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A fast learning algorithm based on extreme learning machine for regular fuzzy neural network

机译:一种基于常规模糊神经网络极限学习机的快速学习算法

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摘要

The regular fuzzy neural network (RFNN) is a kind of fuzzy neural network by fuzzifying the feed-forward neural network. The RFNN can directly deal with the language information and it has the merits of fuzzy system and neural network. It is presented a fast learning algorithm based on the extreme learning machine (ELM) for the RFNN in this paper. The RFNN referred here is a three-layer feed-forward fuzzy neural network and the connected weights in the RFNN are all fuzzy numbers. A simulation example is given to approximately realize the fuzzy if-then rules by the RFNN. The results show that the RFNN trained by the proposed algorithm has good performance and approximation ability.
机译:常规模糊神经网络(RFNN)是一种通过模糊前馈神经网络来模糊神经网络。 RFNN可以直接处理语言信息,它具有模糊系统和神经网络的优点。 在本文中基于RFNN的极限学习机(ELM)的快速学习算法。 此处提到的RFNN是三层前馈模糊神经网络,RFNN中的连接权重都是模糊数。 仿真示例是通过RFNN大致实现模糊IF-DOT规则。 结果表明,由所提出的算法训练的RFNN具有良好的性能和近似能力。

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