Extreme Learning Machine Neural Networks for Multi-Agent System in Power Generation

  • Authors

    • Chong Tak Yaw
    • Shen Yuong Wong
    • Keem Siah Yap
    • Chin Hooi Tan
  • Extreme Learning Machine, Power System Generation, Multi-Agent System
  • Extreme Learning Machine (ELM) is widely known as an effective learning algorithm than the conventional learning methods from the point of learning speed as well as generalization. The hidden neurons are optional in neuron alike whereas the weights are the criteria required to study the linking among the output layer as well as hidden layers. On the other hand, the ensemble model to integrate every independent prediction of several ELMs to produce a final output. This particular approach was included in a Multi-Agent System (MAS). By hybrid those two approached, a novel extreme learning machine based multi-agent systems (ELM-MAS) for handling classification problems is presented in this paper. It contains two layers of ELMs, i.e., individual agent layer and parent agent layer. Several activation functions using benchmark datasets and real-world applications, i.e., satellite image, image segmentation, fault diagnosis in power generation (including circulating water systems as well as GAST governor) were used to test the ELM-MAS developed. Our experimental results suggest that ELM-MAS is capable of achieving good accuracy rates relative to others approaches.

  • References

    1. [1] Huang GB, Zhu QY & Siew CK (2004), Extreme Learning Machine: a new learning scheme of feedforward neural networks, in: Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN2004), vol. 2, Budapest, Hungary, July 25-29, pp.985–990.

      [2] Huang GB, Zhu QY, Mao K, Siew CK, Saratchandran P, & Sundararajan N (2006), Can threshold networks be trained directly?, IEEE Trans. Circuits Syst. II, 53 (3), pp.187–191.

      [3] Huang GB, Zhu QY, & Siew CK (2006), Extreme Learning Machine: theory and applications, Neurocomputing, 70 (1), pp.489–501.

      [4] Huang GB, Zhou H, Ding X, & Zhang R (2012), Extreme Learning Machine for regression and multiclass classification, IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, 42 (2), pp.513–529.

      [5] Huang GB, & Chen L (2007), Convex incremental Extreme Learning Machine, Neurocomputing, 70 (168), pp.3056–3062.

      [6] Huang GB, & Chen L (2008), Enhanced random search based incremental Extreme Learning Machine, Neurocomputing, 71 (16), pp.3460–3468.

      [7] Yap KS, & Yap HJ (2012), Daily Maximum Load Forecasting of Consecutive National Holidays using OSELM-Based Multi-Agents System with Average Strategy, Neurocomputing, 81, pp.108-112.

      [8] Huang G, Huang GB, Song S, & You K (2015), Trends in extreme learning machine: A review. Neural Networks, 61, pp.32-48.

      [9] Liu X, Lin S, Fang J, & Xu Z (2015), Is extreme learning machine feasible? A theoretical assessment (part I). IEEE Transactions on Neural Networks and Learning Systems, 26 (1), pp.7-20.

      [10] Zhou Y, Peng J, & Chen CP (2015), Extreme learning machine with composite kernels for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), pp.2351-2360.

      [11] You ZH, Lei YK, Zhu L, Xia J, & Wang B (2013), Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis. BMC Bioinformatics, 14(8), p.S10.

      [12] Song Y, Crowcroft J, & Zhang J (2012), Automatic epileptic seizure detection in EEGs based on optimized sample entropy and extreme learning machine. Journal of neuroscience methods, 210 (2), pp.132–146.

      [13] Yang Y, Wang Y, & Yuan X (2012), Bidirectional extreme learning machine for regression problem and its learning effectiveness. IEEE Transactions on Neural Networks and Learning Systems, 23(9), pp.1498– 1505.

      [14] Yan Z, & Wang J (2014), Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks. IEEE Transactions on Neural Networks and Learning Systems, 25(3), pp.457–469.

      [15] Zhang Y, & Zhang P (2011), Optimization of nonlinear process based on sequential extreme learning machine. Chemical Engineering Science, 66(20), pp.4702–4710.

