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Based on differential minimax control, this paper develops a novel theoretic design to achieve chaotic synchronization of neuronet-type stochastic complex networks that are heavily influenced by noise signals. According to robust inverse optimality, the proposed method is mathematically formulated by using stochastic Lyapunov technique and the associated Hamilton-Jacobi-Isaacs (HJI) equation, which ensures that the system accomplishes the best rational synchronization in probability, meanwhile, to reduce noises to a predefined level with stability margins. To verify analytical results, a numerical example is given to show the effectiveness of the proposed technique.

Chaotic synchronization of neuronet-type stochastic complex networks using decentralized differential minimax controllers