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中文摘要：
为了提高T—S型模糊RBF神经网络的训练效率，把Levenberg—Marquardt算法引入到T—S型模糊RBF神经网络的训练过程中，提高了网络训练的收敛速度，减小了训练过程陷入局部极小点的概率，然后基于这种算法推导出T—S型模糊RBF神经网络的快速训练算法，即混合学习算法。最后通过实验验证了这种算法的有效性和实用性。
英文摘要：
To improve the efficiency of training the T-S fuzzy model based RBF neural network, the Levenberg-Marquardt algorithm is introduced into it, which speeds up the convergence and reduces the probability for the training to get into the local minimum point. Next, a kind of more efficient algorithm, named hybrid learning algorithm, is proposed. At last, the efficiency and practicability of the Levenberg-Marquardt algorithm for the training of the T-S fuzzy model based RBF neural network are tested through an experiment.
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