Model parameters' estimation is one of the most important tasks in the analysis and design process of a nonlinear dynamic system in real time, especially in the presence of noise. This article presents a novel approach in estimating important parameters of gray-box model for such a system on real nonlinear EEG to simulate efficiently the dynamic characteristics of neurons. Specifically, the proposed methodology exploits unscented Kalman filter (UKF) that is combined with chaos neural population model to formulate the interaction between the cortical areas. The proposed methodology is compared with the state-of-the-art parameters' estimation techniques to verify the efficiency of the UKF on the gray-box model. Experimental results show that the proposed method demonstrates the lowest error value of root mean square error (RMSE) among existing parameter estimation methods. The robustness of the proposed approach is further validated in its convergence and automation, with minimum error relatively than others and without any user-specified input, respectively.
|Number of pages||11|
|Journal||IEEE Transactions on Instrumentation and Measurement|
|Publication status||Published - 2020 Sept|
Bibliographical noteFunding Information:
Manuscript received August 27, 2019; revised December 15, 2019; accepted December 29, 2019. Date of publication January 17, 2020; date of current version August 11, 2020. This work was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) under Grant NRF-2018R1A2B6006046, in part by a Korea University Grant, in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2017R1A4A1015559, and in part by the Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government under Grant 2017-0-00451 (Development of BCI Based Brain and Cognitive Computing Technology for Recognizing User’s Intentions Using Deep Learning). The Associate Editor coordinating the review process was Anirban Mukherjee. (Corresponding author: Seong-Whan Lee.) Sun-Hee Kim and Seong-Whan Lee are with the Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, South Korea (e-mail: email@example.com; firstname.lastname@example.org).
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- gray-box model
- nonlinear dynamic system
- parameter estimation
- unscented Kalman filter (UKF)
ASJC Scopus subject areas
- Electrical and Electronic Engineering