| 从样本数据中获取模糊规则的一种算法 |
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| 引用本文： | 荣莉莉 王众托. 从样本数据中获取模糊规则的一种算法[J]. 系统工程学报, 1998, 13(1): 57-65 |
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| 作者姓名： | 荣莉莉 王众托 |
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| 作者单位： | 大连理工大学系统工程研究所 |
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| 基金项目： | 国家教委博士点专项基金,辽宁省博士科研启动基金 |
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| 摘 要： | 提出一种直接从样本数据中获取模糊规则的算法．模糊规则的隶属函数通过计算样本数据的方差与期望而得出，规则的抽取通过一个５层模糊神经网络实现，该算法包括两部分，第１部分确定出最佳规则；第２部分通过学习提高推理精度，通过仿真验证了该算法的有效性．
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| 关 键 词： | 模糊神经网络，规则抽取，隶属函数，学习 |
AN ALGORITHM OF EXTRACTING FUZZY RULES FROM NUMERICAL EXAMPLES |
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| Rong Lili Wang Zhongtuo. AN ALGORITHM OF EXTRACTING FUZZY RULES FROM NUMERICAL EXAMPLES[J]. Journal of Systems Engineering, 1998, 13(1): 57-65 |
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| Authors: | Rong Lili Wang Zhongtuo |
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| Abstract: | In this paper,a general method to obtain fuzzy rules directly from numerical data is proposed.The membership functions of antecedent part and consequuent part are determined by calculating the variance and expectation of the examples.The extraction of fuzzy rules is done by using a fuzzy neural network.The algorithm has two parts.The first part is to determine the optimal number of fuzzy rules.The second part is to improve the accuracy of the inference system.Through a simulation example,the effectiveness of the proposed algorithm is verified. |
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| Keywords: | fuzzy neural network extracting rules membership function learning |
| 本文献已被 CNKI 维普 等数据库收录！ |
