The MPSO employs local version constriction factor method and global version inertia weight method simultaneously to achieve relatively high performance.
MPSO同时采用局部模式压缩因子方法和全局模式惯性权重方法以获得相对较高的性能。
The HPSO employs local version constriction factor method and global version inertia weight method simultaneously to achieve relatively high performance.
HPSO同时采用局部模式的压缩因子方法和全局模式的惯性权重方法以获得相对较高的性能。
RBF neural network adopts the off-line training and the on-line adaptation of weight and threshold value. In order to speed up the convergence, the grads descent method with inertia item was used.
RBF神经网络采用离线学习在线修正权值和阈值，为加快收敛速度，应用带惯性项的梯度下降法。
RBF neural network adopts the off-line training and the on-line adaptation of weight and threshold value. In order to speed up the convergence, the grads descent method with inertia item was used.
RBF神经网络采用离线学习在线修正权值和阈值，为加快收敛速度，应用带惯性项的梯度下降法。
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