Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions

Salleh, Mohd Najib Mohd; Talpur, Noureen; Hussain, Kashif

doi:10.1007/978-3-319-61845-6_52

Cited by 82 publications

(38 citation statements)

References 17 publications

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“…Salleh et al 2017 [13], proposed an Adaptive neuro-fuzzy inference system (ANFIS) for estimation model that yields results approximately with high degree of accuracy in fields such as transportation, engineering and medicine. However, a limitation of ANFIS is high computational cost due to complex structures.…”

Section: Related Workmentioning

confidence: 99%

“…A test case from the NHTSA shows that texting for a period of 5 seconds is equivalent to driving at 55 miles per hour (mph) across an entire length of a football field with one's eyes closed [20]. Salleh et al [13] and Dobbins and Fairclough [14], stated that 28% of teens admitted to using their mobile devices while driving and that this adversely reduced their driving ability. Their report further stated that 52% said texting at wheel is less common but that they talked on a cell phone while driving.…”

Section: ) Textingmentioning

confidence: 99%

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A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

Fasanmade

Al-Bayatti

et al. 2020

IEEE Access

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Detecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi-autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers' activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi-class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver's distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context. INDEX TERMS Fuzzy logic systems, driver distraction, severity level, ADAS, image processing, dynamic Bayesian.

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Section: Related Workmentioning

confidence: 99%

Section: ) Textingmentioning

confidence: 99%

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

Fasanmade

Al-Bayatti

et al. 2020

IEEE Access

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“…Where is the normalized firing strength from layer 3, and { , } is the parameter set of this node which are all consequent parameters. Nodes in Layer 5 perform defuzzification of consequent part of rules by summing outputs of all the rules as shown in equation 8 [26].…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System

Temitope¹,

Kingsley²,

Chidinma³

2020

CAE

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This paper aimed at developing a model that will aid the process of identifying and extracting earth surface features from satellite images using adaptive neuro-fuzzy inference system. Conventional methods of classifying earth features (Normalized Difference Vegetation Index, NDVI, and Normalized Difference Water Index, NDWI) were first used to generate the data for the training of the ANFIS model using the three bands in Landsat 8 (band 2: Blue, band 4: Red and band 5: NIR). The performance of the developed ANFIS model was validated using four different satellite images and the results compared with the conventional classification methods. An accuracy level of 98.66-99.88 % with a RMSE of 0.0218-0.0506 were obtained.

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“…Overall, the DWA behavior can be adapted to the scenario conditions with fuzzy logic rules. In recent decades, however, fuzzy logic has been improved in learning capabilities, and is known as neuro-fuzzy, as Salleh [23] mentioned. In 1993, an adaptive neuro-fuzzy inference system (ANFIS) was introduced by Jang [24].…”

Section: Of 19mentioning

confidence: 99%

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

et al. 2019

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The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the α D , β D , and γ D parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.

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Adaptive Neuro-Fuzzy Inference System: Overview, Strengths, Limitations, and Solutions

Cited by 82 publications

References 17 publications

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

Adaptive Neuro-fuzzy Inference System based Earth Surface Features Classification System

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

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