Radial basis function (RBF) network have unique advantages in control applications due to its features of simple topological structure, quick convergence speed and no local minima.
径向基函数(RBF)神经网络由于其结构简单、收敛速度快、无局部极小等特点使其在控制中的应用有着独特的优势。
People put forward radial basis function networks considering the conventional BP algorithm problems of slow convergence speed and easily getting into local dinky value.
对于传统BP算法存在的收敛速度慢和易陷入局部极小值问题，人们提出了径向基函数网络。
The weights of network adjusted by using the least mean square made fast convergence of radial basis function to obtain better PID control parameters and achieve the speed tracked control.
利用最小均方差来调整网络的权值，使径向基函数快速收敛，获得较佳pid控制参数，达到对转速跟随控制。
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