1. Application of back propagation neural network in predicting hc emission from i. c. engines
Authors : Aunshuman Chatterjee
Pages : 57-63
DOI : http://dx.doi.org/10.21172/1.133.09
Keywords : Emission, Neural Network (NN), Back propagation neural network (BPNN), RMS error Abstract :One of the prominent emissions of I.C engines is the Hydro-carbons commonly known as HC which adversely affects our eco-system. In the present work, an attempt has been made towards the application of Back Propagation Neural Network (BPNN) for predicting the HC emission from a diesel engine for various engine settings such as compression ratio, injection timing and load. The data collected trains the Neural Network (NN) and these inputs were strategically combined to predict HC emission. It has been observed that by the right combination of input parameters to the NN, may effectively predict the level of HC emission with minimum Root Mean Squared (RMS) error of almost less than 7.4%.
Citing this Journal Article :Aunshuman Chatterjee, "Application of back propagation neural network in predicting hc emission from i. c. engines", Volume 13 Issue 3 - May 2019, 57-63
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