Accuracy analysis in back propagation neural network considering neurons proportionality among hidden layers

Y. Yong *, M. Guoe, J. Shan

Department of Mechanical and Aerospace Engineering, Old Dominion University, Norfolk, VA, United States


In this paper, an analysis is performed on the importance of proportionality among multiple hidden layers of BPNN. In case of any discrepancies in the network, a maximum of two layers is enough to train the whole neural network to get the desired result. But in some situations where accuracy is the chief criteria and training data is similar in data sets, (Like in Multiscript Numeral Recognition where shapes of different numerals resemble different values), in such conditions, accuracy matters the most. This paper describes a five-layer hidden approach to get the most possible accurate results in Multiscript Pin Code Recognition System using MATLAB. In order to get the desired output, the proportionality of hidden layers are semi-optimized to precise level but it may vary depending upon the training data set and the type of problem.


Multiple hidden layers, Back propagation neural network, Multiscript pin code recognition

Digital Object Identifier (DOI)

Article history

Received 5 February 2019, Received in revised form 1 May 2019, Accepted 2 May 2019

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Yong Y, Guoe M, and Shan J (2019). Accuracy analysis in back propagation neural network considering neurons proportionality among hidden layers. Annals of Electrical and Electronic Engineering, 2(6): 6-9

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