Fault Detection and Diagnosis Using Parameter Estimation with Recursive Least Squares

Cimpoesu, Elena M.; Ciubotaru, Bogdan; Ștefănoiu, Dan

doi:10.1109/cscs.2013.35

Cited by 19 publications

(10 citation statements)

References 7 publications

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“…The way in which the mentioned components interact with one another is illustrated in Figure 1. Node outputs in Amygdala and Orbitofrontal are calculated as (2) and (3), respectively, ie, Node outputs in Amygdala and Orbitofrontal are calculated as (2) and (3), respectively, ie,…”

Section: Belm Structurementioning

confidence: 99%

“…According to (2) and (4), the values of A j cannot decrease because the process of forgetting the learned information does not occur in Amygdala but in Orbitofrontal. Rew is also a reward function which is chosen based on each problem.…”

Section: Figurementioning

confidence: 99%

“…In the computational model presented in the work of Moren and Balkenius, 18 Orbitofrontal and Amygdala have network structures for which there is a node per sensory input. Node outputs in Amygdala and Orbitofrontal are calculated as (2) and (3), respectively, ie,…”

Section: Belm Structurementioning

confidence: 99%

“…Rew is also a reward function which is chosen based on each problem. According to (2) and (4), the values of A j cannot decrease because the process of forgetting the learned information does not occur in Amygdala but in Orbitofrontal. The model output is defined as follows:…”

Section: Figurementioning

confidence: 99%

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Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Akhormeh

Roshanian

MoradiMaryamnegari

et al. 2019

Adaptive Control & Signal

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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. KEYWORDSAmygdala computing model, BEL model, dynamic system parameter estimation, online and stable estimation Parameters of a large number of industrial systems need to be monitored online for various reasons, ranging from adaptive controls 1 to fault detection systems. 2,3 As a consequence, many research studies have been devoted to introducing novel parameters identification algorithms able to insure the stability of the system. Additionally, the proposed techniques must converge to real values as fast as possible in order to be applicable to engineering systems while high accuracy in the converging process is an evitable need in any novel method. The low complexity of the new algorithm is another challenge, leading the system identification method to be implemented with less effort and become wider. For mentioned challenges to be addressed, various investigations create new frameworks in terms of problem formulation, methodology development, theoretical foundation, software tools, and hardware modules. 4Int J Adapt Control Signal Process. 2019;33:1047-1065.wileyonlinelibrary.com/journal/acs

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Section: Belm Structurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

Section: Belm Structurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

See 2 more Smart Citations

Online and stable parameter estimation based on normalized brain emotional learning model (NBELM)

Akhormeh

Roshanian

MoradiMaryamnegari

et al. 2019

Adaptive Control & Signal

View full text Add to dashboard Cite

show abstract

“…Various approaches are utilized to estimate parameters online, such as ARX [32,33], recursive least squares [34][35][36] and Kalman Filtering [37,38]. After the construction of these models, measured data is converted into a set of residuals, parameters or performance indices to enhance FDD accuracy.…”

Section: Related Workmentioning

confidence: 99%