Two Approaches to Data-Driven Design of Evolving Fuzzy Systems: eTS and FLEXFIS

Author(s):  
P. Angelov ◽  
E. Lughofer ◽  
E.P. Klement
2010 ◽  
Vol 20 (05) ◽  
pp. 355-364 ◽  
Author(s):  
JOSE ANTONIO IGLESIAS ◽  
PLAMEN ANGELOV ◽  
AGAPITO LEDEZMA ◽  
ARACELI SANCHIS

Environments equipped with intelligent sensors can be of much help if they can recognize the actions or activities of their users. If this activity recognition is done automatically, it can be very useful for different tasks such as future action prediction, remote health monitoring, or interventions. Although there are several approaches for recognizing activities, most of them do not consider the changes in how a human performs a specific activity. We present an automated approach to recognize daily activities from the sensor readings of an intelligent home environment. However, as the way to perform an activity is usually not fixed but it changes and evolves, we propose an activity recognition method based on Evolving Fuzzy Systems.


2008 ◽  
Vol 16 (6) ◽  
pp. 1390-1392 ◽  
Author(s):  
Plamen Angelov ◽  
Dimitar Filev ◽  
Nikola Kasabov

Micromachines ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1390
Author(s):  
Khalid A. Alattas ◽  
Ardashir Mohammadzadeh ◽  
Saleh Mobayen ◽  
Ayman A. Aly ◽  
Bassem F. Felemban ◽  
...  

In this study, a novel data-driven control scheme is presented for MEMS gyroscopes (MEMS-Gs). The uncertainties are tackled by suggested type-3 fuzzy system with non-singleton fuzzification (NT3FS). Besides the dynamics uncertainties, the suggested NT3FS can also handle the input measurement errors. The rules of NT3FS are online tuned to better compensate the disturbances. By the input-output data set a data-driven scheme is designed, and a new LMI set is presented to ensure the stability. By several simulations and comparisons the superiority of the introduced control scheme is demonstrated.


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