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Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)
Author(s) -
Naderi Akhormeh Alireza,
Roshanian Jafar,
MoradiMaryamnegari Hoomaan,
Khoshnood Abdol Majid
Publication year - 2019
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3009
Subject(s) - computer science , artificial intelligence , convergence (economics) , nonlinear system , artificial neural network , control theory (sociology) , stability (learning theory) , sensitivity (control systems) , feature (linguistics) , machine learning , engineering , linguistics , philosophy , physics , control (management) , quantum mechanics , electronic engineering , economics , economic growth
Summary Recently, a computational model of Amygdala based on the brain emotional learning is presented by psychologists. This brain emotional learning model (BELM) that has a neuro‐inspired architecture is utilized to train the weights which are in Amygdala and Orbitofrontal. In this paper, unknown parameters of dynamic systems are estimated by developing the normalized BELM (NBELM). To this end, after proving the stability of the model output, the sufficient condition for weights convergence is extracted while the sensitivity analysis is applied for this model. In order to evaluate the performance of NBELM, in the first example, the matrices of a twin rotor MIMO system are estimated and compared with the equation error method (EEM). In the second example, the nonlinear model of a servomotor is utilized as a case study. In the third example, the performance of the NBELM in experimental systems is validated using a reaction wheel with a DC motor. An important feature of the brain emotional system is its fast response, leading the NBELM to have a high speed performance in estimating the parameters of dynamic systems. A few number of adjustable parameters and low computing complexity also cause the NBELM to be an appropriate method for online estimation of the unknown parameters of dynamic systems.