NDLI: Development of an artificial neural network system for sulphate-reducing bacteria detection by using model-based design technique

Content Provider	IEEE Xplore Digital Library
Author	Earn Tzeh Tan Halim, Z.A.
Copyright Year	2012
Description	Author affiliation: Collaborative Microelectronic Design Excellence Centre, University Sains Malaysia, Penang, Malaysia (Halim, Z.A.) \|\| School of Electrical and Electronic Engineering, University Sains Malaysia, Penang, Malaysia (Earn Tzeh Tan)
Abstract	The paper demonstrates an approach for the implementation of artificial neural network (ANN) as a pattern recognition system in the development of electronic nose devices for sulphate-reducing bacteria (SRB) detection. The neural network algorithm was modeled using Xilinx System Generator and can be realized in Xilinx FPGA. The neural network was trained using the Levenberg-Marquardt back propagation algorithm in Matlab based on data collected from experiments, in which samples that contained either the broth medium with SRB or only the broth medium were used. In Matlab, sigmoid function is used as the activation function for the neurons in hidden and output layers. However, the block sets provided by Xilinx System Generator do not contain the sigmoid function. Thus, the sigmoid function was modeled through the expansion of the Maclaurin power series. Adders and multipliers blocks are used to calculate the series equation. This paper discusses on the similarity of sigmoid function generated using Matlab and approximation of sigmoid function generated using Maclaurin power series. The simulated results show that the absolute error between the values of sigmoid function based on Matlab and Xilinx System Generator using Maclaurin power series are not more than 1.38% when the inputs fall in the range between −0.5 to 0.5. Thus, the modeling of sigmoid function in Xilinx System Generator has been successfully modeled with the accuracy of the ANN to detect SRB is 100%.
Starting Page	352
Ending Page	355
File Size	761192
Page Count	4
File Format	PDF
e-ISBN	9781457717291
DOI	10.1109/APCCAS.2012.6419044
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-02
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Mathematical model Biological neural networks Generators Approximation methods Neurons MATLAB
Content Type	Text
Resource Type	Article

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