Abstract 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) consid- ered 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 ex- hibit 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. An Efficient Adaptive System Identification Technique Based on Wind Driven Optimization Method Rashmi Sinha*, Arvind Choubey and Santosh Kumar Mahto Department of Electronics and Communication Engineering, National Institute of Technology, Jamshedpur, India. Email: rsinha.ece@nitjsr.ac.in, achoubey.ece@nitjsr.ac.in, ec51236@nitjsr.ac.in Keywords: Signal to Noise Ratio, System Identification, Wind Driven Optimization 1. Introduction System identification is a major concern in industrial control, automation, and intelligent instrumentation and communication system areas because it is difficult to obtain effective solutions for these nonlinear dynamic systems. e problem is more aggravated when stricter design criteria has to be satisfied with no or negligible a priori knowledge of the system. It aims at estimating the parameter vector of the mathematical model that aptly describes the dynamics of the unknown system (also referred as plant) under consideration. A great benefit of identification, seldom acknowledged, is its ability to uncover shortcomings and anomalies of an unknown sys- tem by observing its input-output relation and to inspect intelligent means for controlling it 1 . It finds extensive applications in instrumentation, control, acoustic noise and vibration control, power system, telecommunication, adaptive guidance or fault tolerance etc 2-3 . e task of the identification algorithm is to adapt itself to track the vary- ing system dynamics of the plant appropriately. It will not be an exaggeration if we say that the topic of system iden- tification is the accurate estimation of the parameters of the plant which, otherwise, is not possible in real world. Existing literature has rich articles on methods for identification of unknown system parameters based *Author for correspondence Indian Journal of Science and Technology, Vol 9(38), DOI: 10.17485/ijst/2016/v9i38/94883, October 2016 ISSN (Print) : 0974-6846 ISSN (Online) : 0974-5645