Fuzzy logic systems and medical applications

Elena Vlamou; Basil Papadopoulos; Elena Vlamou; Basil Papadopoulos

doi:10.3934/Neuroscience.2019.4.266

AIMS Neuroscience

2019, Volume 6, Issue 4: 266-272. doi: 10.3934/Neuroscience.2019.4.266

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Fuzzy logic systems and medical applications

Elena Vlamou ^,,
Basil Papadopoulos

Department of Civil Engineering, Democritus University of Thrace, Xanthi, 67100, Greece

Received: 10 July 2019 Accepted: 11 October 2019 Published: 22 October 2019

The combination of Artificial Neural Networks and Fuzzy Logic Systems enables the representation of real-world problems via the creation of intelligent and adaptive systems. By adapting the interconnections between layers, Artificial Neural networks are able to learn. A computing framework based on the concept of fuzzy set and rules as well as fuzzy reasoning is offered by fuzzy logic inference systems. The fusion of the aforementioned adaptive structures is called a “Neuro-Fuzzy” system. In this paper, the main elements of said structures are examined. Researchers have noticed that this fusion could be applied for pattern recognition in medical applications.
- fuzzy logic systems,
- neuro-fuzzy networks,
- fuzzy logic inference,
- fuzzy controllers,
- Parkinson’s disease
Citation: Elena Vlamou, Basil Papadopoulos. Fuzzy logic systems and medical applications[J]. AIMS Neuroscience, 2019, 6(4): 266-272. doi: 10.3934/Neuroscience.2019.4.266

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Abstract

The combination of Artificial Neural Networks and Fuzzy Logic Systems enables the representation of real-world problems via the creation of intelligent and adaptive systems. By adapting the interconnections between layers, Artificial Neural networks are able to learn. A computing framework based on the concept of fuzzy set and rules as well as fuzzy reasoning is offered by fuzzy logic inference systems. The fusion of the aforementioned adaptive structures is called a “Neuro-Fuzzy” system. In this paper, the main elements of said structures are examined. Researchers have noticed that this fusion could be applied for pattern recognition in medical applications.

Conflicts of interest

The authors have no conflicts of interest.

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