Two-pass orthogonal least-squares algorithm to train and reduce fuzzy logic systems

Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference Pub Date : 1994-06-26 DOI:10.1109/FUZZY.1994.343651

J. Hohensohn, J. Mendel

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引用次数: 29

Abstract

Fuzzy logic systems (FLSs) can be designed using training data (i.e. given M numerical input/output pairs) and supervised learning algorithms. Orthogonal least-squares (OLS) learning decomposes a FLS into a linear combination of M/sub s/>

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两步正交最小二乘算法训练和约简模糊逻辑系统

模糊逻辑系统(FLSs)可以使用训练数据(即给定M个数值输入/输出对)和监督学习算法来设计。正交最小二乘(OLS)学习将FLS分解为M/sub />的线性组合

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Proceedings of 1994 IEEE 3rd International Fuzzy Systems Conference

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