CAJ | 학술논문

针对模糊神经网络控制器中很难确定一个最佳学习速率的问题，将带有动量因子的自适应学习速率 BP 算法引入模糊神经网络控制器中。采用模糊推理自适应调节学习速率，同时引入动量因子，提高系统的收敛速度，并基于 Lyapunov 定理给出了系统稳定的证明过程。针对同一数学模型，用 Matlab编程仿真3种方法的实验结果表明：优化后的模糊神经网络控制器较普通模糊神经网络控制器和模糊控制器具有更优越的控制性能。
침대모호신경망락공제기중흔난학정일개최가학습속솔적문제，장대유동량인자적자괄응학습속솔 BP 산법인입모호신경망락공제기중。채용모호추리자괄응조절학습속솔，동시인입동량인자，제고계통적수렴속도，병기우 Lyapunov 정리급출료계통은정적증명과정。침대동일수학모형，용 Matlab편정방진3충방법적실험결과표명：우화후적모호신경망락공제기교보통모호신경망락공제기화모호공제기구유경우월적공제성능。
Aiming at the difficulty in determining optimal learning rate in the fuzzy neural network controller, the adaptive learning rate’s BP algorithm with momentum factor was introduced to the fuzzy neural network controller.The method has the fuzzy inference adopted to adjust adaptive learning rate and the momentum fac-tor introduced to improve convergence speed of the system,as well as the Lyapunov principle based to provide certification process for the system stability.Regarding the same mathematical model,simulation with Matlab shows that the optimized fuzzy neural network controller outperforms both ordinary fuzzy neural network control-ler and fuzzy controller in the control performance.