Partially connected feedforward neural networks structured by input types

Sanggil Kang, Can Isik

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


This paper proposes a new method to model partially connected feedforward neural networks (PCFNNs) from the identified input type (IT) which refers to whether each input is coupled with or uncoupled from other inputs in generating output. The identification is done by analyzing input sensitivity changes as amplifying the magnitude of inputs. The sensitivity changes of the uncoupled inputs are not correlated with the variation on any other input, while those of the coupled inputs are correlated with the variation on any one of the coupled inputs. According to the identified ITs, a PCFNN can be structured. Each uncoupled input does not share the neurons in the hidden layer with other inputs in order to contribute to output in an independent manner, while the coupled inputs share the neurons with one another. After deriving the mathematical input sensitivity analysis for each IT, several experiments, as well as a real example (blood pressure (BP) estimation), are described to demonstrate how well our method works.

Original languageEnglish (US)
Pages (from-to)175-184
Number of pages10
JournalIEEE Transactions on Neural Networks
Issue number1
StatePublished - Jan 2005


  • Fully connected neural network (FCNN)
  • Input sensitivity
  • Input type (IT)
  • Partially connected neural network (PCNN)

ASJC Scopus subject areas

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


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