Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Karaboğa, Derviş; Kaya, Ebubekir

doi:10.1007/s10462-017-9610-2

Cited by 490 publications

(259 citation statements)

References 230 publications

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“…Fuzzy inference systems are increasingly becoming more predominant in the area of fuzzy logic methods where 15% of the selected papers in the current SLR are related to this particular method. Fuzzy inference systems are used for the processes of mapping the inward variables to appropriate outward [21,22]. Fuzzy inference process incorporates three key concepts: the membership functions, the fuzzy set operations, and the inference procedures [23].…”

Section: Fuzzy Inference Systems (Fis)mentioning

confidence: 99%

“…The ANFIS method was proposed by Jang and its notions then were used in other fields [22]. This method works by setting a list of features by using an amalgam of learning rules which will incorporate the back-propagation incline in error digestion and a tiniest squares method.…”

Section: Anfismentioning

confidence: 99%

“…This method works by setting a list of features by using an amalgam of learning rules which will incorporate the back-propagation incline in error digestion and a tiniest squares method. This method can be implemented as the basis for creating a set of IF-THEN guidelines with suitable association of functions to brand the inward and outward variables [22]. ANFIS method has been notably successful in disease diagnosis in the past few years.…”

Section: Anfismentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

Arji

Ahmadi

Nilashi

et al. 2019

Biocybernetics and Biomedical Engineering

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Section: Fuzzy Inference Systems (Fis)mentioning

confidence: 99%

Section: Anfismentioning

confidence: 99%

Section: Anfismentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

Arji

Ahmadi

Nilashi

et al. 2019

Biocybernetics and Biomedical Engineering

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show abstract

“…Since the function and architecture of ANFIS are noted in many related articles, its function is not explained here to prevent repetition. For understanding the details of this method, interested readers are referred to the related papers [49][50][51]. ANFIS can be made by partitioning the input-output data to the rules and using the methods such as FCM.…”

Section: Adaptive Neuro-fuzzy Inference System With Fuzzy C-means Clumentioning

confidence: 99%

Prediction of Suspended Sediment Load Using Data-Driven Models

Adnan

Liang

El-Shafie

et al. 2019

Water

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Estimation of suspended sediments carried by natural rivers is essential for projects related to water resource planning and management. This study proposes a dynamic evolving neural fuzzy inference system (DENFIS) as an alternative tool to estimate the suspended sediment load based on previous values of streamflow and sediment. Several input scenarios of daily streamflow and suspended sediment load measured at two locations of China-Guangyuan and Beibei-were tried to assess the ability of this new method and its results were compared with those of the other two common methods, adaptive neural fuzzy inference system with fuzzy c-means clustering (ANFIS-FCM) and multivariate adaptive regression splines (MARS) based on three commonly utilized statistical indices, root mean square error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe efficiency (NSE). The data period covers 01/04/2007-12/31/2015 for the both stations. A comparison of the methods indicated that the DENFIS-based models improved the accuracy of the ANFIS-FCM and MARS-based models with respect to RMSE by 33% (32%) and 31% (36%) for the Guangyuan (Beibei) station, respectively. The NSE accuracy for ANFIS-FCM and MARS-based models were increased by 4% (36%) and 15% (19%) using DENFIS for the Guangyuan (Beibei) station, respectively. It was found that the suspended sediment load can be accurately estimated by DENFIS-based models using only previous streamflow data.

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“…A first order Takagi-Sugeno-Kang (TSK) adaptive neuro-fuzzy inference system (ANFIS) [13] [14] [15] was utilized. The goal is to explore a neural encoding and subsequent optimization of expert knowledge input.…”

Section: ) Anfismentioning

confidence: 99%

Improved Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks

Cannaday

Davis

Scott

2019

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium

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Here we demonstrate how Deep Neural Network (DNN) detections of multiple constitutive or component objects that are part of a larger, more complex, and encompassing feature can be spatially fused to improve the search, detection, and retrieval (ranking) of the larger complex feature. First, scores computed from a spatial clustering algorithm are normalized to a reference space so that they are independent of image resolution and DNN input chip size. Then, multi-scale DNN detections from various component objects are fused to improve the detection and retrieval of DNN detections of a larger complex feature. We demonstrate the utility of this approach for broad area search and detection of Surface-to-Air Missile (SAM) sites that have a very low occurrence rate (only 16 sites) over a ∼90,000 km 2 study area in SE China. The results demonstrate that spatial fusion of multiscale component-object DNN detections can reduce the detection error rate of SAM Sites by >85% while still maintaining a 100% recall. The novel spatial fusion approach demonstrated here can be easily extended to a wide variety of other challenging object search and detection problems in large-scale remote sensing image datasets.

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Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey

Cited by 490 publications

References 230 publications

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

Fuzzy logic approach for infectious disease diagnosis: A methodical evaluation, literature and classification

Prediction of Suspended Sediment Load Using Data-Driven Models

Improved Search and Detection of Surface-to-Air Missile Sites Using Spatial Fusion of Component Object Detections from Deep Neural Networks

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