The Prediction of Permeability in a Carbonate Reservoir Using Adaptive Neurofuzzy Inference System

2009 ◽  
Author(s):  
Y. Beiraghdar ◽  
M. Nabi-Bidhendi ◽  
C. Lucas
Keyword(s):  
Carbonate Reservoir ◽  
Inference System ◽  
Adaptive Neurofuzzy Inference System

A fuzzy sensitivity analysis approach to estimate brain effective connectivity and its application to epileptic seizure detection

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nader Moharamzadeh ◽  
Ali Motie Nasrabadi
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Effective Connectivity ◽  
Multivariate Time Series ◽  
Inference System ◽  
Eeg Data ◽  
Adaptive Neurofuzzy Inference System ◽  
Intracranial Electroencephalography ◽  
Fuzzy Network ◽  
The Brain

Abstract The brain is considered to be the most complicated organ in human body. Inferring and quantification of effective (causal) connectivity among regions of the brain is an important step in characterization of its complicated functions. The proposed method is comprised of modeling multivariate time series with Adaptive Neurofuzzy Inference System (ANFIS) and carrying out a sensitivity analysis using Fuzzy network parameters as a new approach to introduce a connectivity measure for detecting causal interactions between interactive input time series. The results of simulations indicate that this method is successful in detecting causal connectivity. After validating the performance of the proposed method on synthetic linear and nonlinear interconnected time series, it is applied to epileptic intracranial Electroencephalography (EEG) signals. The result of applying the proposed method on Freiburg epileptic intracranial EEG data recorded during seizure shows that the proposed method is capable of discriminating between the seizure and non-seizure states of the brain.

scholarly journals Prediction of Oilfield-Increased Production Using Adaptive Neurofuzzy Inference System with Smoothing Treatment

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Lin Chen ◽  
Zhibin Liu ◽  
Nannan Ma ◽  
Yi Wang
Keyword(s):  
Initial Data ◽  
Numerical Experiments ◽  
The Novel ◽  
Inference System ◽  
Feasible Method ◽  
Prediction Result ◽  
Well Stimulation ◽  
Adaptive Neurofuzzy Inference System ◽  
Oilfield Development ◽  
Better Than

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.

scholarly journals Forecasting Daily Precipitation Using Hybrid Model of Wavelet-Artificial Neural Network and Comparison with Adaptive Neurofuzzy Inference System (Case Study: Verayneh Station, Nahavand)

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Abazar Solgi ◽  
Vahid Nourani ◽  
Amir Pourhaghi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Daily Precipitation ◽  
Inference System ◽  
Short And Long Term ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Original Time ◽  
Adaptive Neurofuzzy Inference System ◽  
Precipitation Modeling

Doubtlessly the first step in a river management is the precipitation modeling over the related watershed. However, considering high-stochastic property of the process, many models are still being developed in order to define such a complex phenomenon in the field of hydrologic engineering. Recently artificial neural network (ANN) as a nonlinear interextrapolator is extensively used by hydrologists for precipitation modeling as well as other fields of hydrology. In the present study, wavelet analysis combined with artificial neural network and finally was compared with adaptive neurofuzzy system to predict the precipitation in Verayneh station, Nahavand, Hamedan, Iran. For this purpose, the original time series using wavelet theory decomposed to multiple subtime series. Then, these subseries were applied as input data for artificial neural network, to predict daily precipitation, and compared with results of adaptive neurofuzzy system. The results showed that the combination of wavelet models and neural networks has a better performance than adaptive neurofuzzy system, and can be applied to predict both short- and long-term precipitations.

scholarly journals Prediction of Surface Roughness When End MillingTi6Al4VAlloy Using Adaptive Neurofuzzy Inference System

2013 ◽  
Vol 2013 ◽  
pp. 1-12
Author(s):  
Salah Al-Zubaidi ◽  
Jaharah A. Ghani ◽  
Che Hassan Che Haron
Keyword(s):  
Surface Roughness ◽  
Metal Cutting ◽  
End Milling ◽  
Cutting Tools ◽  
Computing Methods ◽  
Milling Process ◽  
Dry Cutting ◽  
Inference System ◽  
Cutting Processes ◽  
Adaptive Neurofuzzy Inference System

Surface roughness is considered as the quality index of the machine parts. Many diverse techniques have been applied in modelling metal cutting processes. Previous studies have revealed that artificial intelligence techniques are novel soft computing methods which fit the solution of nonlinear and complex problems like metal cutting processes. The present study used adaptive neurofuzzy inference system for the purpose of predicting the surface roughness when end millingTi6Al4Valloy with coated (PVD) and uncoated cutting tools under dry cutting conditions. Real experimental results have been used for training and testing ofANFISmodels, and the best model was selected based on minimum root mean square error. A generalized bell-shaped function has been adopted as a membership function for the modelling process, and its numbers were changed from 2 to 5. The findings provided evidence of the capability ofANFISin modelling surface roughness in end milling process and obtainment of good matching between experimental and predicted results.

scholarly journals Investigations on Incipient Fault Diagnosis of Power Transformer Using Neural Networks and Adaptive Neurofuzzy Inference System

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Nandkumar Wagh ◽  
D. M. Deshpande
Keyword(s):  
Distribution System ◽  
Power Transformer ◽  
Rbf Neural Network ◽  
Correct Diagnosis ◽  
Well Being ◽  
Gas Analysis ◽  
Inference System ◽  
Training Time ◽  
Multiple Faults ◽  
Adaptive Neurofuzzy Inference System

Continuity of power supply is of utmost importance to the consumers and is only possible by coordination and reliable operation of power system components. Power transformer is such a prime equipment of the transmission and distribution system and needs to be continuously monitored for its well-being. Since ratio methods cannot provide correct diagnosis due to the borderline problems and the probability of existence of multiple faults, artificial intelligence could be the best approach. Dissolved gas analysis (DGA) interpretation may provide an insight into the developing incipient faults and is adopted as the preliminary diagnosis tool. In the proposed work, a comparison of the diagnosis ability of backpropagation (BP), radial basis function (RBF) neural network, and adaptive neurofuzzy inference system (ANFIS) has been investigated and the diagnosis results in terms of error measure, accuracy, network training time, and number of iterations are presented.

