ANFIS: Adaptive Neuro-Fuzzy Inference System- A Survey

Walia, Navneet; Singh, Harjit; Sharma, Anurag

doi:10.5120/ijca2015905635

Cited by 175 publications

(92 citation statements)

References 20 publications

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“…The grey fuzzy neural network is newly designed with the aid of [17] grey wolf Optimizer (GWO) and adaptive neuro-fuzzy inference system (ANFIS) [18]. In ANFIS, the training algorithm is altered with the grey wolf optimizer algorithm.…”

Section: Proposed Grey Fuzzy Neural Networkmentioning

confidence: 99%

“…Initially, the premise parameters are optimized to fix the values using GWO algorithm. Once the premise parameters are fixed, then the final output [18] is expressed by the combination of the linear parameter.…”

Section: Proposed Gfnnmentioning

confidence: 99%

“…Figure 3 shows the architecture of the ANFIS network model. In figure 3, the ANFIS [18] structure contains a five-layered structure in which g number of input data points are fed as input to determine the optimal value for missing attribute. It constitutes the antecedent and consequent parameters to train the ANFIS network and updates the input data.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…The ANFIS [18] model is sculpted by the fuzzy system and neural network in which the weight function plays a vital role in the training algorithm. The main advantage of ANFIS over other training algorithm is that has smoothness property, mitigates the search dimensions, attain more learning capability, etc.…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…The proposed GFNN method comprises of both Grey Wolf Optimizer [17] and ANFIS [18] network model. The core intent of our proposed method is to find out the optimal membership parameters.…”

Section: Proposed Gfnnmentioning

confidence: 99%

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Grey Fuzzy Neural Network-Based Hybrid Model for Missing Data Imputation in Mixed Database

Kuppusamy¹,

Paramasivam²

2017

IJIES

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Nowadays, the missing data imputation is the novel paradigm to replace with the imputed value of the missing attribute. The missing data occurs due to bias information, non-response of the system. In the medical domain, it becomes the major challenge to impute the both categorical and numerical data. In this paper, the Grey Fuzzy Neural Network is proposed for missing data imputation in the mixed database. Initially, the WLI fuzzy clustering mechanism is utilized to generate the different clusters in which the medical data are grouped together. Then, we intend to integrate the Grey Wolf Optimizer (GWO) with the ANFIS network model, termed the Grey Fuzzy Neural Network (GFNN). The proposed method is mainly used to determine the optimal parameters to design the membership function. Finally, the hybrid prediction model is used to find out the imputed data for both categorical and numerical. In the hybrid prediction model, the categorical data is then imputed by the distance measure. The experimental results are validated, and performance is analysed by metrics such as MSE and RMSE using MATLAB implementation. The outcome of the proposed GFNN attains lower 0.13 MSE, and 0.35 RMSE ensures to impute the data significantly in the missing attribute of the mixed database.

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Section: Proposed Grey Fuzzy Neural Networkmentioning

confidence: 99%

Section: Proposed Gfnnmentioning

confidence: 99%

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Section: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

“…The proposed GFNN method comprises of both Grey Wolf Optimizer [17] and ANFIS [18] network model. The core intent of our proposed method is to find out the optimal membership parameters.…”

Section: Proposed Gfnnmentioning

confidence: 99%

See 3 more Smart Citations

Grey Fuzzy Neural Network-Based Hybrid Model for Missing Data Imputation in Mixed Database

Kuppusamy¹,

Paramasivam²

2017

IJIES

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Review on remote sensing methods for landslide detection using machine and deep learning

Mohan

Singh

Kumar

et al. 2020

Trans Emerging Tel Tech

129

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Landslide, one of the most critical natural hazards, is caused due to specific compositional slope movement. In the past decades, due to inflation of urbanized area and climate change, a compelling expansion in landslide prevalence took place which is also termed as mass/slope movement and mass wasting, causing extensive collapse around the world. The principal reason for its pursuance is a reduction in the internal resistance of soil and rocks, classified as a slide, topple, fall, and flow. Slopes can be differentiated based on earth material and the nature of its movements. The downward flow of landslides occurs due to excessive rainfall, snowmelt, earthquake, volcanic eruption, and so on. This review article revisits the conventional approaches for identification of landslides, predicting future risk, associated with slope failures, followed by emphasizing the advantages of modern geospatial techniques such as aerial photogrammetry, satellite remote sensing images (ie, panchromatic, multispectral, radar images), Terrestrial laser scanning, and High‐Resolution Digital Elevation Model (HR‐DEM) in updating landslide inventory maps. Machine learning techniques like Support Vector Machine, Artificial neural network, deep learning has been extensively used with geographical data producing effective results for assessment of natural hazard/resources and environmental research. Based on recent studies, deep learning is a reliable tool addressing remote sensing challenges such as trade‐off in imaging system producing poor quality investigation, in addition, to expedite consequent task such as image recognition, object detection, classification, and so on. Conventional methods, like pixel and object‐based machine learning methods, have been broadly explored. Advanced development in deep learning technique like CNN (Convolutional neural network) has been extensively successful in information extraction from an image and has exceeded other traditional approaches. Over the past few years, minor attempts have been made for landslide susceptibility mapping using CNN. In addition, small sample sizes for training purpose will be major drawback and notably remarkable while using deep learning techniques. Also, assessment of the model's performance with diverse training and testing proportion other than commonly utilized ratio, that is, 70/30 needs to be explored further. The review article briefly highlights the remote sensing methods for landslide detection using machine learning and deep learning.

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