神经模糊合作系统的原型

A. Kawamura, N. Watanabe, H. Okada, K. Asakawa
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引用次数: 43

摘要

作者正在开发一种神经-模糊合作系统的原型,该系统具有神经网络的精度和学习能力,并且像模糊模型一样易于理解。为了帮助在神经系统和模糊系统之间进行转换,该系统具有一个结构与模糊模型相对应的神经网络。从专家那里获得的知识由模糊系统转化为神经网络。将神经网络应用于目标系统,并从运行过程中获得的数据中学习,以提高模型的准确性。将神经网络转换回模糊模型有助于解释神经网络的内部表示。目标系统的模型将被构建为基本规则,并将使用模糊-神经和神经-模糊转换的重复逐步改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A prototype of neuro-fuzzy cooperation system
The authors are developing a prototype of a neuro-fuzzy cooperation system that has the precision and learning ability of a neural network and is easy to understand like a fuzzy model. To help convert between neural and fuzzy systems, this system has a neural network with a structure corresponding to that of a fuzzy model. Knowledge acquired from experts was converted from a fuzzy system to a neural network. The neural network was applied to a target system and learned from data obtained during operation to enhance the accuracy of the model. Converting the neural network back into a fuzzy model helps explain the inner representation of the neural network. The model of the target system will be constructed as basic rules and will be improved step by step using a repetition of the fuzzy-neuro and neuro-fuzzy conversion.<>
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