Research on dendrites has been conducted for decades, providing valuable information for
the development of dendritic computation. Creating an ideal neuron model is crucial for …

This work proposes a decision tree (DT)-based method for initializing a dendritic neuron
model (DNM). Neural networks become larger and larger, thus consuming more and more …

As the well-known McCulloch–Pitts neuron model has long been criticized to be
oversimplified, different algebra to formulate a single neuron model has received increasing …

Because of the intrinsic complexity and chaotic nature of wind speed time series, an
appropriate model for accurately forecasting the moving tendency is required. In this paper …

To construct a strong classifier ensemble, base classifiers should be accurate and diverse.
However, there is no uniform standard for the definition and measurement of diversity. This …

The dendritic neural model (DNM) mimics the non‐linearity of synapses in the human brain
to simulate the information processing mechanisms and procedures of neurons. This …

Financial time series forecasting has been an attractive application of machine learning
techniques because an advanced forecasting method can help to accurately predict price …

The combustion behavior, kinetics, thermodynamics and gas products of fine screenings
(FS) classified from municipal solid waste (MSW) in an air atmosphere were explored by TG …

In 2020, a novel coronavirus disease became a global problem. The disease was called
COVID-19, as the first patient was diagnosed in December 2019. The disease spread …

A dendritic neuron model with adaptive synapses (DMASs) based on differential evolution
(DE) algorithm training is proposed. According to the signal transmission order, a DNM can …