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Knowledge Discovery from Dynamic Data on a Nonlinear System

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DOI: 10.4236/ojapps.2015.510056    2,108 Downloads   2,354 Views  
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ABSTRACT

A method is presented for performing knowledge discovery on the dynamic data of a nonlinear system. In the proposed approach, a synchronized phasor measurement technique is used to acquire the dynamic data of the nonlinear system and a hyper-rectangular type neural network (HRTNN) is then applied to extract crisp and fuzzy rules with which to estimate the system stability. The effectiveness of the proposed methodology is verified using the dynamic data of a typical real-world nonlinear system, namely an AEP-14 bus, and the extracted rules are relating to the knowledge discovery of the stability levels for the nonlinear system. The discovered relationships among the dynamic data (i.e., the operating state), the extracted rules, and the system stability are confirmed by means of a two-stage confirmatory factor analysis.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Chang, C. (2015) Knowledge Discovery from Dynamic Data on a Nonlinear System. Open Journal of Applied Sciences, 5, 576-585. doi: 10.4236/ojapps.2015.510056.

References

[1] McClean, S., Scotney, B., Morrow, P. and Greer, K. (2005) Knowledge Discovery by Probabilistic Clustering of Distributed Databases. Data and Knowledge Engineering, 54, 189-210.
http://dx.doi.org/10.1016/j.datak.2004.12.001
[2] Cuzzocrea, A. (2010) Advanced Knowledge-Based Systems. Data and Knowledge Engineering, 69, 661-663. http://dx.doi.org/10.1016/j.datak.2010.02.001
[3] Nura, E., Mohammad, R.B., Amir-Masoud, E.M. and Vahid, K.T. (2014) Knowledge Discovery in Medicine: Current Issue and Future Trend. Expert Systems with Applications, 41, 4434-4463.
http://dx.doi.org/10.1016/j.eswa.2014.01.011
[4] Lau, R.Y.K., Li, Y., Song, D. and Kwok, R.C.W. (2008) Knowledge Discovery for Adaptive Negotiation Agents in E-Marketplaces. Decision Support Systems, 45, 310-323.
http://dx.doi.org/10.1016/j.dss.2007.12.018
[5] Nkambou, R., Fournier-Viger, P. and Nguifo, E.M. (2011) Learning Task Models in Ill-Defined Domain Using a Hybrid Knowledge Discovery Framework. Knowledge-Based Systems, 24, 176-185.
http://dx.doi.org/10.1016/j.knosys.2010.08.002
[6] Kwong, C.K., Chan, K.Y. and Tsim, Y.C. (2009) A genetic Algorithm Based Knowledge Discovery System for the Design of Fluid Dispensing Processes for Electronic Packaging. Expert Systems with Applications, 36, 3829-3838. http://dx.doi.org/10.1016/j.eswa.2008.02.041
[7] Guruler, H., Istanbullu, A. and Karahasan, M. (2010) A New Student Performance Analysing System Using Knowledge Discovery in Higher Educational Databases. Computers and Education, 55, 247-254.
http://dx.doi.org/10.1016/j.compedu.2010.01.010
[8] Chen, X. and Wang, D. (2012) Management of Geometric Knowledge in Textbooks. Data and Knowledge Engineering, 73, 43-57. http://dx.doi.org/10.1016/j.datak.2011.10.004
[9] Phadke, A.G. and Thorp, J.S. (1991) Improved Control and Protection of Power System through Synchronized Phasor Measurements. Control and Dynamic system, 43, 335-376.
[10] Phadke, A.G. (1993) Synchronized Phasor Measurements in Power Systems. IEEE Computer Applications in Power, 6, 10-15. http://dx.doi.org/10.1109/67.207465
[11] Moxley, R. (2006) Practical Application of Synchronized Phasor Measurement. 2006 Power Systems Conference: Advanced Metering, Protection, Control, Communication, and Distributed Resources, Clemson, 14-17 March 2006, 73- 82. http://dx.doi.org/10.1109/psamp.2006.285374
[12] Rasmussen, J. and Jorgensen, P. (2006) Synchronized Phasor Measurements of a Power System Event in Eastern Denmark. IEEE Transactions on Power System, 21, 278-284.
http://dx.doi.org/10.1109/TPWRS.2005.860947
[13] Leick, A. (1994) GPS Satellite Surveying. 2nd Edition, Wiley Inter-Science, Hoboken.
[14] Simpson, P.K. (1992) Fuzzy Min-Max Neural Networks. Part 1: Classification. IEEE Transactions on Neural Networks, 3, 776-786. http://dx.doi.org/10.1109/72.159066
[15] Abe, S. and Lan, M.S. (1995) Fuzzy Rules Extraction Directly from Numerical Data for Function Approximation. IEEE Transactions on System, Man and Cybernetics, 25, 119-129.
http://dx.doi.org/10.1109/21.362960
[16] Liu, X.D., Feng, X.H. and Pedrycz, W. (2013) Extraction of Fuzzy Rules from Fuzzy Decision Trees: An Axiomatic Fuzzy Sets (AFS) Approach. Data and Knowledge Engineering, 84, 1-25.
http://dx.doi.org/10.1016/j.datak.2012.12.001
[17] Su, M.C. (1993) A Neural Network Approach to Knowledge Acquisition. PhD Thesis, University of Maryland, College Park.
[18] Kline, R.B. (1998) Principles and Practice of Structural Equation Modeling. Guilford, New York.
[19] Kelloway, E.K. (1995) Structural Equation Modeling in Perspective. Journal of Organizational Behavior, 16, 215-224. http://dx.doi.org/10.1002/job.4030160304
[20] Martínez-Torres, M.R. (2006) A Procedure to Design a Structural and Measurement Model of Intellectual Capital: An Exploratory Study. Information and Management, 43, 617-626.
http://dx.doi.org/10.1016/j.im.2006.03.002
[21] Toral, S.L., Barrero, F. and Martínez-Torres, M.R. (2007) Analysis of Utility and Use of a Web Based Tool for Digital Signal Processing Teaching by Means of a Technological Acceptance Model. Computers and Education, 49, 957-975. http://dx.doi.org/10.1016/j.compedu.2005.12.003
[22] Byrne, M.B. (2005) Factor Analytic Models: Viewing the Structure of an Assessment Instrument from Three Perspectives. Journal of Personality Assessment, 85, 17-32.
http://dx.doi.org/10.1207/s15327752jpa8501_02
[23] Chang, C.S. (2011) A Matrix-Based VaR Model for Risk Identification in Power Supply Networks. Applied Mathematical Modeling, 35, 4567-4574. http://dx.doi.org/10.1016/j.apm.2011.03.032
[24] Byrne, M.B. (2001) Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Lawrence Erlbaum Associates, Mahwah.
[25] Jonathan, N. and Hancock, G.R. (2001) Performance of Bootstrapping Approaches to Model Test Statistics and Parameter Standard Error Estimation in Structural Equation Model. Structural Equation Modeling, 8, 353-377. http://dx.doi.org/10.1207/S15328007SEM0803_2
[26] Raines-Eudy, R. (2001) Using Structural Equation Modeling to Test for Differential Reliability and Validity: An Empirical Demonstration. Structural Equation Modeling, 7, 124-141.
http://dx.doi.org/10.1207/S15328007SEM0701_07

  
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