Displaying similar documents to “Modeling acquaintance networks based on balance theory”

Modelling Evolution of Regulatory Networks in Artificial Bacteria

Y. Sanchez-Dehesa, D. Parsons, J. M. Peña, G. Beslon (2008)

Mathematical Modelling of Natural Phenomena

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Studying the evolutive and adaptative machanisms of prokayotes is a complicated task. As these machanisms cannot be easily studied "in vivo", it is necessary to consider other methods. We have therefore developed the RAevol model, a model designed to study the evolution of bacteria and their adaptationto the environment. Our model simulates the evolution of a population of artificial bacteria in a changing environment, providing us with an insight into the strategies that digital organisms...

A mathematical model for file fragment diffusion and a neural predictor to manage priority queues over BitTorrent

Christian Napoli, Giuseppe Pappalardo, Emiliano Tramontana (2016)

International Journal of Applied Mathematics and Computer Science

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BitTorrent splits the files that are shared on a P2P network into fragments and then spreads these by giving the highest priority to the rarest fragment. We propose a mathematical model that takes into account several factors such as the peer distance, communication delays, and file fragment availability in a future period also by using a neural network module designed to model the behaviour of the peers. The ensemble comprising the proposed mathematical model and a neural network provides...

Using normal mode analysis in teaching mathematical modeling to biology students

D. A. Kondrashov (2011)

Mathematical Modelling of Natural Phenomena

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Linear oscillators are used for modeling a diverse array of natural systems, for instance acoustics, materials science, and chemical spectroscopy. In this paper I describe simple models of structural interactions in biological molecules, known as elastic network models, as a useful topic for undergraduate biology instruction in mathematical modeling. These models use coupled linear oscillators to model the fluctuations of molecular structures around the equilibrium state. I present many...

Systemic risk through contagion in a core-periphery structured banking network

Oliver Kley, Claudia Klüppelberg, Lukas Reichel (2015)

Banach Center Publications

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We contribute to the understanding of how systemic risk arises in a network of credit-interlinked agents. Motivated by empirical studies we formulate a network model which, despite its simplicity, depicts the nature of interbank markets better than a symmetric model. The components of a vector Ornstein-Uhlenbeck process living on the nodes of the network describe the financial robustnesses of the agents. For this system, we prove a LLN for growing network size leading to a propagation...

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....

Generalizations of the noisy-or model

Jiří Vomlel (2015)

Kybernetika

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In this paper, we generalize the noisy-or model. The generalizations are three-fold. First, we allow parents to be multivalued ordinal variables. Second, parents can have both positive and negative influences on their common child. Third, we describe how the suggested generalization can be extended to multivalued child variables. The major advantage of our generalizations is that they require only one parameter per parent. We suggest a model learning method and report results of experiments...