Neurodynamical optimization

Li-Zhi Liao*, Houduo Qi, Liqun Qi

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

62 Citations (Scopus)
22 Downloads (Pure)


Dynamical (or ode) system and neural network approaches for optimization have been co-existed for two decades. The main feature of the two approaches is that a continuous path starting from the initial point can be generated and eventually the path will converge to the solution. This feature is quite different from conventional optimization methods where a sequence of points, or a discrete path, is generated. Even dynamical system and neural network approaches share many common features and structures, yet a complete comparison for the two approaches has not been available. In this paper, based on a detailed study on the two approaches, a new approach, termed neurodynamical approach, is introduced. The new neurodynamical approach combines the attractive features in both dynamical (or ode) system and neural network approaches. In addition, the new approach suggests a systematic procedure and framework on how to construct a neurodynamical system for both unconstrained and constrained problems. In analyzing the stability issues of the underlying dynamical (or ode) system, the neurodynamical approach adopts a new strategy, which avoids the Lyapunov function. Under the framework of this neurodynamical approach, strong theoretical results as well as promising numerical results are obtained.

Original languageEnglish
Pages (from-to)175-195
Number of pages21
JournalJournal of Global Optimization
Issue number2
Publication statusPublished - Feb 2004

Scopus Subject Areas

  • Computer Science Applications
  • Management Science and Operations Research
  • Control and Optimization
  • Applied Mathematics

User-Defined Keywords

  • Dynamical system
  • Neural network
  • Neurodynamical
  • Ode system
  • Optimization


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