Intelligent Dendritic Neural Model for Classification Problems

Abstract

In recent years, the dendritic neural model has been widely employed in various fields because of its simple structure and inexpensive cost. Traditional numerical optimization is ineffective for the parameter optimization problem of the dendritic neural model; it is easy to fall into local in the optimization process, resulting in poor performance of the model. This paper proposes an intelligent dendritic neural model firstly, which uses the intelligent optimization algorithm to optimize the model instead of the traditional dendritic neural model with a backpropagation algorithm. The experiment compares the performance of ten representative intelligent optimization algorithms in six classification datasets. The optimal combination of user-defined parameters for the model evaluates by using Taguchi’s method, systemically. The results show that the performance of an intelligent dendritic neural model is significantly better than a traditional dendritic neural model. The intelligent dendritic neural model has small classification errors and high accuracy, which provides an effective approach for the application of dendritic neural model in engineering classification problems. In addition, among ten intelligent optimization algorithms, an evolutionary algorithm called biogeographic optimization algorithm has excellent performance, and can quickly obtain high-quality solutions and excellent convergence speed.

Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning

“…erefore, this paper combines metaheuristic algorithm and dendrite neuron model effectively, and DNM + BBO is established in this study, especially as a powerful algorithm to solve classification and optimization problems. In the future, we will further explore and try to expand the output range of DNM, apply it to a wider range of fields to solve other different problems, and continue to pursue improving its performance [64].…”