Technique for effective validation of bio sensor using auto-associative neural network

Subhas A. Meti, V. Sangam
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Abstract

The major issue concerning the Wireless Body Area Network (WBAN) systems is that of the energy dissipation which directly affects the system longevity. One of the reasons for energy dissipation in WBAN system is due to interference of signals from other such networks which results in a dimensionality reduction problem. Another issue concerning the WBAN system is the prediction of data with respect to faults or misinterpretation of the signal. Many learning based algorithm are proposed for efficient prediction, however, these learning require large number of training samples often leading to increased computational time making it less preferred in practical applications. This paper intends to address the above mentioned issues by combining the method of principal component analysis with respect to nonlinearity along with Auto Associative Neural Network (AANN). Experimental observations shows an increase in the system efficiency considering the computational time and number of training samples considered for prediction and training phase with reduced mean absolute error.
基于自关联神经网络的生物传感器有效验证技术
无线体域网络(WBAN)系统的主要问题是能量损耗问题,它直接影响到系统的寿命。WBAN系统能量耗散的原因之一是由于来自其他网络信号的干扰,从而导致降维问题。关于WBAN系统的另一个问题是关于信号故障或误读的数据预测。许多基于学习的算法被提出用于高效预测,然而,这些学习需要大量的训练样本,往往导致计算时间增加,使其在实际应用中不太受欢迎。本文拟将非线性的主成分分析方法与自动关联神经网络(AANN)相结合来解决上述问题。实验观察表明,考虑到预测和训练阶段的计算时间和训练样本数量,系统效率有所提高,平均绝对误差降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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