Evolutionary Dendritic Neuron Model Learned by A State-of-the-art Evolutionary Learning Algorithm

Jiarui Shi, Zhenyu Lei, Houtian He, Ziqian Wang, Shangce Gao*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Recently, the role of dendrite structures in nerve calculation has attracted wide attention. As a single neuron model, Dendritic Neuron Model (DNM) is usually built to transmit information through imitating the mechanism and process network of biological nerves. The branches of a dendrite that distribute corresponding to the three coordinates are used to classify the training data based on demands. Contrarily, traditional artificial neural networks which use a couple of McCulloch and Pitts' neurons are still difficult to be understood and trained. Commonly, evolutionary computing is adopted to solve nonlinear problems. In this paper, a recently proposed spherical search algorithm (SASS) is for the first time introduced as the training algorithm for DNM. It substitutes the traditional error back propagation (BP) learning method to alleviate the local minima trapping problem. Six benchmark classification datasets are tested to verify the accuracy of the well trained neural network. Experimental results suggest that SASS performs better as a learning algorithm for DNM in terms of solution accuracy.

Original languageEnglish
Title of host publicationProceedings - 2021 6th International Conference on Computational Intelligence and Applications, ICCIA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages48-52
Number of pages5
ISBN (Electronic)9781665439336
DOIs
StatePublished - 2021
Event6th International Conference on Computational Intelligence and Applications, ICCIA 2021 - Xiamen, China
Duration: 2021/06/112021/06/13

Publication series

NameProceedings - 2021 6th International Conference on Computational Intelligence and Applications, ICCIA 2021

Conference

Conference6th International Conference on Computational Intelligence and Applications, ICCIA 2021
Country/TerritoryChina
CityXiamen
Period2021/06/112021/06/13

Keywords

  • artificial neural network
  • classification
  • computational intelligence
  • deep learning
  • spherical search algorithm

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Optimization
  • Modeling and Simulation

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