Input-dependent neural network trained by real-coded genetic algorithm and its industrial applications

Steve H. Ling, Hung Fat Frank Leung, H. K. Lam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

9 Citations (Scopus)


This paper presents an input-dependent neural network (IDNN) with variable parameters. The parameters of the neurons in the hidden nodes adapt to changes of the input environment, so that different test input sets separately distributed in a large domain can be tackled after training. Effectively, there are different individual neural networks for different sets of inputs. The proposed network exhibits a better learning and generalization ability than the traditional one. An improved real-coded genetic algorithm (RCGA) Ling and Leung (Soft Comput 11(1):7-31, 2007) is proposed to train the network parameters. Industrial applications on short-term load forecasting and hand-written graffiti recognition will be presented to verify and illustrate the improvement.
Original languageEnglish
Pages (from-to)1033-1052
Number of pages20
JournalSoft Computing
Issue number11
Publication statusPublished - 1 Sept 2007


  • Hand-written recognition
  • Neural network
  • Real-coded genetic algorithm
  • Short-term load forecasting

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Software
  • Geometry and Topology


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