• Dr ARUL LEENA ROSE P.J

ABSTRACT In this Paper the application of neural network techniques to the paper-making industry, particularly for the prediction of paper “curl”. Paper curl is a common problem and can only be measured reliably off-line, after manufacture. Model development is carried out using imperfect data, typical of that collected in many manufacturing environments, and addresses issues pertinent to real-world use. Predictions then are presented in terms that are relevant to the machine operator, as a measure of paper acceptability, a direct prediction of the quality measure, and always with a measure of prediction confidence. Therefore, the techniques described in this Paper are widely applicable to industry.

Key words: Paper Curl, Neural Networks and multiplayer Perceptron.

"FINDING PAPER QUALITY TO PREDICT PAPER CURL USING ARTIFICIAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 5, page no.516-527, May-2018, Available :http://www.jetir.org/papers/JETIR1805508.pdf

"FINDING PAPER QUALITY TO PREDICT PAPER CURL USING ARTIFICIAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 5, page no. pp516-527, May-2018, Available at : http://www.jetir.org/papers/JETIR1805508.pdf