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Comparison of MLP Neural Network and Neuro-fuzzy System in Transcranial Doppler Signals Recorded from the Cerebral Vessels

机译:脑血管记录的经颅多普勒信号中MLP神经网络和神经模糊系统的比较

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摘要

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.
机译:通过使用16位声卡将110例患者的脑血管记录的经颅多普勒信号传输到个人计算机。进行了经颅多普勒信号的频谱分析,以确定多层感知器(MLP)神经网络和神经安卡拉模糊系统输入。为了更好的解释和快速诊断,采用MLP神经网络和神经模糊系统对经颅多普勒信号的FFT参数进行分类。我们的发现表明,从MLP神经网络获得92%的正确分类率,从神经模糊系统获得86%的正确分类率。

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