16 September 1992 Neural network for ocean bottom parameter estimation
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Proceedings Volume 1700, Automatic Object Recognition II; (1992); doi: 10.1117/12.138306
Event: Aerospace Sensing, 1992, Orlando, FL, United States
This concerns the estimation of physical parameters characterizing the ocean bottom from parallel time series representing (A) the instantaneous height of the water column above a section of bottom and (B) the vertical displacement of the bottom section from its long term average. Time series A, representing wind-generated waves, is modeled by a sinusoid with phase jitter. In the absence of both seismic background noise and any nonlinear behavior in the ocean bottom, time series B could be modeled by coupling the bottom through a spring and dashpot to a mass proportional to A. We created two- and three-layer adaptive networks in which series A and B (with lag) were inputs. Training consisted in subtracting the network output from the current value of series B and feeding back these errors in accordance with the appropriate formulas for gradient descent in squared error. The trained nets act as models of the way in which the bottom responds to changes at the surface.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert H. Baran, Hanseok Ko, "Neural network for ocean bottom parameter estimation", Proc. SPIE 1700, Automatic Object Recognition II, (16 September 1992); doi: 10.1117/12.138306; https://doi.org/10.1117/12.138306

Digital filtering

Neural networks

Object recognition

Linear filtering

Autoregressive models

Nonlinear filtering

Differential equations

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