IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication

© 2014 by IJCA Journal

ETCC - Number 1

Year of Publication: 2014

M Mohanty and S Mishra. Article: Classification of Power Signal by using S-Transform and PSO based FLANN. *IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication* ETCC(1):1-5, September 2014. Full text available. BibTeX

@article{key:article, author = {M. Mohanty and S. Mishra}, title = {Article: Classification of Power Signal by using S-Transform and PSO based FLANN}, journal = {IJCA Proceedings on International Conference on Emergent Trends in Computing and Communication}, year = {2014}, volume = {ETCC}, number = {1}, pages = {1-5}, month = {September}, note = {Full text available} }

This paper presents a novel PSO(Particle swarm optimization) based FLANN(Functional Link Artificial Neural Network)classifier for the classification of non stationary power signals. The Multilayer perceptron (MLP) neural network model with back propagation learning algorithm consumes larger computational time. When the number of layers and number of hidden nodes in the MLP model increases, the complexity of the network increases. So, it is also very difficult to finalize the number of nodes in a layer. In this paper particle swarm optimization (PSO) is used to train the weights of the functional link artificial neural network (FLANN) for power signal classification. S-Transform is used to extract the features of the power signals and fed as input to the PSO based FLANN model.

- H. Zang, P. Liu and O. P. Malik, "Detection And Classification Of Power Quality Disturbances In Noisy Conditions", IEE Proc. Gener. Transm. Distrib, Vol 150, No. 5, pp. 567-572, Sept. 2003.
- T. McConaghy, H. Lung, E. Bosse, V. Vardan, "Classification Of Audio Radar Signals Using Radial Basis Function Neural Network", IEEE Transactions on Inst . And Measurement, Vol. 52, No. 6, pp. 1771-17779, Dec. 2003.
- J. C. Patra, R. N. Pal, B. N. Chatterji, G. Panda, "Identification of non-linear & dynamic system using functional link artificial neural network ", IEEE Transactions on System, Man & Cybernetics – Part B; Cybernetics, Vol. 29, No. 2, April 1999.
- S. Dehuri, Sung-Bae Cho "Evolutionarily optimized features in functional link neural network for classification" Expert Systems with Applications, Vol. 37 (2010) ,pp-4379–4391.
- S. Dehuria,?, R. Roy, Sung-Bae Choc, A. Ghosh "An improved swarm optimized functional link artificial neural network(ISO-FLANN) for classification" The Journal of Systems and Software ,Vol. 85 (2012) pp-1333– 1345
- S. Chakravarty, P. K. Dash, "A PSO based integrated functional link net and interval type-2 fuzzy logic system for predicting stock market indices" Applied Soft Computing 12 (2012) 931–941
- C. R. Hema. M. P. Paulraj. S. Yaacob,A. H. Adom. R. Nagarajan,"particl swarm optimization neural network based classification of mental task" biomed 2008,Proceedings 21;pp. 883-888,2008, (c) Springer-Verlag Berlin Heidelberg 2008
- James Kennedy and Russell Eberhart (1995) Particle Swarm Optimization, Proc. IEEE International Conf. on Neural Networks, Perth , Vol. 4, pp 1942-1948
- C. Xudong, Q. Jingen,N. Guangzheng,Y. Shiyou,Z. Mingliu, "An Improved Genetic Algorithm For Global Optimization Of Electromagnetic Problems", IEEE Transactions on magnetic, Vol. 37, pp. 3579–3583, Sept. 2001.
- F. Zhao and R. Yang, "Power-Quality Disturbance Recognition Using S-Transform", IEEE Transactions on Power Delivery Vol. 22, No. 2, pp. 944–950, 2007.
- C. R. Pinnegar, L. Mansinha, "The S-Transform With Window Of Arbitrary And Varying Shape," GEOPHYSICS, Vol. 68, No. 1, pp. 381-385, 2003.
- C. R. Pinnegar, L. Mansinha, "Time-Local Fourier Analysis With A Scalable, Phase-Modulated Analyzing Function: The S-Transform With A Complex Window," Signal Processing, Vol. 84, pp. 1167-1176, July 2004.
- S. Assous, A. Humeau, M. Tartas, P. Abraham, and J. L. Huillier, "S–Transform Applied To Laser Doppler Flowmetry Reactive Hyperemia Signals", IEEE Trans. Biomed. Eng. Vol. 53, pp. 1032-1037,2006.
- P. K. Dash, B. K. Panigrahi and G. Panda, Power Quality Analsis using S-Transform, IEEE Trans on Power Delivery Vol. 8, No. 2, pp. 406–412, April 2003.
- T. Wang et al. , "A Wavelet Neural Network For The Approximation Of Nonlinear Multivariable Function", The Trans. of the Institute of Electrical Engineering C, 102-C, pp. 185-193, 2000.
- D. F. Specht and H. Romsdahl, "Experience With Adaptive Probabilistic And General Regression Neural Networks", Proceedings of the IEEE World Congress Computational Intelligence, F. L. Orlando, ed. , Vol. 2, pp. 1203–1208, 1994.
- S. Albrecht and J. Busch et al. , "Generalized Radial Basis Function Networks for Classification And Novelty Detection: Self Organization Of Optimal Bayesian Decision", Neural Networks Vol. 13, pp. 1075–1093, 2000.
- M. Plutowski, H. White, "Selecting Concise Training Sets From Clean Data", IEEE Trans. Neural Networks, Vol. 4, pp. 305–318, 1993.
- R. Reed, S. Oh, R. J. Marks, II, "Similarities Of Error Regularization, Sigmoid Gain Scaling, Target Smoothing, And Training With Jitter", IEEE Trans. Neural Networks ,Vol. 6, pp. 529–538.
- M. D. Richard, R. P. Lippmann, "Neural Network classifier estimates Bayesian a posteriori probabilities", Neural Computing, Vol. 3, pp. 461–483, 1991