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Novel Application of Linear Scaling to Improve Accuracy of Optimized Artificial Neural Network Using Levenberg-Marquardt Algorithm in Prediction of Daily Nitrogen Oxide for Health Management

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Other literature type 09 Oct 2020Publisher:Springer Singapore

Authors: Vibha Yadav; Satyendra Nath;

doi: 10.1007/978-981-15-7571-6_31

Novel Application of Linear Scaling to Improve Accuracy of Optimized Artificial Neural Network Using Levenberg-Marquardt Algorithm in Prediction of Daily Nitrogen Oxide for Health Management

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Abstract

This chapter examines application of linear scaling for improving the developed model ANN-1 and ANN-2 prediction accuracy using artificial neural network. Normalized values of different input and output variable such as ambient temperature, rack temperature, toluene, xylene and Nitrogen Oxide are used for ANN-1 model. The ANN-2 model utilize scaled stochastic as input and air pollutants as output variables. For ANN one the least mean absolute percentage error (MAPE) is 0.0076 whereas for ANN-2 model is 0.00106, showing predicted ANN-2 model has more accuracy than ANN-1 by 85.53%. Therefore it is found that with the application of linear scaling prediction accuracy of ANN model is improved by 85.53%. This study recommends by using linear scale data along with normalized data in developing models higher prediction accuracy is achieved.

Related Organizations

University of Agriculture Faisalabad
Pakistan
University of Agriculture Faisalabad
Pakistan

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