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

IEEE Transactions on Neural Networks and Learning Systems
Shangce GaoJiahai Wang

Abstract

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 p...Continue Reading

Citations

Dec 4, 2019·Frontiers in Neuroscience·Ensieh IranmehrBernabe Linares-Barranco
May 15, 2020·Computational Intelligence and Neuroscience·Zhe WangYuki Todo
Sep 6, 2019·Computational Intelligence and Neuroscience·Xiaoxiao QianShangce Gao
Apr 4, 2021·Sensors·Bowen LiuZhilin Zhang
Apr 2, 2021·Computational Intelligence and Neuroscience·Lei LiYuquan Zhu
Jun 29, 2021·Frontiers in Neurorobotics·Alessandro SalatielloMartin A Giese
Oct 26, 2021·Computer Methods in Biomechanics and Biomedical Engineering·Mesut Melek, Negin Melek
Nov 13, 2021·Environmental Science and Pollution Research International·Wenjie WangZheng Miao

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