiple models

Rodolfo Orjuela, Benoît Marx, José Ragot, Didier Maquin (2013)

International Journal of Applied Mathematics and Computer Science

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Multiple models are recognised by their abilities to accurately describe nonlinear dynamic behaviours of a wide variety of nonlinear systems with a tractable model in control engineering problems. Multiple models are built by the interpolation of a set of submodels according to a particular aggregation mechanism, with the heterogeneous multiple model being of particular interest. This multiple model is characterized by the use of heterogeneous submodels in the sense that their state...

Numerical analysis of nonlinear model of excited carrier decay

Natalija Tumanova, Raimondas Čiegis, Mečislavas Meilūnas (2013)

Open Mathematics

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This paper presents a mathematical model for photo-excited carrier decay in a semiconductor. Due to the carrier trapping states and recombination centers in the bandgap, the carrier decay process is defined by the system of nonlinear differential equations. The system of nonlinear ordinary differential equations is approximated by linearized backward Euler scheme. Some a priori estimates of the discrete solution are obtained and the convergence of the linearized backward Euler method...

Advances in parallel heterogeneous genetic algorithms for continuous optimization

Enrique Alba, Francisco Luna, Antonio Nebro (2004)

International Journal of Applied Mathematics and Computer Science

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In this paper we address an extension of a very efficient genetic algorithm (GA) known as Hy3, a physical parallelization of the gradual distributed real-coded GA (GD-RCGA). This search model relies on a set of eight subpopulations residing in a cube topology having two faces for promoting exploration and exploitation. The resulting technique has been shown to yield very accurate results in continuous optimization by using crossover operators tuned to explore and exploit the solutions...

Efficient nonlinear predictive control based on structured neural models

Maciej Ławryńczuk (2009)

International Journal of Applied Mathematics and Computer Science

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This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction...

Maxwell-Kelvin model for highloft materials

Přívratská, Jana, Jirsák, Oldřich, Bharanitharan, R.

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Compression behaviour and elastic recovery of highloft materials are described by the Maxwell-Kelvin rheological model. We present an algorithm how to determine input parameters for this rheological model using experimental data.

The nonlinear complementarity model of industrial symbiosis network equilibrium problem

Shiqin Xu, Guoshan Liu, Wendai Lv, Yingmei Liu (2014)

RAIRO - Operations Research - Recherche Opérationnelle

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In this paper, we propose an industrial symbiosis network equilibrium model by using nonlinear complementarity theory. The industrial symbiosis network consists of industrial producers, industrial consumers, industrial decomposers and demand markets, which imitates natural ecosystem by means of exchanging by-products and recycling useful materials exacted from wastes. The industrial producers and industrial consumers are assumed to be concerned with maximization of economic profits as...

Adaptive modeling of reliability properties for control and supervision purposes

Kai-Uwe Dettmann, Dirk Söffker (2011)

International Journal of Applied Mathematics and Computer Science

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Modeling of reliability characteristics typically assumes that components and systems fail if a certain individual damage level is exceeded. Every (mechanical) system damage increases irreversibly due to employed loading and (mechanical) stress, respectively. The main issue of damage estimation is adequate determination of the actual state-of-damage. Several mathematical modeling approaches are known in the literature, focusing on the task of how loading effects damage progression (e.g.,...

Nonlinear predictive control based on neural multi-models

Maciej Ławryńczuk, Piotr Tatjewski (2010)

International Journal of Applied Mathematics and Computer Science

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This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated....

On finding optimal parameters of an oscillatory model of handwriting

Gaëtan André, Frédéric Messine (2014)

RAIRO - Operations Research - Recherche Opérationnelle

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In this paper, we show how optimization methods can be used efficiently to determine the parameters of an oscillatory model of handwriting. Because these methods have to be used in real-time applications, this involves that the optimization problems must be rapidely solved. Hence, we developed an original heuristic algorithm, named FHA. This code was validated by comparing it (accuracy/CPU-times) with a multistart method based on Trust Region Reflective algorithm.