Implementing ANFIS for prediction of reservoir oil solution gas-oil ratio

Zamani, Hossein; Rafiee-Taghanaki, Shahin; Karimi, Masoud; Arabloo, Milad; Dadashi, Abbas

doi:10.1016/j.jngse.2015.04.008

Cited by 39 publications

(17 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANFIS has some limitations, such as the problem of dimensionality and training complexity, which restricts some applications with large datasets. Apart from that, Zamani et al [25] explained that ANFIS has a huge generalization capability compared with other neural networks or machine learning techniques. Furthermore, Badde et al [26] compared the difference between fuzzy logic and ANFIS, concluding that ANFIS has better performance solving complex problems than the fuzzy controller.…”

Section: Of 19mentioning

confidence: 99%

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

Section: Of 19mentioning

confidence: 99%

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Such performance metrics are defined in Eq. (8) to (10), where L corresponds to the number of samples.…”

Section: Multimodal Forecasting By Neuro Fuzzy Inferencementioning

confidence: 99%

“…Indeed, ANFIS based modelling is one of the most used methods for industrial process modelling, but the risk to get trapped in a local minima during the convergence procedure must be considered. In this regard, Zamani et al,in [10], propose the use of an ANFIS modelling for complex non-linear time series forecasting. In such method, a single ANFIS model to forecast a complex non-periodic gas concentration signal is used.…”

mentioning

confidence: 99%

Multimodal Forecasting Methodology Applied to Industrial Process Monitoring

Zurita

Delgado

Carino

et al. 2018

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

“…Khotbehsara [7] applied ANFIS to predict SnO 2 performance and ZrO 2 and CaCO 3 nanoparticle solution transport and self-compression characteristics. Zamani [8] applied ANFIS to predict the ratio of diesel and gas in an oil reservoir. Selimiefendigi [9] applied ANFIS to predict the convection of internal CuO-water nanofluids through the thermal cycling of a circular cylinder.…”

Section: Introductionmentioning

confidence: 99%

Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Yeom

Kwak

2018

Symmetry

View full text Add to dashboard Cite

In this paper, we compare the predictive performance of the adaptive neuro-fuzzy inference system (ANFIS) models according to the input space segmentation method. The ANFIS model can be divided into four types according to the method of dividing the input space. In general, the ANFIS1 model using grid partitioning method, ANFIS2 model using subtractive clustering (SC) method, and the ANFIS3 model using fuzzy C-means (FCM) clustering method exist. In this paper, we propose the ANFIS4 model using a context-based fuzzy C-means (CFCM) clustering method. Context-based fuzzy C-means clustering is a clustering method that considers the characteristics of the output space as well as the input space. Here, the symmetric Gaussian membership functions are obtained by the clusters produced from each context in the design of the ANFIS4. In order to evaluate the performance of the ANFIS models according to the input space segmentation method, a prediction experiment was conducted using the combined cycle power plant (CCPP) data and the auto-MPG (miles per gallon) data. As a result of the prediction experiment, we confirmed that the ANFIS4 model using the proposed input space segmentation method shows better prediction performance than the ANFIS model (ANFIS1, ANFIS2, ANFIS3) using the existing input space segmentation method.

show abstract

Implementing ANFIS for prediction of reservoir oil solution gas-oil ratio

Cited by 39 publications

References 37 publications

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

Predictive Dynamic Window Approach Development with Artificial Neural Fuzzy Inference Improvement

Multimodal Forecasting Methodology Applied to Industrial Process Monitoring

Performance Comparison of ANFIS Models by Input Space Partitioning Methods

Contact Info

Product

Resources

About