Evolving Dendritic Neuron Model by Equilibrium Optimizer Algorithm

Abstract

In recent years, the role of a single dendritic neural structures with non-linear localisation in computing has attracted a lot of attention from the industry. The dendritic neuron model (DNM) is an approximate logical neuron model based on dendrites, with branches of dendrites corresponding to three distributions in coordinates.The model is trained to assort data as needed by mimicking the mechanisms of transmitting information and biological nerves. Traditionally DNM models use error back propagation (BP) to optimise local minimum problems, but also degrade their performance. We now train it using an equilibrium optimizer based on physical phenomena inspired by control volume mass balance. Experimental results due to some real-world classification problems show that the mentioned algorithm can improve the accuracy of the DNM solution.