Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Kohonen Maps
Functional Networks with Applications: A Neural-Based Paradigm
Functional Networks with Applications: A Neural-Based Paradigm
Neural Computing and Applications
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
Self-learning fuzzy controllers based on temporal backpropagation
IEEE Transactions on Neural Networks
Environmental Modelling & Software
Unsupervised adaptive neural-fuzzy inference system for solving differential equations
Applied Soft Computing
Prediction of liquefaction potential based on CPT up-sampling
Computers & Geosciences
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This article presents the application of various Artificial Intelligence techniques to images that proceed from Remote Sensing and serve to predict Prionace Glauca captures (the Prionace Glauca is a type of shark). Our data proceed from remote sensors whose spectral signature allows us to calculate products that are useful for ecological modelling. After digitally processing the Remote Sensing images, we created a database from which to extract the necessary patterns for the training of the artificial neural networks (Backpropagation network, RBF, functional separability network) and the neuro-diffuse networks (ANFIS). These data are used for the training of our system with the aforementioned algorithms. The results show that for this type of problems the generalisation capacity of the functional networks is reduced, which is probably due to the absence of a subjacent mathematical model. Finally, the implementation was carried out with a multilayer perceptron that was trained with a Backpropagation algorithm (error backpropagation); a method that is less complicated than the ANFIS and RBF networks.