Study and comparison of multilayer perceptron NN and radial basis function NN in oceanographic forecasting
The research presented in this paper describes a comparison of the use of Multi-Layer Perceptron Neural Network and the Radial Basis Functions Neural Networks in predicting the physical structure of the ocean up to 20 km ahead of a sea going vessel. The data used in this experiment was acquired on board a vessel transecting the Atlantic Ocean from 50 degree(s) South (the Falkland Island) to 50 degree(s) North (the UK). The research is part of the Strategic Tactical Environmental Bubble project which aims to develop a methodology for predicting the physical structures in three dimensions, around a sea going vessel from data acquired in situ and historical records. A Sobel filter has been applied to pre- process the raw data, enhancing features and smoothing stable areas. Some characteristics of both types of Neural Networks are outlined, as well as an exhaustive comparison of both methods. The advantages and disadvantages of both to this particular problem are discussed. A final system is presented together with a critical evaluation of its performance. For this purpose a real-time simulator has been built and its results are presented here.