      [16] Nizar A, Dong Z, & Wang Y (2008), Power utility nontechnical loss analysis with extreme learning machine method. IEEE Transactions on Power Systems, 23(3), pp.946–955.

      [17] Minhas R, Mohammed AA, & Wu QMJ (2012), Incremental learning in human action recognition based on snippets. IEEE Transactions on Circuits and Systems for Video Technology, 22(11), pp.1529– 1541.

      [18] Zhao G, Shen Z, Miao C, & Gay R (2008), Enhanced Extreme Learning Machine with stacked generalization. in: Proceedings of the IEEE International Joint Conference on Neural Networks, pp.1191–1198.

      [19] Sun ZL, Choi TM, Au KF, & Yu Y (2008), Sales forecasting using Extreme Learning Machine with applications in fashion retailing. Decision Support Systems, 46 (1), pp.411–419.

      [20] Lan Y, Soh YC, & Huang GB (2009), Ensemble of online sequential Extreme Learning Machine. Neurocomputing, 72 (135), pp.3391–3395.

      [21] Heeswijk MV, Miche Y, Lindh-Knuutila T, Hilbers P, Honkela T, Oja E, & Lendasse A (2009), Adaptive ensemble models of extreme learning machines for time series prediction. in: Proceedings of the 19th International Conference on Artificial Neural Networks: Part II, Vol. 5769 of ICANN’09, Springer-Verlag, Berlin, Heidelberg, pp.305–314.

      [22] Heeswijk MV, Miche Y, Oja E, & Lendasse A (2011), GPU-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing, 74 (16), pp.2430–2437.

      [23] Quteishet A, Lim CP, Tweedale J, & CJain L (2009), A Neural Network-based Multi-agent Classifier System, Neurocomputing, 72, pp.1639-1647.

      [24] Ossowski S, Fernandez A, Serrano JM, Hernandez JZ, Garcia-Serrano AM, Perez-de-la-Cruz JL, Belmonte MV, & Maseda JM (2004), Designing Multi agent Decision Support System the Case of Transportation Management. in: The 3rd International Joint Conference on Autonomous Agents and Multi agent Systems, vol.3 pp.1470-1471.

      [25] Tolk A (2005), An Agent-Based Decision Support System Architecture for the Military Domain. in: G. Phillips-Wren and L. Jain, (Eds.) Intelligent Decision Support Systems in Agent-Mediated Environments, IOS Press.

      [26] Hudson DL, & Cohen ME (2002), Use of Intelligent Agents in the Diagnosis of Cardiac Disorders. Computers in Cardiology, pp.633-636.

      [27] Ossowski S, Hernandez JZ, Iglesias CA, & Ferndndez A (2002), Engineering Agent Systems for Decision Support. in: The 3rd International Workshop Engineering Societies in the Agents World, pp.184-198.

      [28] Gwebu K, Wang J, & Troutt MD (2005), Constructing a Multi-Agent System: An Architecture for a Virtual Marketplace. in: G. Phillips-Wren and L. Jain, (Eds.) Intelligent Decision Support Systems in Agent-Mediated Environments, IOS Press.

      [29] Singh R, Salam A, & Lyer L (2003), Using Agents and XML for Knowledge Representation and Exchange: An Intelligent Distributed Decision Support Architecture. in: The 9th Americans Conference on Information Systems, pp.1854-1863.

      [30] Cao JW, Lin ZP, Huang GB, & Liu N (2012), Voting based Extreme Learning Machine, Information Sciences, 185, pp.66–77.

      [31] Marom ND, Rokach L, Shmilovici A (2010), Using the confusion matrix for improving ensemble classifiers, Electrical and Electronics Engineers in Israel 26th (IEEEI), pp.555-559.

      [32] Chan PK, & Stolfo SJ (1993), Experiments on multistrategy learning by metalearning. in: Proceedings of the Second International Conference on Information and Knowledge Management, pp.314–323.

