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http://dx.doi.org/10.12989/was.2014.18.6.619

New criteria to fix number of hidden neurons in multilayer perceptron networks for wind speed prediction  

Sheela, K. Gnana (Anna University, Regional Centre)
Deepa, S.N. (Anna University, Regional Centre)
Publication Information
Wind and Structures / v.18, no.6, 2014 , pp. 619-631 More about this Journal
Abstract
This paper proposes new criteria to fix hidden neuron in Multilayer Perceptron Networks for wind speed prediction in renewable energy systems. To fix hidden neurons, 101 various criteria are examined based on the estimated mean squared error. The results show that proposed approach performs better in terms of testing mean squared errors. The convergence analysis is performed for the various proposed criteria. Mean squared error is used as an indicator for fixing neuron in hidden layer. The proposed criteria find solution to fix hidden neuron in neural networks. This approach is effective, accurate with minimal error than other approaches. The significance of increasing the number of hidden neurons in multilayer perceptron network is also analyzed using these criteria. To verify the effectiveness of the proposed method, simulations were conducted on real time wind data. Simulations infer that with minimum mean squared error the proposed approach can be used for wind speed prediction in renewable energy systems.
Keywords
hidden neurons; mean squared error; multilayer perceptron; neural networks; wind speed prediction;
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  • Reference
1 Choy, B., Lee, J.H. and Kim, D.H. (2008), "Solving local minima problem with large number of hidden nodes on two layered feed forward artificial neural networks", Neurocomputing, 71, 3640- 3643.   DOI
2 Fujita, O. (1998), "Statistical estimation of the number of hidden units for feed forward neural networks", Neural Networks, 11(5), 851-859.   DOI
3 Grewal, B.S. (2007), Higher engineering mathematics, Khanna Publishers, 40th Ed., New Delhi.
4 Hagiwara, M. (1996), "A Simple and effective method for removal of hidden units and weights", Neuro Comput., 6(2), 207-218.
5 Doukim, C.A., Dargham, J.A. and Chekima, A. (2010), "Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique", Proceedings of the IEEE 10th Int. Conf. on Information Science, Signal Processing and their Applications.
6 Han, M. and Yin, J. (2008), "The hidden neurons selection of the wavelet networks using Support vector machines and ridge regression", Neurocomputing, 72(1-3), 471-479.   DOI
7 Islam, M. and Murase, K. (2011), "A new algorithm to design compact two hidden layer artificial neural network", Neural Networks, 14(9), 1265-1278.
8 Ke, J. and Liu, X. (2008), "Empirical analysis of optimal hidden neurons in neural network modeling for stock prediction", Proceedings of the IEEE Pacific Asia Workshop on Computa. Intel. and Indust. App.
9 Zhang, Z., Ma, X. and Yang, Y. (2003), "Bounds on the number of hidden neurons in three layers Binary neural networks", Neural Networks, 16(7), 995-1002.   DOI
10 Trenn, S. (2008), "Multilayer perceptrons: approximation order and necessary number of hidden units", IEEE T. Neural Networ., 19(5), 836-844.   DOI
11 Xu, S. and Chen, L. (2008), "A novel approach for determining the optimal number of hidden layer neurons for FNN's and its application in data mining", Proceedings of the 5th Int. Conf. on Inform. Techn. and Appln.
12 Zeng, X., and Yeung, D.S.(2006), "Hidden neuron pruning of multilayer perceptrons using a quantified sensitivity measure", Neurocomputing, 69(7-9)825-837.   DOI   ScienceOn
13 Li, J.Y., Chow, T.W.S. and Yu, Y.L. (1995), "The estimation theory and optimization algorithm for the number of hidden units in the higher order feedforward neural network", Neural Networks, 3, 1228- 1233.
14 Keeni, K. and Kenji, N, (1999), Estimation of initial weights and hidden units for fast learning of multi layer neural networks for pattern classification, Dept. of Infor. Systems & Quantitative Sciences, Japan.
15 kurkova, V., Kainen, P.C. and kreinovich, V. (1997), "Estimates of the number of hidden units and variation with respect to half spaces", Neural Networks, 10(6),1061-1068.   DOI
16 Lan, Y., Soh, Y.C. and Huang, G.B. (2010), "Constructive hidden nodes selection of extreme learning machine for regression", Neurocomputing, 73(16-18) 3191-3199.   DOI
17 Liu, Y., Starzyk, J.A. and Zhu, Z. (2007), "Optimizing number of hidden neurons in neural networks", Proceedings of the IASTED Int Conf .
18 Panchal, G., Ganatra, A., Kosta, Y.P. and Panchal, D. (2011), "Behaviour analysis of multilayer perceptrons with multiple hidden neurons and hidden layers", Int.J. Comput. Theory Eng., 3, 332-337.
19 Sartori, M.A. and Antsaklis, P.J. (1991), "A simple method to derive bounds on the size and to train multilayer neural networks", IEEE T. Neural Networ., 2(4), 467-471.   DOI   ScienceOn
20 Sivanandam, S.N., Sumathi, S. and Deepa, S.N. (2008), Introduction to neural networks using Matlab 6.0, Tata McGraw Hill, 1st Ed.
21 Sun, J. (2012), "Learning algorithm and hidden node selection scheme for local Coupled feed forward neural network classifier", Neurocomputing, 79, 158-163.   DOI   ScienceOn
22 Tamura, S. and Tateishi, M. (1997), "Capabilities of a four layered feed forward neural network: four layers versus three", IEEE T. Neural Networ., 8(2), 251-255.   DOI
23 Hunter, D., Yu, H., Pukish, M.S., Kolbusz, J. and Wilamowski, B.M. (2012), "Selection of proper neural network sizes and architectures- a comparative study", IEEE T. Ind. Inform., 8(2), 228-240.   DOI