Prognostics of Machine Condition Using Energy Based Monitoring Index and Computational Intelligence

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
B. Samanta ◽  
C. Nataraj
Keyword(s):  
Computational Intelligence ◽  
Support Vector ◽  
Machine Condition ◽  
Data Set ◽  
Inference System ◽  
Training Time ◽  
Vibration Data ◽  
Monitoring Index ◽  
Improved Performance ◽  
Adaptive Neurofuzzy Inference System

A study is presented on applications of computational intelligence (CI) techniques for monitoring and prognostics of machinery conditions. The machine condition is assessed through an energy-based feature, termed as “energy index,” extracted from the vibration signals. The progression of the “monitoring index” is predicted using the CI techniques, namely, recursive neural network (RNN), adaptive neurofuzzy inference system (ANFIS), and support vector regression (SVR). The proposed procedures have been evaluated through benchmark data sets for one-step-ahead prediction. The prognostic effectiveness of the techniques has been illustrated through vibration data set of a helicopter drivetrain system gearbox. The prediction performance of SVR was better than RNN and ANFIS. The improved performance of SVR can be attributed to its inherently better generalization capability. The training time of SVR was substantially higher than RNN and ANFIS. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating feature, the level of damage or degradation, and their progression.

scholarly journals Adaptive Neurofuzzy Inference System-Based Pollution Severity Prediction of Polymeric Insulators in Power Transmission Lines

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
C. Muniraj ◽  
S. Chandrasekar
Keyword(s):  
Leakage Current ◽  
Transmission Lines ◽  
Power Transmission ◽  
Severity Index ◽  
Power Transmission Lines ◽  
Anfis Model ◽  
Inference System ◽  
Severity Prediction ◽  
Polymeric Insulators ◽  
Adaptive Neurofuzzy Inference System

This paper presents the prediction of pollution severity of the polymeric insulators used in power transmission lines using adaptive neurofuzzy inference system (ANFIS) model. In this work, laboratory-based pollution performance tests were carried out on 11 kV silicone rubber polymeric insulator under AC voltage at different pollution levels with sodium chloride as a contaminant. Leakage current was measured during the laboratory tests. Time domain and frequency domain characteristics of leakage current, such as mean value, maximum value, standard deviation, and total harmonics distortion (THD), have been extracted, which jointly describe the pollution severity of the polymeric insulator surface. Leakage current characteristics are used as the inputs of ANFIS model. The pollution severity index “equivalent salt deposit density” (ESDD) is used as the output of the proposed model. Results of the research can give sufficient prewarning time before pollution flashover and help in the condition based maintenance (CBM) chart preparation.

Discriminating Shale Layers by Pseudo CGR Logs Created Using Artificial Intelligence

2021 ◽  
Author(s):  
Saud K. Aldajani ◽  
Saud F. Alotaibi ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Artificial Intelligence ◽  
Gamma Ray ◽  
Effective Porosity ◽  
Carbonate Reservoir ◽  
Added Value ◽  
Subtractive Clustering ◽  
Neutron Density ◽  
High Porosity ◽  
Inference System ◽  
Porosity Logs

Abstract The discrimination of shale vs. non-shale layers significantly influences the quality of reservoir geological model. In this study, a novel approach was implemented to enhance the model by creating Pseudo Corrected Gamma Ray (CGR) logs using Artificial Intelligence methods to identify the thin shale beds within the reservoir. The lithology of the carbonate reservoir understudy is mostly composed of dolomite and limestone rock with minor amounts of anhydrite and thin shale layers. The identification of shale layers is challenging because of the nature of such reservoirs. The high organic content of the shales and the presence of dolomites, particularly the floatstones and rudstones, can adversely affect the log quality and interpretation and may result in inaccurate log correlations, overestimating/ underestimating Original Oil In Place (OOIP) and reservoir net pays. In such cases, Corrected Gamma Ray (CGR) curves are typically used to identify shale layers. The CGR curve response is due to the combination of thorium and potassium that is associated with the clay content. The difference between the total GR and the CGR is essentially the amount of uranium-associated organic matter. Because of the very limited number of CGR logs in this reservoir, Artificial Intelligence (AI) approach was used to identify shale volume across the entire reservoir. Synthetic CGR curves were generated for the wells lacking CGR logs using AI methods. Resistivity, Density, Neutron and total GR logs were used as inputs while CGR was set as the target. Five wells that have CGR logs were used to train the model. The created pseudo logs were then used to identify shale layers and could also be used to correct effective porosity logs. After statistical analysis of the data, two different Artificial Intelligence Techniques were tested to predict CGR logs; Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN). A Sugeno-type FIS structure using subtractive clustering demonstrated the best prediction with correlation coefficient of 0.96 and mean absolute percentage error (MAPE) of 20%. The resulting synthetic CGR curves helped identify shale layers that do not extend over the entire reservoir area and ultimately correct the effective porosity logs in the reservoir model. Porosity was primarily obtained from the neutron-density logs which results in very high porosity measurements across the shale layers. This study shows a new workflow to predict shale layers in Carbonate reservoirs. The created pseudo CGR logs would help predict shale and is an added-value data that could be incorporated into the Earth model.

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