柔性加权神经模糊系统

Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02. Pub Date : 2002-11-18 DOI:10.1109/ICONIP.2002.1198995

L. Rutkowski, K. Cpałka

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引用次数: 40

摘要

本文研究了一种新的神经模糊系统。它们被称为or型模糊推理系统(NFIS)。根据输入输出数据，我们不仅学习了隶属函数的参数，还学习了系统的类型和聚合参数。我们提出了神经模糊推理系统的加权t范数和s范数。我们的方法为神经模糊系统的结构和学习引入了更多的灵活性。

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Flexible weighted neuro-fuzzy systems

In the paper we study new neuro-fuzzy systems. They are called the OR-type fuzzy inference systems (NFIS). Based on the input-output data we learn not only parameters of membership functions but also a type of the systems and aggregating parameters. We propose the weighted T-norm and S-norm to neuro-fuzzy inference systems. Our approach introduces more flexibility to the structure and learning of neuro-fuzzy systems.

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Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02.

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