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Localizing moving underwater objects using an artificial lateral line and neural networks

Boulogne, L.H. (2016) Localizing moving underwater objects using an artificial lateral line and neural networks. Bachelor's Thesis, Artificial Intelligence.

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With their lateral line, fish are able to sense water velocities. Using this organ, they can determine the location of nearby moving objects. In this research project neural networks are used to extract a source location from excitation patterns measured by fluid velocity sensors along a 1D lateral line. These sensor measurements are simulated using a theoretical model. The networks used are Echo State Networks and Multilayer Perceptrons, both with 16 and 32 input sensors. The parameter settings of the networks were not fully experimentally determined. The performance of the different networks for the chosen settings is compared. Their noise robustness with respect to the input excitation pattern amplitude divided by the standard deviation of the Gaussian distribution from which the noise was sampled is analyzed. This research shows that, for the chosen settings, Multilayer Perceptrons with 16 input sensors have the best performance on average, although the best performing individual network is an ESN with 16 input sensors with a mean error in Euclidean distance of less than 0.2 in a field of 1x2. In an x,y-plane with the x-axis parallel to the lateral line, the y-coordinate is more accurately detected by networks with 32 sensors than by networks with 16 sensors, while the x-coordinate is best detected by networks with 16 sensors. The accuracy of all networks is decreased in the corners of the field. The noise robustness of both types of networks is similar.

Item Type: Thesis (Bachelor's Thesis)
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 15 Feb 2018 08:11
Last Modified: 15 Feb 2018 08:11

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