Volume 8 Issue 1 - January 2017

  • 1. Optimization of spinning process using computational intelligence

    Authors : Chandrakant Jadhav, Atul Kamble

    Pages : 1-9

    DOI : http://dx.doi.org/10.21172/1.81.001

    Keywords : Artificial Neural Network, Genetic Algorithm, Optimization

    Abstract :

    The BP feed-forward neural network is popular in solving many non-linear problems. The most important problem in this is to decide optimal structure of a feed-forward neural network. Literature presents a multitude of methods but there is no rigorous and accurate analytical method. Different ANN topologies can be trained for prediction, but results obtained are not necessarily be optimal. In Hybrid ANN-GA methodology, many neural network topologies are trained and results obtained for these topologies are optimized using GA.This paper presents hybrid approach of neural networks and genetic algorithms for application of Textile Spinning in computing optimal values of fibre properties for required yarn. It is feasible to predict yarn properties in advance for provided fibre for spinning. This help in selection of fibre to produce yarn required by customer. ANN is developed to predict multi-property fibre for required yarn. Total 25 different structures of ANN are trained for same data and mean absolute error is calculated. After testing, predicted fibre properties of each topology, i.e. 25 results are provided to GA. GA searches optimal fibre properties from outcome of different ANN topologies. The result shows that the accuracy of proposed integrated approach is higher than individual topologies of ANN.

    Citing this Journal Article :

    Chandrakant Jadhav, Atul Kamble, "Optimization of spinning process using computational intelligence", https://www.ijltet.org/journal_details.php?id=910&j_id=3556, Volume 8 Issue 1 - January 2017, 1-9, #ijltetorg