Dilation-erosion perceptrons with evolutionary learning for weather forecasting

The Dilation-erosion perceptron (DEP) is considered a good forecasting model, whose foundations are based on mathematical morphology (MM) and complete lattice theory (CLT). However, a drawback arises from the gradient estimation of morphological operators into classical gradient-based learning process, since they are not differentiable of usual way. In this sense, this work presents an evolutionary learning process, called DEP(MGA), using a modified genetic algorithm (MGA) to design the DEP model for weather forecasting. In addition, we have included an automatic phase fix procedure (APFP) into the proposed learning process to eliminate time phase distortions observed in some temporal phenomena. At the end, an experimental analysis is presented using two complex time series, where five well-known performance metrics and an evaluation function are used to assess forecasting performance.

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