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Received:April 14, 2010 Revised:June 04, 2010
Received:April 14, 2010 Revised:June 04, 2010
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。
中文关键词: T-S模糊模型 RBF Levenberg-Marquardt算法 混合学习算法
Abstract: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.
文章编号： 中图分类号： 文献标志码：
基金项目:国家自然科学基金(No.60874096)(50704016)
| Author Name | Affiliation |
| 徐奉友 | 湖南大学 电气与信息工程学院 湖南 长沙 410082 |
| 张小刚 |
| Author Name | Affiliation |
| 徐奉友 | 湖南大学 电气与信息工程学院 湖南 长沙 410082 |
| 张小刚 |
引用文本：
徐奉友,张小刚.Levenberg-Marquardt算法在T-S型模糊RBF神经网络训练中的应用.计算机系统应用,2010,19(12):155-159
.Application of Levenberg-Marquardt Algorithm to Training of T-S Fuzzy Model Based RBF Neural Network.COMPUTER SYSTEMS APPLICATIONS,2010,19(12):155-159
徐奉友,张小刚.Levenberg-Marquardt算法在T-S型模糊RBF神经网络训练中的应用.计算机系统应用,2010,19(12):155-159
.Application of Levenberg-Marquardt Algorithm to Training of T-S Fuzzy Model Based RBF Neural Network.COMPUTER SYSTEMS APPLICATIONS,2010,19(12):155-159
