Title: Enhanced auto associative neural network using feed forward neural network: an approach to improve performance of fault detection and analysis

Authors: Subhas A. Meti; V.G. Sangam

Addresses: Department of Computer Science Engineering, Sanjeevan Engineering and Technology Institute, Kolhapur, India ' Department of Electronics and Instrumentation, Dayanand Sagar College of Engineering, Bangalore, India

Abstract: Biosensors have played a significant role in many of present day's applications ranging from military applications to healthcare sectors. However, its practicality and robustness in its usage in real time scenario is still a matter of concern. Primarily issues such as prediction of sensor data, noise estimation and channel estimation and most importantly in fault detection and analysis. In this paper an enhancement is applied to the auto associative neural network (AANN) by considering the cascade feed forward propagation. The residual noise is also computed along with fault detection and analysis of the sensor data. An experimental result shows a significant reduction in the MSE as compared to conventional AANN. The regression based correlation coefficient has improved in the proposed method as compared to conventional AANN.

Keywords: WBAN; fault detection and analysis; feed forward neural network; enhanced AANN; residual noise.

DOI: 10.1504/IJDATS.2019.103754

International Journal of Data Analysis Techniques and Strategies, 2019 Vol.11 No.4, pp.291 - 309

Received: 09 Dec 2016
Accepted: 02 Sep 2017
Published online: 27 Nov 2019 *

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