Theoretic design of differential minimax controllers for stochastic cellular neural networks

Ziqian Liu, Henri Schurz, Nirwan Ansari, Qunjing Wang

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


This paper presents a theoretical design of how a minimax equilibrium of differential game is achieved in stochastic cellular neural networks. In order to realize the equilibrium, two opposing players are selected for the model of stochastic cellular neural networks. One is the vector of external inputs and the other is the vector of internal noises. The design procedure follows the nonlinear H infinity optimal control methodology to accomplish the best rational stabilization in probability for stochastic cellular neural networks, and to attenuate noises to a predefined level with stability margins. Three numerical examples are given to demonstrate the effectiveness of the proposed approach.

Original languageEnglish (US)
Pages (from-to)110-117
Number of pages8
JournalNeural Networks
StatePublished - Feb 2012

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence


  • Differential minimax game
  • Hamilton-Jacobi-Bellman equation
  • Lyapunov function
  • Stochastic cellular neural networks
  • Stochastic stability


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