Firefly algorithm for training the radial basis function network for data classifications

Ming Huwi Horng, Ming Chi Lee, Ren Jean Liou

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

The radial basis function (RBF) network had been widely used in pattern classification and model predictions. In literature, several algorithms had been developed to train the RBF network such as gradient descent (GD) algorithm, particle swarm optimization (PSO) algorithm, artificial bee colony algorithm (ABC) and genetic algorithm (GA). A new training algorithm of the radial basis function network for classification problems based on the firefly (FF) algorithm is proposed in this paper. In experiments, the well known classification datasets from UCI repository are extracted to evaluate the classification performance by using the firefly algorithm, and then to compare with the results by using other GD, PSO, GA and ABC algorithms. Experimental results show that the usage of the firefly algorithm is superior to those of other four methods.

原文English
頁(從 - 到)755-758
頁數4
期刊Advanced Science Letters
11
發行號1
DOIs
出版狀態Published - 2012 5月

All Science Journal Classification (ASJC) codes

  • 一般電腦科學
  • 健康(社會科學)
  • 一般數學
  • 教育
  • 一般環境科學
  • 一般工程
  • 一般能源

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