id: 06091887
dt: j
an: 06091887
au: Fan, Jianchao; Han, Min
ti: Model-structure analysis of dynamic neural networks with particle-swarm
    optimization.
so: Control Theory Appl. 28, No. 9, 1075-1081 (2011).
py: 2011
pu: South China University of Technology, Guangzhou
la: ZH
cc: 
ut: particle-swarm optimization; dynamic neural networks; robust uncertainty;
    stability
ci: 
li: 
ab: Summary: Due to the random uncertainty in the particle-swarm optimization
    algorithm (PSO), the analysis of stability is difficult to be
    performed. Most of the researchers on PSO solve this problem based on
    the practical model obtained by experience. Being different, we employ
    the robust uncertainty theory to decompose the original algorithm into
    the time-invariant part and the uncertain time-variant part for
    reducing the original fixed constraints on parameters, and perform the
    asymptotic stability analysis by using the PSO algorithm with dynamic
    inertia weight. By using the Lyapunov method, we obtain the sufficient
    conditions of stability for the dynamic neural networks based on PSO
    and the upper and lower bounds of the parameters to be adjusted,
    providing the theoretical basis for parameter selection. Finally,
    simulation examples validate the stability conditions and the
    effectiveness of the proposed dynamic neural networks based on PSO
    algorithm.
rv: