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An Efficient Adaptive System Identification Technique Based on Wind Driven Optimization Method


  • Department of Electronics and Communication Engineering, National Institute of Technology, Jamshedpur, India


Objectives: This paper presents a novel methodology for estimating the parameters of an unknown system or a plant using Wind Driven Optimization (WDO) based adaptive identification model. Methods/Statistical Analysis: Significant endeavours are being made by researchers in the field of system identification towards building an adaptive identification model which can approximately track the plant dynamics. This work introduces the application of one of the most recently developed nature inspired WDO algorithm in system identification task. Findings: The performance of the introduced WDO based system identification technique is compared with few popular algorithms such as Least Mean Square (LMS), Bacteria Foraging Optimization (BFO), and Genetic Algorithm (GA). WDO based model is implemented for two sets of experiments at 10dB and 30dB signal to noise ratio. Normalised Mean Square Error (NMSE) obtained by the presented method at 10dB is as low as -14.71dB at 38 iterations as compared to -10.69dB at 350 iterations, -9.526dB at 192 iterations and -8.99dB at 18 iterations for LMS, BFO and GA based methods respectively. When Signal to Noise Ratio (SNR) considered is 30dB, NMSE obtained by WDO is -33.74dB at 14 iterations which is better than -30.15dB at 328 iterations, -28.66dB at 193 iterations and -28.09dB at 30 iterations for LMS, BFO and GA based methods respectively. The obtained results exhibit a very promising capability of WDO in tracking the unknown system parameters. Application/Improvements: The proposed adaptive identification model can be widely used in instrumentation, acoustic noise and vibration control, power system, telecommunication, adaptive guidance or fault tolerance etc.


Signal to Noise Ratio, System Identification, Wind Driven Optimization.

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  • Ljung L. System Identification- Theory for the user. Prentice-Ha ll, 1987, p. 163-73.
  • Ogata K. Discrete- time control systems. Prentice- Hall. 2nd edition, 1995, p. 1-150.
  • Dong-Hee Hong, Joon-Koo Choi, Gha-Jung Kim, Hyeong-Gyun Kim. A Study on the Size of Bore on Signal to Noise Ratio (SNR) in Magnetic Resonance Imaging (MRI). Indian Journal of Science and Technology. 2016 May; 9(20). Doi: 10.17485/ijst/2016/v9i20/94683.
  • Sayed AH. Fundamentals of adaptive filtering. Wiley. New York, 2003.
  • Haykin S. Adaptive Filter Theory. 4th Edition. Prentice Hall, 2001.
  • Mandic DP. A generalized normalized gradient descent algorithm. IEEE Signal Processing Letters. 2004; 11(2):115-18.
  • Sinha R, Choubey A, Mahto SK. A novel and efficient Hybrid Least Mean Square (HLMS) adaptive algorithm for system identification. IEEE SAI Intelligent Systems Conference. London. 2015, 1, p. 894-97.
  • Zameer Gulzar, A. Anny Leema. Proliferation of E-Learning in Indian Universities through the Analysis of Existing LMS Scenario: A Novel Approach. Indian Journal of Science and Technology. 2016 Jun; 9(21). Doi: 10.17485/ijst/2016/v9i21/95290
  • Mahalakshmi R, Suresh ESM. The Design and Development of Courseware for MCA Students through LMS. Indian Journal of Science and Technology. 2011 Jan; 4(1). Doi:10.17485/ijst/2011/v4i1/29933
  • Narendra KS, Parthasarthy K. Identification and control of dynamical systems using neural networks. IEEE Trans. Neural Networks, USA,1990; 1(1):4-27.
  • Nanda SJ, Panda G, Manjhi B. Improved identification of nonlinear dynamic systems using artificial immune system. Annual IEEE India Conference INDICON, 2008, p. 268-73.
  • Patra JC, Pal RN, Chaterjee BN, Panda G. Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans. on Systems, Man and Cybernetics-Part B. 1999; 29(2):254-63.
  • Sadegh N. A perceptron based neural network for identification and control of nonlinear systems. IEEE Trans. Neural Networks. 1993; 4(1):982-88.
  • Kennedy J, Eberhart RC. Particle swarm optimization. Proc. IEEE Int. Conf. on Neural Networks. 1995, p. 1942-48.
  • Majhi B, Panda G. Development of efficient identification scheme for nonlinear dynamic systems using swarm intelligence techniques. Expert Systems with Applications. Elsevier. 2010; 37(1):556-66.
  • Katari V, Malireddi S, Bendapudi SKS, Panda G. Adaptive nonlinear system identification using comprehensive learning PSO. Proc. Of third IEEE Int. Symp. On Comm., Control and Signal Processing. Malta, India, 2000, p. 434-39.
  • Tang KS, Man KF, He Q. Genetic algorithms and their applications. IEEE Signal Processing Magazine. 1996;13(6):28-46.
  • Panda G, Majhi B, Mohanty D, Choubey A. Development of GA based adaptive techniques for nonlinear system identification. Proc. Of Int. Conf. on Information Technology (CIT). India, 2005, p. 198-204.
  • Nabeel Kaghed H, Eman Al–Shamery S, Fanar Emad Khazaal Al-Khuzaie. Multiple Sequence Alignment based on Developed Genetic Algorithm. Indian Journal of Science and Technology. 2016 Jan; 9(2). Doi: 10.17485/ijst/2016/v9i2/84236
  • Sajjad Najafi, Vahid Majazi Dalfard, Ghorbanali Mohammadi. Hybrid Genetic Algorithm for Network Locating Problem by considering Multi-purpose Trip in Stochastic State. Indian Journal of Science and Technology. 2011 Sep; 4(9). Doi: 10.17485/ijst/2011/v4i9/30240.
  • Panda G, Majhi B, Mishra S. Nonlinear system identification using bacterial foraging based learning. Proc. Of third Int. Conf. on Artificial Intelligence in Engineering Technology (ICAIET). Malaysia. 2006; 177(18):120-25.
  • Wolpert DH, Macready WG. No free lunch theorems for optimizations. IEEE Trans. on Evolutionary Computation. 1997; 1(1):67-82.
  • Bayraktar Z, Muge M, Werner DH. Wind Driven Optimization (WDO): A novel nature inspired optimization algorithm and its application to electromagnetics. IEEE International Symposium on Antennas and Propagation Society, 2010, p. 1-4.
  • Bayraktar Z, Komurcu M, Bossard JA, Werner DH. The wind driven optimization technique and its application in electromagnetics. IEEE Transactions on Antennas and Propagation. 2013; 61(5):2745-57.
  • Mahto SK, Choubey A. Linear array synthesis with minimum side lobe level and null control using wind driven optimization. IEEE Int. Conf. SPACES, 2015, p. 191-95.
  • Mahto SK, Choubey A. A novel hybrid IWO/WDO algorithm for interference minimization of uniformly excited linear sparse array by position-only control. IEEE Antennas and Wireless Propagation letters. 2016; 15(1):250-54.
  • Padmavathi G, Shanmugavel S. Performance Analysis of Cooperative Spectrum Sensing Technique for Low SNR Regime over Fading Channels for Cognitive Radio Networks. Indian Journal of Science and Technology. 2015 Jul; 8(16). Doi: 10.17485/ijst/2015/v8i16/64746.


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