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Analysis Of Neural Network Back-Propagation Algorithm
Subject area: Science,Engineering and Technology · Area of research: Neural Network
Abstract
Artificial neural networks simulate the neural systems behaviour by means of the interconnection of the basic processing units called neurons. The neurons canreceive external signals or signals coming from the otherneurons affected by a factor called weight. The output ofneuron is the result of applying a specific function, knownas transfer function, to the sum of its inputs plus thresholdvalue called bias. This paper demonstrates the practical analysis of neural network back-propagation algorithm.It shows the mathematical process of how the neural network manages the data fed to it for it to be trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for the pattern recognition process.
Keywords
Feed forward back propagation algorithm, Neural network, Neuron, Local gradient, Synapticweight,Activation function
References
[1] Anderson, J. A. An Introduction to Neural Networks. Prentice Hall, 2003.
[2] Gaudrat, J., Giusiano B., and Huiart. Comparison of the performance of multi- layer perceptron and linear regression for epidemiological data. Computer Statist. & Data Anal., 44, 547-70, 2004.
[3] Howard Demuth, Mark Beale, Martin Hagan. The MathWorks.
[4] Lukowicz M., Rosolowski E. (2013). Artificial neural network based dynamic compensation of current transformer errors. Proceedings of the 8 th International Symposium on Short-Circuit Currents in Power Systems, Brussels, pp. 19-24.
[5] R.P.Hasbe, A.P.Vaidya, Detection and classification of faults on 220 KV transmission line using wavelet transform and neural network, International Journal of Smart Grid and Clean Energy, August 2013.
How to cite this paper
@article{1700753,
author = {Ezechukwu O. A., Aneke Jude I., Uwaechi P. C.},
title = {Analysis Of Neural Network Back-Propagation Algorithm},
journal = {Iconic Research And Engineering Journals},
year = {2018},
volume = {2},
number = {4},
pages = {33-37},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1700753.pdf},
abstract = {Artificial neural networks simulate the neural systems behaviour by means of the interconnection of the basic
processing units called neurons. The neurons canreceive external signals or signals coming from the otherneurons affected by a factor called weight. The output ofneuron is the result of applying a specific function, knownas transfer function, to the sum of its inputs plus thresholdvalue called bias. This paper demonstrates the practical analysis of neural network back-propagation algorithm.It shows the mathematical process of how the neural network manages the data fed to it for it to be trained to recognize patterns, classify data and forecast future events. Feed forward networks have been employed along with back propagation algorithm for the pattern recognition process.
},
keywords = {Feed forward back propagation algorithm, Neural network, Neuron, Local gradient, Synapticweight,Activation function},
month = {October},
}