Tri-state neural network and analysis of its performance

P. G. Madhavan, B. E. Stephens, W. C. Low

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


The tri-state neuron is introduced to simulate the tri-state property of neurons in the nervous system. The double sigmoid function is utilized as the activation function of the tri-state neural network. The convergence rate and the generalization ability of the tri-state network are compared with those of the traditional bi-state network with single sigmoid function. It is shown analytically and computationally that (1) the tri-state network has a faster convergence rate than the bi-state network and that (2) the generalization ability of these two networks are comparable.

Original languageEnglish (US)
Pages (from-to)235-246
Number of pages12
JournalIntelligent Automation and Soft Computing
Issue number3
StatePublished - Jan 1 1995


  • Double sigmoid
  • Fast convergence
  • Prediction
  • Tri-state neuron
  • Volterra time series

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