Suppr超能文献

多层感知器神经网络与神经模糊系统在从脑血管记录的经颅多普勒信号中的比较。

Comparison of MLP neural network and neuro-fuzzy system in transcranial Doppler signals recorded from the cerebral vessels.

作者信息

Hardalaç Firat

机构信息

Department of Computer Engineering, Faculty of Engineering, Kirikkale University, Kirikkale, Turkey.

出版信息

J Med Syst. 2008 Apr;32(2):137-45. doi: 10.1007/s10916-007-9116-6.

Abstract

Transcranial Doppler signals recorded from cerebral vessels of 110 patients were transferred to a personal computer by using a 16 bit sound card. Spectral analyses of Transcranial Doppler signals were performed for determining the Multi Layer Perceptron (MLP) neural network and neuro Ankara-fuzzy system inputs. In order to do a good interpretation and rapid diagnosis, FFT parameters of Transcranial Doppler signals classified using MLP neural network and neuro-fuzzy system. Our findings demonstrated that 92% correct classification rate was obtained from MLP neural network, and 86% correct classification rate was obtained from neuro-fuzzy system.

摘要

通过使用16位声卡,将从110名患者脑血管记录的经颅多普勒信号传输到个人计算机。对经颅多普勒信号进行频谱分析,以确定多层感知器(MLP)神经网络和神经安卡拉模糊系统的输入。为了进行良好的解释和快速诊断,使用MLP神经网络和神经模糊系统对经颅多普勒信号的快速傅里叶变换(FFT)参数进行分类。我们的研究结果表明,MLP神经网络的正确分类率为92%,神经模糊系统的正确分类率为86%。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验