基于新模糊神经元的系统建模新方法，在实际系统中的应用

Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94 Pub Date : 1994-11-06 DOI:10.1109/TAI.1994.346442

E. Uchino, T. Yamakawa

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引用次数: 27

摘要

本文介绍了一种利用新模糊神经元进行系统建模的新方法。关注系统通过简单地将目标系统的输入和输出数据馈送给作者在1992年提出的新模糊神经元(neo-fuzzy-neuron)进行自适应建模。首先，将新模糊神经元应用于饱和和/或间歇语音或混沌信号的恢复，以显示其实际效果。然后对其进行扩展，以获得更好的泛化能力。介绍了一种利用分段线性隶属函数的自适应模糊建模方法。实验结果为其实际应用提供了充分的依据。

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Neo-fuzzy-neuron based new approach to system modeling, with application to actual system

This paper introduces a new approach to system modeling by using a neo-fuzzy-neuron. The system of concern is modeled adaptively by simply feeding to the neo-fuzzy-neuron, the basic principle of which was proposed by the authors in 1992, the input and the output data of the objective system. Firstly, the neo-fuzzy-neuron is applied to the restoration of a saturated and/or intermittent speech or chaotic signal to show its actual effectiveness. It is then extended in order to get a better generalization capability. An adaptive fuzzy modeling with use of a piece-wise linear membership function is also introduced. The experimental results have provided substantial proofs for their practical use.<>

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Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94

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