学习基于模糊规则的神经网络的函数逼近

[Proceedings 1992] IJCNN International Joint Conference on Neural Networks Pub Date : 1992-06-07 DOI:10.1109/IJCNN.1992.287127

C. Higgins, R. M. Goodman

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引用次数: 33

摘要

作者提出了一种从函数及其因变量的样本中归纳模糊逻辑规则来预测数值函数的方法。该方法使用了一种基于作者先前对离散值数据的工作的信息论方法(参见Proc. Int)。关节。忏悔。网，第1卷，第875-80页，1991年)。学习到的规则可以用在神经网络中，根据它的因变量来预测函数值。给出了一个学习控制系统功能的例子。

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Learning fuzzy rule-based neural networks for function approximation

The authors present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on the authors' previous work with discrete-valued data (see Proc. Int. Joint. Conf. on Neur. Net., vol.1, p.875-80, 1991). The rules learned can then be used in a neural network to predict the function value based on its dependent variables. An example is shown of learning a control system function.<>

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[Proceedings 1992] IJCNN International Joint Conference on Neural Networks

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