System oriented neural networks - Problem formulation, methodology and application

Kang Li, Jian Xun Peng

Research output: Contribution to journalArticle

14 Citations (Scopus)


A novel methodology is proposed for the development of neural network models for complex engineering systems exhibiting nonlinearity. This method performs neural network modeling by first establishing some fundamental nonlinear functions from a priori engineering knowledge, which are then constructed and coded into appropriate chromosome representations. Given a suitable fitness function, using evolutionary approaches such as genetic algorithms, a population of chromosomes evolves for a certain number of generations to finally produce a neural network model best fitting the system data. The objective is to improve the transparency of the neural networks, i.e. to produce physically meaningful
Original languageEnglish
Pages (from-to)143-158
Number of pages16
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number2
Publication statusPublished - Mar 2006

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition

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