Abstract
For safety critical systems in nuclear power plant (NPP), accurate classification of multiclass transient leads to safer operation of the plant. Supervised machine learning is a key technique which solves multiclass classification related problems. The most widely used multiclass supervised machine learning methods for this purpose are k-nearest neighbor algorithm, support vector machine algorithm and artificial neural network (ANN) algorithm. This paper describes a comparative study on the performance of these algorithms towards classifying some of the transients in NPP. The performance analysis is mostly based on the prediction accuracy in classifying the correct transient occurred. Along with prediction accuracy, total number of epochs, training time and root mean square error was also observed as a characteristic feature for determining the performance of any backpropagation ANN. A 10-fold cross validation was carried on all these algorithms for ten times and the best among them was finally concluded for multiclass transient classification in NPP.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Waegeman, W., Verwaeren, J., Slabbinck, B., Baets, B.D.: Supervised learning algorithms for multi-class classification problems with partial class memberships. Fuzzy Sets Syst. 184, 106–125 (2011)
Ventouras, E.M., Asvestas, P., Karanasiou, I., Matsopoulos, G.K.: Classification of error-related negativity (ERN) and positivity (Pe) potentials using kNN and support vector machines. Comput. Biol. Med. 41, 98–109 (2011)
Chen, J., Wang, C., Wang, R.: Adaptive binary tree for fast SVM multiclass classification. Neurocomputing 72, 3370–3375 (2009)
Kumar, M.A., Gopal, M.: Reduced one-against-all method for multiclass SVM classification. Expert Syst. Appl. 38, 14238–14248 (2011)
Li, Y.: A novel statistical algorithm for multiclass EEG signal classification. Eng. Appl. Artif. Intell. 34, 154–167 (2014)
Oong, T.H., MatIsa, N.A.: One-against-all ensemble for multiclass pattern classification. Appl. Soft Comput. 12, 1303–1308 (2012)
Chena, K., Xu, L., Chi, H.: Improved learning algorithms for mixture of experts in multiclass classification. Neural Netw. 12, 1229–1252 (1999)
Ahmed, S.S., Rao, B.P.C., Jayakumar, T.: A framework for multidimensional learning using multilabel ranking. Int. J. Ad. Intell. Paradigms 5(4), 299–318 (2013)
Prusty, M.R., Chakraborty, J., Seetha, H., Jayanthi, T., Velusamy, K.: Fuzzy logic based transient identification system for operator guidance using prototype fast breeder reactor operator training simulator. In: Proceedings of IEEE International Advance Computing Conference (IACC), pp. 1259–1264 (2014)
Mishra, S., Bhenede, C.N., Panigrahi, B.K.: Detection and classification of power quality disturbances using S-transform and probabilistic neural network. IEEE Trans. Power Deliv. 23(1), 280–287 (2008)
Panigrahi, B.K., Pandi, V.R.: Optimal feature selection for classification of power quality disturbances using wavelet packet based fuzzy k-nearest neighbor algorithm. IET Proc. Gen. Trans. Distrib. 3(3), 296–306 (2009)
Saini, I., Singh, D., Khosla, A.: QRS detection using K-nearest neighbor algorithm (KNN) and evaluation on standard ECG databases. J. Adv. Res. 4, 331–344 (2013)
Yazdani, A., Ebrahimi, T., Hoffmann, U.: Classification of EEG signals using Dempster Shafer theory and a k-nearest neighbor classifier. In: Proceedings of the 4th International IEEE EMBS Conference on neural engineering, Antalya, Turkey, pp. 327–330 (2009)
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)
Roobaert, D.: DirectSVM: a fast and simple support vector machine perceptron. In: Proceedings of the 2000 IEEE Signal Processing Society Workshop, pp. 356–365 (2000)
Ahmed, S.S., Rao, B.P.C., Jayakumar, T.: Radial basis functions for multidimensional learning with an application to nondestructive sizing of defects. In: IEEE Symposium on Foundations of Computational Intelligence (FOCI), pp. 38–43 (2013)
Chen, F., Tang, B., Song, T., Li, L.: Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization. Measurement 47, 576–590 (2014)
Pan, X., Lee, B., Zhang, C.: A comparison of neural network backpropagation algorithms for electricity load forecasting. In: IEEE International Workshop on Intelligent Energy Systems (IWIES), pp. 22–27 (2013)
Design Document on PFBR Simulator - PFBR/08610/DN/1000/Rev A (2003)
Acknowledgement
The authors express their sincere thanks to the PFBR Operator Training simulator (KALBR-SIM) team members and Shri S.A.V. Satya Murty, Director, EIRSG, IGCAR for providing constant guidance and support in completing this research. The authors are greatly indebted to the constant support and motivation provided by Dr. P.R. Vasudeva Rao, Director, IGCAR.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Prusty, M.R., Chakraborty, J., Jayanthi, T., Velusamy, K. (2015). Performance Comparison of Supervised Machine Learning Algorithms for Multiclass Transient Classification in a Nuclear Power Plant. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-319-20294-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20293-8
Online ISBN: 978-3-319-20294-5
eBook Packages: Computer ScienceComputer Science (R0)