      [33] Guo Y, R¨uger SM, Sutiwaraphun J, & Forbes-Millott J (1997), Meta-learning for parallel data mining. in: Proceedings of the Seventh Parallel Computing Workshop, pp.1-2.

      [34] Liao SZ, & Feng C (2014), Meta-ELM: ELM with ELM Hidden Nodes. Neucomputing, 128, pp.81-87.

      [35] Liang NY, Huang GB, Saratchandran P, & Sundararajan N (2006), A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks. Neural Networks, 17(6), pp.1411-1423.

      [36] Quteishat AMA, & Lim CP (2008), A Modified Fuzzy Min-max Neural Network with Rule Extraction And Its Application To Fault Detection and Classification. Journal of Applied Soft Computing, 8(2), pp.985-995.

      [37] Tan SC, & Lim CP (2004), Application of An Adaptive Neural Network With Symbolic Rule Extraction To Fault Detection And Diagnosis An A Power Generation Plant. IEEE Trans. Energy Conversion, 19(2), pp.369–377.

      [38] Abdul Aziz, NLA, Yap KS, Afif Bunyamin M (2013), A hybrid fuzzy logic and extreme learning machine for improving efficiency of circulating water systems in power generation plant. IOP Conf. Series: Earth and Environmental Science, 16(1), p.012102.

      [39] Tan SC, Lim CP, & Rao MVC (2007), A Hybrid Neural Network Model for Rule Generation and its Application to Process Fault Detection and Diagnosis. Engineering Applications of Artificial Intelligence, 20, pp.203–213.

      [40] Yap KS, Lim CP, & Au MT (2011), Improved GART Neural Network Model for Pattern Classification and Rule Extraction with Application to Power Systems. IEEE Trans. Neural Networks, 22(12), pp.2310-2323.

      [41] Wong KP (1993), Artificial Intelligence and Neural Network Applications in Power Systems. IEEE 2nd International Conference on Advances in Power System Control, Operation and Management, Hong Kong, pp.37-46.

      [42] Large-scale blackout in Malaysia (1996). in Kaigai Denryoku (Foreign Power): Japan Electric Power Information Center, Inc., pp.103–104.

      [43] Inoue T, Sudo Y, Takeuchi A, Mitani Y, & Nakachi Y (1999), Development of a combined cycle plant model for power system dynamic simulation studies. Trans. Inst. Elect. Eng. Jpn., 119-B, (7), pp.788–797.

      [44] Suzaki S, Kawata K, Sekoguchi M, & Goto M (2000), Combined cycle plant model for power system dynamic simulation study. Trans. Inst. Elect. Eng. Jpn., 120-B (8/9), pp.1146–1152.

      [45] Kunitomi K, Kurita A, Okamoto H, Tada Y, Ihara S, Pourbeik P, Price WW, Leirbukt AB, & Sanchez-Gasca JJ (2001), Modeling frequency dependency of gas turbine output. Proc. IEEE/Power Eng. Soc. Winter Meeting, vol.2, pp.678-683.

      [46] Rowen WI (1983), Simplified mathematical representations of heavy-duty gas turbines. Trans. Amer. Soc. Mech. Eng., 105, pp.865–869.

      [47] de Mello FP, & Ahner DJ (1994), Dynamic models for combined cycle plants in power system studies. IEEE Trans. Power Syst., 9, pp.1698–1708.

      [48] Yaw CT, Namas Khan NR, Ungku Amirulddin UA, Hashim AH, & Harun MN (2009), Development of Gas Turbines Model in MATLAB to Investigate Response in the Event of Major System Contingencies. PowerTech 2009, Bucharest, Romania.

      [49] PSS/Eâ„¢ 26, Program Application Guide: Volume II, December 1998, 1990-1998 Power Technologies, Inc.

  • Downloads

  • How to Cite

    Yaw, C. T., Wong, S. Y., Yap, K. S., & Tan, C. H. (2018). Extreme Learning Machine Neural Networks for Multi-Agent System in Power Generation. International Journal of Engineering & Technology, 7(4.35), 347-353. https://doi.org/10.14419/ijet.v7i4.35.22760