A learning algorithm for tuning fuzzy rules based on the gradient descent method

机译：一种基于梯度下降法调整模糊规则的学习算法

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In this paper, we suggest a utility learning algorithm for tuning fuzzy rules by using input-output training data, based on the gradient descent method. The major advantage of this method is that the fuzzy rules or membership functions can be learned without changing the form of the fuzzy rule table used in usual fuzzy controls, so that the case of weak-firing can be avoided, which is different from the conventional learning algorithm. Furthermore, we illustrated the efficiency of the suggested learning algorithm by means of several numerical examples.

机译：在本文中，我们提出了一种基于梯度下降方法使用输入输出训练数据调整模糊规则的实用学习算法。这种方法的主要优点是可以学习模糊规则或隶属函数而不改变通常模糊控制中使用的模糊规则表的形式，因此可以避免闪电的情况，这与传统不同学习算法。此外，我们通过若干数值示例说明了所建议的学习算法的效率。

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《IEEE International Conference on Fuzzy Systems》|1996年||共7页
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Shi Y.; Mizumoto M.; Institute of Electric and Electronic Engineer;
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中图分类自动化技术、计算机技术;
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4. A learning algorithm for tuning fuzzy rules based on the gradient descent method [C] . Shi, Y., Mizumoto, Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on . 1996

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A learning algorithm for tuning fuzzy rules based on the gradient descent method

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