Electrical Impedance Tomography (EIT) is a non-invasive image reconstruction technique. Typically, an EIT scheme involves the solution to an inverse problem, which usually gives a poor resolution, due to linearization and ill-posedness of the problem. An alternative approach based on Artificial Neural Networks (ANN) has been used as a replacement of the inverse problem, giving correct results without linearizing the problem. However, training an ANN may be time consuming and usually requires a large amount of iterations before achieving a correct answer to the input stimulation. Several studies focused on training ANNs, and Evolutionary Algorithms (EA) gives a faster global convergence. In this paper, a novel approach based on Artificial Neural Networks and Particle Swarm Optimization (PSO) is proposed to improve the training process. A training method based on PSO algorithm achieves a faster global convergence.
CITATION STYLE
Martin, S., & Choi, C. T. M. (2015). Electrical impedance tomography: A reconstruction method based on neural networks and particle swarm optimization. In IFMBE Proceedings (Vol. 47, pp. 177–179). Springer Verlag. https://doi.org/10.1007/978-3-319-12262-5_49
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