Hypercomplex Multilayer Perceptron for Planetary Orbits Prediction

This contribution is oriented towards proposing a novel strategy to simulate and predict planetary orbits based on exploiting the optimized performance of hypercomplex neural networks. These structures perform a learning process based on quaternion algebra thus leading to lower the number of epochs required to obtain an adequate degree of approximation. Moreover, we propose a strategy for predicting the outcome of the learning phase by instantiating suitable hypercomplex auxiliary networks to predict the trends of the main weights of the network.