Smooth dendrite morphological neurons.

A typical feature of hyperbox-based dendrite morphological neurons (DMN) is the generation of sharp and rough decision boundaries that inaccurately track the distribution shape of classes of patterns. This feature is because the minimum and maximum activation functions force the decision boundaries to match the faces of the hyperboxes. To improve the DMN response, we introduce a dendritic model that uses smooth maximum and minimum functions to soften the decision boundaries. The classification performance assessment is conducted on nine synthetic and 28 real-world datasets. Based on the experimental results, we demonstrate that the smooth activation functions improve the generalization capacity of DMN. The proposed approach is competitive with four machine learning techniques, namely, Multilayer Perceptron, Radial Basis Function Network, Support Vector Machine, and Nearest Neighbor algorithm. Besides, the computational complexity of DMN training is lower than MLP and SVM classifiers.