Structure and Evolving Fuzzy Models for Prosthetic Hand Myoelectric-Based Control Systems

Precup, Radu‐Emil; Teban, Teodor-Adrian; Petriu, Emil M.; Albu, Adriana; Mituletu, Ion-Cornel

doi:10.1109/med.2018.8442770

Cited by 7 publications

(3 citation statements)

References 32 publications

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“…It is still challenging to create a reliable prognostic model able to achieve the requested confidence in order to be used in real clinical circumstances. Future research will deal with the combination with fuzzy modeling and the transfer of results to various nonlinear models using results from other modeling and control applications [35]- [40], fuzzy [32], [41]- [44] and other models [45]- [52] applied to the medical field, and the consideration of other nonlinear models that proved to be successful in different fields [53]- [57] including various ANN architectures [58]- [61] and optimization techniques [62]- [68].…”

Section: Discussionmentioning

confidence: 99%

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Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

Albu¹,

Precup

Teban³

2019

FU Mech Eng

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The aim of this paper is to present several approaches by which technology can assist medical decision-making. This is an essential, but also a difficult activity, which implies a large number of medical and technical aspects. But, more important, it involves humans: on the one hand, the patient, who has a medical problem and who requires the best solution; on the other hand, the physician, who should be able to provide, in any circumstances, a decision or a prediction regarding the current and the future medical status of the patient. The technology, in general, and particularly the Artificial Intelligence (AI) tools could help both of them, and it is assisted by appropriate theory regarding modeling tools. One of the most powerful mechanisms that can be used in this field is the Artificial Neural Networks (ANNs). This paper presents some of the results obtained by the Process Control group of the Politehnica University Timisoara, Romania, in the field of ANNs applied to modeling, prediction and decision-making related to medical systems. An Iterative Learning Control-based approach to batch training a feedforward ANN architecture is given. The paper includes authors’ concerns in this domain and emphasizes that these intelligent models, even if they are artificial, are able to make decisions, being useful tools for prevention, early detection and personalized healthcare.

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Section: Discussionmentioning

confidence: 99%

“…This section treats the use of neural networks in modeling the finger dynamics of an amputated hand and the control of a prosthetic hand based on the architecture specified in [6] and [32]. The inputs of the system are 8 myoelectric sensors placed:…”

Section: Artificial Neural Network Applied To Modeling Finger Dynamicsmentioning

confidence: 99%

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

Albu¹,

Precup

Teban³

2019

FU Mech Eng

Self Cite

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“…This relates them to evolving fuzzy systems. As shown in [61], the concept of evolving fuzzy systems was coined by P. Angelov back in 2001 and further developed in his later works [62][63][64][65][66]. The specific feature of these systems is the computation of the rule bases by a learning process, i.e.…”

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

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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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Intelligent Paradigms for Diagnosis, Prediction and Control in Healthcare Applications

Albu

Precup

Teban

2021

Handbook of Artificial Intelligence in Healthcare

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Structure and Evolving Fuzzy Models for Prosthetic Hand Myoelectric-Based Control Systems

Cited by 7 publications

References 32 publications

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

Results and Challenges of Artificial Neural Networks Used for Decision-Making and Control in Medical Applications

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

Intelligent Paradigms for Diagnosis, Prediction and Control in Healthcare Applications

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