A genetic-based neuro-fuzzy generator: NEFGEN

Rahmoun, Abdellatif; Berrani, Sid-Ahmed

doi:10.1109/aiccsa.2001.933946

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…In this paper, a novel neural fuzzy approach using NEFCLASS (NEuro Fuzzy CLASSification) for diagnosis of K + disturbances is proposed. The alternative neuro-fuzzy approaches are: adaptive neuro-fuzzy inference system (ANFIS), neuro-fuzzy function approximation (NEFPROX) and genetic-based neuro-fuzzy generator (NEFGEN) (Nauk, D., Klawonn & Kruse, 1997;Rahmoun & Berrani, 2001;Jang, 1993;Nauck, Nauck, & Kruse, 1995;Nauck, Klawonn, & Kruse, 1997). According to the results presented in Rahmoun and Berrani (2001), the NEFGEN has better performance than ANFIS and NEFPROX while its classification accuracy is close to that of NEFCLASS.…”

Section: Introductionmentioning

confidence: 99%

“…The alternative neuro-fuzzy approaches are: adaptive neuro-fuzzy inference system (ANFIS), neuro-fuzzy function approximation (NEFPROX) and genetic-based neuro-fuzzy generator (NEFGEN) (Nauk, D., Klawonn & Kruse, 1997;Rahmoun & Berrani, 2001;Jang, 1993;Nauck, Nauck, & Kruse, 1995;Nauck, Klawonn, & Kruse, 1997). According to the results presented in Rahmoun and Berrani (2001), the NEFGEN has better performance than ANFIS and NEFPROX while its classification accuracy is close to that of NEFCLASS. The NEFGEN has slightly better classification accuracy when it is used to classify the breast cancer cases.…”

Section: Introductionmentioning

confidence: 99%

“…The NEFGEN has slightly better classification accuracy when it is used to classify the breast cancer cases. But in case of Iris plants, the NEFCLASS has slightly better classification accuracy than the NEFGEN (Rahmoun & Berrani, 2001). Furthermore, the main advantage of NEFCLASS in comparison with the NEFGEN is that its outputs are the targeted classes while in NEFGEN the outputs are integers that represent the desired classes.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Neural Fuzzy Approach for Diagnosis of Potassium Disturbances

Amer

Amawi²,

El-Khatib

2011

International Journal of Healthcare Information Systems and Informatics

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In this paper, a neural fuzzy system for the diagnosis of potassium disturbances is presented. This paper develops an adaptive neuro-fuzzy expert system that can provide accurate diagnosis of potassium disturbances. The proposed diagnostic approach has many attractive features. First, it provides an efficient tool for diagnosis of K+ disturbances and aids clinicians, especially the non-expert ones, in providing fast and accurate diagnosis of K+ disturbances in critical time. Second, it significantly reduces the time needed to accomplish precise diagnosis of K+ disturbances and thus enhances the healthcare standards. Third, it is capable of diagnosing the different types of potassium disturbances using a hybrid neural fuzzy approach. Finally, it has good accuracy (higher than 87%), specificity (100%), and average sensitivity (83%). The performance of the proposed diagnostic system was experimentally evaluated and the achieved results confirmed that the proposed system is efficient and accurate in diagnosing K+ disturbances.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Neural Fuzzy Approach for Diagnosis of Potassium Disturbances

Amer

Amawi²,

El-Khatib

2011

International Journal of Healthcare Information Systems and Informatics

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“…In the 1990's, optimization of fuzzy system parameters was done using genetic algorithms [24]. Currently, researchers are working on a new generation of intelligent systems or hybrid systems, using genetic algorithms, artificial neural networks, fuzzy logic and other artificial intelligence techniques [25,26]. NEFGEN (Neuro Fuzzy Generator), ANFIS (Adaptive-Network-based Fuzzy Inference Systems), NEFCON (Neuro-Fuzzy Control), NEFCLASS (Neuro-Fuzzy Classification) and NEFPROX (Neuro-Fuzzy Function Approximation) and other hybrid NF (Neuro-Fuzzy) adaptive network models are successfully tested [27].…”

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confidence: 99%

Fuzzy control of a resonant vibration dryer

Sudera¹

2021

Preprint

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Fuzzy Control of a Resonant Vibration Dryer

Sudera

Liu

2003

Dynamic Systems and Control, Volumes 1 and 2

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This paper presents a fuzzy control method for a resonant vibration grain dryer. The proposed control method identifies and maintains the resonant condition for the dynamical system having constant linear stiffness, varying mass and varying damping. Both simulation results and experimental results show that the amplitude-based fuzzy control approach proposed in this paper are reliable and efficient.

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A genetic-based neuro-fuzzy generator: NEFGEN

Cited by 7 publications

References 6 publications

A Novel Neural Fuzzy Approach for Diagnosis of Potassium Disturbances

A Novel Neural Fuzzy Approach for Diagnosis of Potassium Disturbances

Fuzzy control of a resonant vibration dryer

Fuzzy Control of a Resonant Vibration Dryer

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