ANFIS models for prediction of biodiesel fuels cetane number using desirability function

Mostafaei, Mostafa

doi:10.1016/j.fuel.2017.12.025

Cited by 56 publications

(25 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They are the root mean square error (RMSE) and mean square error (MSE). These statistical criteria are used to indicate the accuracy of marine weather prediction [3]. The formula of RMSE and MSE are as follow:…”

Section: Discussionmentioning

confidence: 99%

“…Based on the researches that have been developed, maritime weather predictions made only using the model ANFIS design grid partition (genfis1) [5]. Whereas in designing ANFIS models, there are three fuzzy inference system structures namely grid partition (genfis1 function), subtractive clustering (genfis2 function) and fuzzy c-means (FCM or genfis3 function) [3] [8] [9] [10]. The three FIS structures are quite significant to determine the accuracy of ANFIS models [3] because the FIS structure is used to train and test data on ANFIS.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal ANFIS Model for Forecasting System Using Different FIS

Adyanti¹,

Novitasar²,

Asyhar³

et al. 2018

EECSI

View full text Add to dashboard Cite

Adaptive Network Based Fuzzy Inference System (ANFIS) with time series analysis is one of the intelligent systems that can be used to predict with good accuracy in all fields like in meteorology's field. However, some researches on prediction or forecasting do not emphasize the structure of the ANFIS fuzzy inference system. Thus, in this research, the optimization of the ANFIS model for forecasting maritime weather was carried out by analyzing the exact initialization determination in the three ANFIS fuzzy inference structures, they are grid partition, subtractive clustering, and fuzzy cmeans clustering. The input variable used in this research is each variable for the previous two hours, one hour, and at a time and the output of this system is the prediction of one hour, six hours, twelve hours and one day ahead of the ocean current speed (cm/s) and wave height (m) using all three FIS ANFIS approaches. Based on the results, the smallest goal error (RMSE and MSE) of the three FIS ANFIS approaches used for predicting the current speed and wave height, the best model is produced by Subtractive Clustering. It can be seen that Subtractive Clustering produces the smallest RMSE and MSE error values.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimal ANFIS Model for Forecasting System Using Different FIS

Adyanti¹,

Novitasar²,

Asyhar³

et al. 2018

EECSI

View full text Add to dashboard Cite

show abstract

“…In this layer applied AND fuzzy logic as a function node, the output node is as in (5). n9 = min (n1, n3) n10 = min (n2, n4) n11 = min (n5, n7) n12 = min (n6, n8)…”

Section: Forward Passmentioning

confidence: 99%

“…The estimated earthquake parameters of this bvalue as a strong earthquake precursor based on time series data, without taking into account the characteristics of other earthquake physical parameters. Some research on ANFIS for prediction can be mentioned as follows: [4][5][6][7]. Likewise, some ELM applications for prediction can be read in the following references: [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Earthquake Prediction System using Neuro-Fuzzy and Extreme Learning Machine

Rahmat¹,

Afiadi²,

Joelianto³

2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

View full text Add to dashboard Cite

Knowledge of earthquake predictions is very important, especially to recognize patterns of occurrence. This paper proposes an earthquake prediction system, in the form of b-value predictions as parameters that indicate the potential for earthquakes. The methods used are neuro-fuzzy with ANFIS structure and Extreme Learning Machine (ELM). From the experimental results, it shows that the ELM method has better performance than Neuro-fuzzy with ANFIS structure.

show abstract

“…Mostafaei et al [17] evaluate and compare the prediction and simulating efficiency of the response surface methodology and adaptive neurofuzzy inference system. Mostafaei [18] designed ANFIS models and generated three fuzzy inference systems structures: grid partition, subtractive clustering, and fuzzy c-means, which are to predict the cetane number of biodiesel. Aghbashlo et al [19] interfaced three different fuzzy optimization methods with the adaptive neurofuzzy inference system as a modeling system.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Oilfield‐Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment

Chen

Liu

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

A novel modified adaptive neurofuzzy inference system with smoothing treatment (MANFIS) is proposed. The MANFIS model considered the smoothing treatment of initial data basing on the adaptive neurofuzzy inference system, and we used it to predict oilfield-increased production under the well stimulation. Numerical experiments show the prediction result of the novel considering smoothing treatment is better than that without smoothing treatment. This study provides a novel and feasible method for prediction of oilfield-increased production under well stimulation, and it can be helpful in the further study of oilfield development measure planning.

show abstract

ANFIS models for prediction of biodiesel fuels cetane number using desirability function

Cited by 56 publications

References 33 publications

Optimal ANFIS Model for Forecasting System Using Different FIS

Optimal ANFIS Model for Forecasting System Using Different FIS

Earthquake Prediction System using Neuro-Fuzzy and Extreme Learning Machine

Prediction of Oilfield‐Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment

Contact Info

Product

Resources

About