针对反馈控制系统优化算法和填充函数法等算法的早熟收敛问题,提出了基于多激励函数神经元网络的智能控制优化算法。该算法结合了神经元网络、智能控制系统和填充函数法。该算法在搜索过程中不但可以在线实时地修正PID等参数,而且不容易陷入局部最优点,从而得到的全局最优值更加精确。 Aiming at the premature convergence problem of algorithms such as feedback control system optimization algorithm and filling function method, an intelligent control optimization algorithm based on multi-excitation neural network is proposed. The algorithm combines neural network, intelligent control system and fill function method. This algorithm not only can correct parameters such as PID in real time during the search process, but also can not easily fall into the local optimal point, so that the global optimal value obtained is more accurate.