A neural network model for currency arbitrage detection

Authors:
Zheng Zhang
Affiliations:
College of Mathematics and Information, China West Normal University, Nanchong, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Year:
2012

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Abstract

The currency arbitrage detection is to find a proper currency conversion sequence that can make the most currency arbitrage. In this paper, the currency arbitrage detection is described as a energy function. And then a Lotka-Volterra (LV) recurrent neural network (RNN) is proposed to obtain the minimum points of the energy function. Simulations demonstrate that the proposed LV RNN is a practical and effective model for the currency arbitrage detection.