Analysis of time-varying cellular neural networks for quadratic global optimization

M. Gilli, P. P. Civalleri, T. Roska, L. O. Chua

Research output: Contribution to journalArticle

3 Citations (Scopus)


The algorithm for quadratic global optimization performed by a cellular neural network (CNN) with a slowly varying slope of the output characteristic (see References 1 and 2) is analysed. It is shown that the only CNN which finds the global minimum of a quadratic function for any values of the input parameters is the network composed by only two cells. If the dimension is higher than two, even the CNN described by the simplest one-dimensional space-invariant template Â=[A1,A0,A1], fails to find the global minimum in a subset of the parameter space. Extensive simulations show that the CNN described by the above three-element template works correctly within several parameter ranges; however, if the parameters are chosen according to a random algorithm, the error rate increases with the number of cells.

Original languageEnglish
Pages (from-to)109-126
Number of pages18
JournalInternational Journal of Circuit Theory and Applications
Issue number2
Publication statusPublished - Jan 1 1998


ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

Cite this