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.
Feed forward back propagation algorithm, Neural network, Neuron, Local gradient, Synapticweight,Activation function
Ezechukwu O. A. , Aneke Jude I. , Uwaechi P. C. "Analysis Of Neural Network Back-Propagation Algorithm" Iconic Research And Engineering Journals Volume 2 Issue 4 2018 Page 33-37
Ezechukwu O. A. , Aneke Jude I. , Uwaechi P. C. "Analysis Of Neural Network Back-Propagation Algorithm" Iconic Research And Engineering Journals, 2(4)