Uncertain Data Modeling Based on Evolving Ellipsoidal Fuzzy Information Granules

Cordovil, Luiz A. Q.; Coutinho, Pedro Henrique Silva; Bessa, Iury; D’Angelo, Marcos Flávio Silveira Vasconcelos; Palhares, Reinaldo M.

doi:10.1109/tfuzz.2019.2937052

Cited by 17 publications

(13 citation statements)

References 38 publications

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“…For the sake of simplicity, the elements of  are denoted by z k = (u k , y k ) ∈ R n+m . Given the observations of z k , the EEFIG 35 is used to estimate an evolving fuzzy model. The EEFIG model is composed by a collection of granules…”

Section: Preliminariesmentioning

confidence: 99%

“…40 The tracker separation verification is made considering the c-separation condition. 35 In this sense, if the Tracker is c-separated from all the existing granule prototypes, a new granule is created considering data samples that do not reach L1, during a certain discrete time interval (T s ). The condition is verified as follows…”

Section: Similaritymentioning

confidence: 99%

“…from convexity property of the normalized membership functions in (35), inequalities in (47) implies that…”

Section: Lmi-based Etc Designmentioning

confidence: 99%

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Learning event‐triggered control based on evolving data‐driven fuzzy granular models

Cordovil

Coutinho

Bessa

et al. 2022

Intl J Robust & Nonlinear

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This article proposes a data-stream-driven event-triggered control strategy using evolving fuzzy models learned by granulation of input-output samples of nonlinear systems with unknown time-varying dynamics. The evolving fuzzy model is obtained online from a data stream ensuring data coverage based on the principle of justifiable granularity and controlled by an event-triggering learning mechanism dependent on the model accuracy. This evolving fuzzy model is used to design event-triggered fuzzy controller to stabilize networked control systems while reducing the used communication resources. The event-triggered learning mechanism is employed to determine the instants in which the event-triggered fuzzy controller should be redesigned. Numerical examples illustrate the effectiveness of the proposed learning event-triggered fuzzy control algorithm.

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Section: Preliminariesmentioning

confidence: 99%

Section: Similaritymentioning

confidence: 99%

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Learning event‐triggered control based on evolving data‐driven fuzzy granular models

Cordovil

Coutinho

Bessa

et al. 2022

Intl J Robust & Nonlinear

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“…Evolving fuzzy systems (eFS) [18] are universal approximators whose parameters and rule-based structure are updated from never-ending data streams, potentially subject to changes. eFS have been effectively employed in systems identification [5], filtering [22], prediction [8] [16], missing data handling [11], classification [2] [25], image recognition [12], fault detection [14] [19], fault prognostics [6] [7], and robust control [17] [23], to mention some.…”

Section: Introduction 1contextualizationmentioning

confidence: 99%

Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model

Leite¹,

Coutinho²,

Bessa³

et al. 2021

Preprint

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We present a method for incremental modeling and time-varying control of unknown nonlinear systems. The method combines elements of evolving intelligence, granular machine learning, and multi-variable control. We propose a State-Space Fuzzy-set-Based evolving Modeling (SS-FBeM) approach. The resulting fuzzy model is structurally and parametrically developed from a data stream with focus on memory and data coverage. The fuzzy controller also evolves, based on the data instances and fuzzy model parameters. Its local gains are redesigned in real-time -whenever the corresponding local fuzzy models change -from the solution of a linear matrix inequality problem derived from a fuzzy Lyapunov function and bounded input conditions. We have shown one-step prediction and asymptotic stabilization of the Henon chaos.

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“…A way of tackling the stationarity assumption is to develop strategies based on multiple models [7]. The multiple model strategy can be automated by developing evolving models, whose knowledge-base is built based on data streams, allowing the learning of complex behaviors and novelties from scratch [6]. The ability to model complex nonlinear dynamics in non-stationary environments places the Evolving Fuzzy Systems (EFSs) as interesting choices for prognostics applications in cases where it is rough to represent or describe time-varying and nonlinear characteristics of a system.…”

Section: Introductionmentioning

confidence: 99%

Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics

Camargos¹,

Bessa²,

Junior³

et al. 2021

Preprint

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This paper addresses the use of data-driven evolving techniques applied to fault prognostics. In such problems, accurate predictions of multiple steps ahead are essential for the Remaining Useful Life (RUL) estimation of a given asset. The fault prognostics' solutions must be able to model the typical nonlinear behavior of the degradation processes of these assets, and be adaptable to each unit's particularities. In this context, the Evolving Fuzzy Systems (EFSs) are models capable of representing such behaviors, in addition of being able to deal with nonstationary behavior, also present in these problems. Moreover, a methodology to recursively track the model's estimation error is presented as a way to quantify uncertainties that are propagated in the long-term predictions. The wellestablished NASA's Li-ion batteries data set is used to evaluate the models. The experiments indicate that generic EFSs can take advantage of both historical and stream data to estimate the RUL and its uncertainty.

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Uncertain Data Modeling Based on Evolving Ellipsoidal Fuzzy Information Granules

Cited by 17 publications

References 38 publications

Learning event‐triggered control based on evolving data‐driven fuzzy granular models

Learning event‐triggered control based on evolving data‐driven fuzzy granular models

Incremental Learning and State-Space Evolving Fuzzy Control of Nonlinear Time-Varying Systems with Unknown Model

Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics

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