Preprint Article Version 1 This version is not peer-reviewed

System Identification in the Delta Domain: A Unified Approach Using FAGWO Algorithm

Version 1 : Received: 30 September 2018 / Approved: 30 September 2018 / Online: 30 September 2018 (09:32:29 CEST)

How to cite: Ganguli, S.; Kaur, G.; Sarkar, P. System Identification in the Delta Domain: A Unified Approach Using FAGWO Algorithm. Preprints 2018, 2018090606 (doi: 10.20944/preprints201809.0606.v1). Ganguli, S.; Kaur, G.; Sarkar, P. System Identification in the Delta Domain: A Unified Approach Using FAGWO Algorithm. Preprints 2018, 2018090606 (doi: 10.20944/preprints201809.0606.v1).

Abstract

The identification of linear dynamic systems with static nonlinearities in the delta domain has been presented in this paper applying a firefly based hybrid meta-heuristic algorithm integrating Firefly algorithm (FA) and Gray wolf optimizer (GWO). FA diversifies the search space globally while GWO intensifies the solutions through its local search abilities. A test system with continuous polynomial nonlinearities has been considered for hammerstein and wiener system identification in continuous, discrete and delta domain. Delta operator modelling unifies system identification of continuous-time systems with discrete domain at higher sampling frequency. Pseudo random binary sequence, contaminated with white noise, has been taken up as the input signal to estimate the unknown model parameters as well as static nonlinear coefficients. The hybrid algorithm not only outperforms the parent heuristics of which they are constituted but also proves better as compared to some standard and latest heuristic approaches reported in the literature.

Subject Areas

system idenfication; delta operator modelling; firefly algorithm gray wolf optimizer (FAGWO)

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