Dendritic Neuron Model with Effective Learning Algorithms for Classification, Approximation, and Prediction

Shangce Gao, Mengchu Zhou, Yirui Wang, Jiujun Cheng, Hanaki Yachi, Jiahai Wang

Research output: Contribution to journalArticlepeer-review

547 Scopus citations


An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi's experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.

Original languageEnglish (US)
Article number8409490
Pages (from-to)601-614
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number2
StatePublished - Feb 2019

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


  • Approximation
  • brain
  • classification
  • dendritic neuron model (DNM)
  • global learning algorithms
  • prediction


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