Evolving fuzzy models for myoelectric-based control of a prosthetic hand

Precup, Radu‐Emil; Teban, Teodor-Adrian; Oliveira, Thiago Eustaquio Alves de; Petriu, Emil M.

doi:10.1109/fuzz-ieee.2016.7737670

Cited by 35 publications

(34 citation statements)

References 43 publications

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“…The paper continues the work carried out in [27] concerning the presentation of real-world applications of the evolving Takagi-Sugeno-Kang fuzzy models that describe the dynamics of nonlinear systems in crane systems [28,29], pendulum systems [30,31], prosthetic hand fingers [32] and twin rotor aerodynamic systems [33]. The main difference with respect to [27] is that this paper applies incremental online identification algorithms to the derivation of evolving Takagi-Sugeno-Kang fuzzy models for other process applications in order to characterize their dynamics.…”

Section: Introductionmentioning

confidence: 94%

“…The possible modification or upgrade of the rule base structure is carried out by means of the potential of the new data compared to the potential of the existing rules' centers. The rule base structure is modified if certain conditions mentioned in [22], [27][28][29][30][31][32][33][34] are fulfilled.…”

Section: Overview On Incremental Online Identification Algorithmsmentioning

confidence: 99%

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Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Precup¹,

Preitl²,

Bojan-Dragoş³

et al. 2017

FU Mech Eng

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Abstract. This paper presents theoretical and application results concerning the development of evolving Takagi-Sugeno-Kang fuzzy models for two dynamic systems, which will be viewed as controlled processes, in the field of automotive applications. The two dynamic systems models are nonlinear dynamics of the longitudinal slip in the Antilock Braking Systems (ABS) and the vehicle speed in vehicles with the Continuously

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Section: Introductionmentioning

confidence: 94%

Section: Overview On Incremental Online Identification Algorithmsmentioning

confidence: 99%

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Precup¹,

Preitl²,

Bojan-Dragoş³

et al. 2017

FU Mech Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…Mathematical modelling based on fuzzy sets is widely applied in the medical domain as witnessed by the large number of papers available in the literature. For instance, fuzzy nonlinear systems can be considered [17][18][19]. A general model based on the novel concept of linear Diophantine fuzzy set is developed in [20].…”

Section: Introductionmentioning

confidence: 99%

A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood

Giordani

Perna

Bianchi³

et al. 2020

PLoS ONE

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The use of mobile communication devices in health care is spreading worldwide. A huge amount of health data collected by these devices (mobile health data) is nowadays available. Mobile health data may allow for real-time monitoring of patients and delivering ad-hoc treatment recommendations. This paper aims at showing how this may be done by exploiting the potentialities of fuzzy clustering techniques. In fact, such techniques can be fruitfully applied to mobile health data in order to identify clusters of patients for diagnostic classification and cluster-specific therapies. However, since mobile health data are full of noise, fuzzy clustering methods cannot be directly applied to mobile health data. Such data must be denoised prior to analyzing them. When longitudinal mobile health data are available, functional data analysis represents a powerful tool for filtering out the noise in the data. Fuzzy clustering methods for functional data can then be used to determine groups of patients. In this work we develop a fuzzy clustering method, based on the concept of medoid, for functional data and we apply it to longitudinal mHealth data on daily symptoms and consumptions of anti-symptomatic drugs collected by two sets of patients in Berlin (Germany) and Ascoli Piceno (Italy) suffering from allergic rhinoconjunctivitis. The studies showed that clusters of patients with similar changes in symptoms were identified opening the possibility of precision medicine.

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“…The specific feature of these systems is the computation of the rule bases by a learning process, i.e. conducting continuous online rule base learning, with some recent results given in [67][68][69]. The stability analysis of systems based on AnYa and evolving fuzzy controllers is an important subject.…”

Section: Introductionmentioning

confidence: 99%

A Center Manifold Theory-Based Approach to the Stability Analysis of State Feedback Takagi-Sugeno-Kang Fuzzy Control Systems

Precup

Preitl²,

Petriu³

et al. 2020

FU Mech Eng

Self Cite

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The aim of this paper is to propose a stability analysis approach based on the application of the center manifold theory and applied to state feedback Takagi-Sugeno-Kang fuzzy control systems. The approach is built upon a similar approach developed for Mamdani fuzzy controllers. It starts with a linearized mathematical model of the process that is accepted to belong to the family of single input second-order nonlinear systems which are linear with respect to the control signal. In addition, smooth right-hand terms of the state-space equations that model the processes are assumed. The paper includes the validation of the approach by application to stable state feedback Takagi-Sugeno-Kang fuzzy control system for the position control of an electro-hydraulic servo-system.

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Evolving fuzzy models for myoelectric-based control of a prosthetic hand

Cited by 35 publications

References 43 publications

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

Automotive Applications of Evolving Takagi-Sugeno-Kang Fuzzy Models

A study of longitudinal mobile health data through fuzzy clustering methods for functional data: The case of allergic rhinoconjunctivitis in childhood

A Center Manifold Theory-Based Approach to the Stability Analysis of State Feedback Takagi-Sugeno-Kang Fuzzy Control Systems

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