Modeling of Tropospheric Delays Using ANFIS

Suparta, Wayan; Alhasa, Kemal Maulana

doi:10.1007/978-3-319-28437-8

Cited by 70 publications

(37 citation statements)

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“…This mathematical model represents some very complex relationships between inputs and outputs [24]. The ANN enables to model a non-linear relationship between inputs and output datasets [25]. The ANN can give an unlimited number of model parameters whatever a complex processes are so given to a set of inputs.…”

Section: Resultsmentioning

confidence: 99%

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Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems

et al. 2019

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This article is published with open access at Springerlink.comAbstract: Subjective and objective evaluations of the handle of textile materials are very important to describe its tactile comfort for next-to-skin goods. In this paper, the applicability of artificial neural-network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) modeling approaches for the prediction of the psychological perceptions of functional fabrics from mechanical properties were investigated. Six distinct functional fabrics were evaluated using human subjects for their tactile score and total hand values (THV) using tactile and comfort-based fabric touch attributes. Then, the measurement of mechanical properties of the same set of samples using KES-FB was performed. The RMSE values for ANN and ANFIS predictions were 0.014 and 0.0122 and are extremely lower than the variations of the perception scores of 0.644 and 0.85 for ANN and ANFIS, respectively with fewer prediction errors. The observed results indicated that the predicted tactile score and THV are almost very close to the actual output obtained using the human judgment. Fabric objective measurement technology, therefore, provides reliable measurement approaches for functional fabric quality inspection, control, and design specification.

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Section: Resultsmentioning

confidence: 99%

“…The ANFIS is an adaptive network that uses supervised learning algorithm [25]. The model is working based on Sugeno [30] fuzzy inference system and designed using Matlab 20127b ® .…”

Section: Prediction Of Tactile Comfort Using Anfismentioning

confidence: 99%

Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems

et al. 2019

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“…the input layer as antecedent parameters, three hidden layers with two constant parameters and one consequent parameter, and one output layer. In more detail about the ANFIS concept, authors can refer to Suparta and Alhasa (Suparta & Alhasa, 2013;2016a;2016b, 2013Suparta & Putro, 2017). In brief, the model can be explanation as follows.…”

Section: Anfis Model Development For Rainfall Predictionsmentioning

confidence: 99%

Application of Anfis Technique for Prediction of Rainfall in the South Tangerang City

Suparta¹,

Resdiansyah²,

Jaya³

2020

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Flood phenomenon is an annual scourge that often occurs at several critical points in the territory of Indonesia, one of which is in the South Tangerang City, Banten. This paper aims to predict rainfall as an early detection model by exploring the application of artificial intelligence techniques such as the Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed technique was built with various input structures and membership functions, then trained and tested to evaluate its capabilities. Daily meteorological data such as relative humidity, temperature, and partial pressure of water vapour are chosen as intermediate parameters to predict rainfall. While rainfall measurement data is used as a comparison or validation. By analysing the three scenarios that have been carried out, it was found that rainfall can be estimated well. The prediction results with ANFIS Fuzzy-C-Means (FCM) algorithm are promising to detect floods that can later be integrated with more complex systems, especially types of floods at various points in the City of South Tangerang and surrounding areas.

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“…With the use of hybrid learning algorithm that combines with the recursive least square estimator and the gradient descent methods, it can ensure the convergence rate is faster because it can reduce the dimensional search space in the original method of back propagation. One level of hybrid learning is called epochs [NAYAK et al 2004;SUPARTA, ALHASA 2016].…”

Section: Learning Of Hybrid Modelmentioning

confidence: 99%

A hybrid model for modelling the salinity of the Tafna River in Algeria

Houari

Hartani

Rémini

et al. 2019

Journal of Water and Land Development

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In this paper, the capacity of an Adaptive-Network-Based Fuzzy Inference System (ANFIS) for predicting salinity of the Tafna River is investigated. Time series data of daily liquid flow and saline concentrations from the gauging station of Pierre du Chat (160801) were used for training, validation and testing the hybrid model. Different methods were used to test the accuracy of our results, i.e. coefficient of determination (R2), Nash–Sutcliffe efficiency coefficient (E), root of the mean squared error (RSR) and graphic techniques. The model produced satisfactory results and showed a very good agreement between the predicted and observed data, with R2 equal (88% for training, 78.01% validation and 80.00% for testing), E equal (85.84% for training, 82.51% validation and 78.17% for testing), and RSR equal (2% for training, 10% validation and 49% for testing).

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Modeling of Tropospheric Delays Using ANFIS

Cited by 70 publications

References 0 publications

Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems

Tactile Comfort Prediction of Functional Fabrics from Instrumental Data Using Intelligence Systems

Application of Anfis Technique for Prediction of Rainfall in the South Tangerang City

A hybrid model for modelling the salinity of the Tafna River in Algeria

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