| 引用本文: | 王寅，荣冈.一种基于神经网络的自组织模糊系统[J].控制理论与应用,2000,17(3):455~457.[点击复制] |
| Wang Yin and Rong Gang.A Self-Organizing Neural-Network-Based Fuzzy System[J].Control Theory and Technology,2000,17(3):455~457.[点击复制] |
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| 一种基于神经网络的自组织模糊系统 |
| A Self-Organizing Neural-Network-Based Fuzzy System |
| 摘要点击 824 全文点击 526 投稿时间：1996-11-11 修订日期：1997-09-29 |
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| DOI编号 |
| 2000,17(3):455-457 |
| 中文关键词 模糊逻辑 神经元网络 模糊聚类学习方法 梯度下降法 BP学习算法 |
| 英文关键词 fuzzy logic neural network nearest neighborhood clustering scheme gradient descent method backpropagation learning scheme |
| 基金项目 |
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| 中文摘要 |
| 提出了一个种基于神经网络的自组织模糊系统，它能够根据输入输出数据灵活地划分模糊集合。由于采肜模糊聚类方法和梯度下降法分两步对该系统进行训练，其收敛速度要比传统的BP算法快得多。仿真结果表明该系统结构简单，学习速度快，规则数少，模型精度高。 |
| 英文摘要 |
| A self-organizing neural-network-based fuzzy system is proposed in this paper. It can partition the input spaces in a flexible way based on the distribution of the training data. By combining both the nearest neighborhood clustering scheme and the gradient descent method, the leaning speed converges much faster than the original back-propagation algorithm. Simulation results suggest that the SONNFS has merits of simple structure, fast learning speed, fewer fuzzy logicrules and relatively high modeling accuracy. |