Methodology for Evolving Fuzzy Kalman Filter Identification

Pires, Danúbia Soares; Serra, Ginalber Luiz de Oliveira

doi:10.1007/s12555-017-0503-6

Cited by 22 publications

(11 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From then on, the important position of Q-learning in model-free quadratic optimal control is established. And the Q-learning schemes for the continuous-time systems also had been proposed [9,10], without the convergence condition.…”

Section: Q-learning Methods For Model-free Control Schemesmentioning

confidence: 99%

Improved Q-Learning Method for Linear Discrete-Time Systems

2020

View full text Add to dashboard Cite

In this paper, the Q-learning method for quadratic optimal control problem of discrete-time linear systems is reconsidered. The theoretical results prove that the quadratic optimal controller cannot be solved directly due to the linear correlation of the data sets. The following corollaries have been made: (1) The correlation of data is the key factor in the success for the calculation of quadratic optimal control laws by Q-learning method; (2) The control laws for linear systems cannot be derived directly by the existing Q-learning method; (3) For nonlinear systems, there are some doubts about the data independence of current method. Therefore, it is necessary to discuss the probability of the controllers established by the existing Q-learning method. To solve this problem, based on the ridge regression, an improved model-free Q-learning quadratic optimal control method for discrete-time linear systems is proposed in this paper. Therefore, the computation process can be implemented correctly, and the effective controller can be solved. The simulation results show that the proposed method can not only overcome the problem caused by the data correlation, but also derive proper control laws for discrete-time linear systems.

show abstract

Section: Q-learning Methods For Model-free Control Schemesmentioning

confidence: 99%

Improved Q-Learning Method for Linear Discrete-Time Systems

2020

View full text Add to dashboard Cite

show abstract

“…In the last years, studies involving the integration of fuzzy systems and Kalman filters have been proposed in the literature Pires and Serra ( 2019 ), Eyoh et al. ( 2018 ).…”

Section: Related Workmentioning

confidence: 99%

A novel interval type-2 fuzzy Kalman filtering and tracking of experimental data

Gomes

Serra

2021

Evolving Systems

View full text Add to dashboard Cite

In this paper, a methodology for design of fuzzy Kalman filter, using interval type-2 fuzzy models, in discrete time domain, via spectral decomposition of experimental data, is proposed. The adopted methodology consists of recursive parametric estimation of local state space linear submodels of interval type-2 fuzzy Kalman filter for tracking and forecasting of the dynamics inherited to experimental data, using an interval type-2 fuzzy version of Observer/Kalman Filter Identification (OKID) algorithm. The partitioning of the experimental data is performed by interval type-2 fuzzy Gustafson–Kessel clustering algorithm. The interval Kalman gains in the consequent proposition of interval type-2 fuzzy Kalman filter are updated according to unobservable components computed by recursive spectral decomposition of experimental data. Computational results illustrate the efficiency of proposed methodology for filtering and tracking the time delayed state variables of Chen’s chaotic attractor in a noisy environment, and experimental results illustrate its applicability for adaptive and real time forecasting the dynamic spread behavior of novel Coronavirus 2019 (COVID-19) outbreak in Brazil.

show abstract

“…In the last years, studies involving the integration of fuzzy systems and Kalman filters have been proposed in the literature (Pires and Serra 2019 ; Eyoh et al. 2018 ; Gil et al.…”

Section: Introductionmentioning

confidence: 99%