scholarly journals Improved Estimation of Bio-Oil Yield Based on Pyrolysis Conditions and Biomass Compositions Using GA- and PSO-ANFIS Models

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Zhimin Li ◽  
Deyin Zhao ◽  
Linbo Han ◽  
Li Yu ◽  
Mohammad Mahdi Molla Jafari
Keyword(s):  
Experimental Tests ◽  
Optimization Methods ◽  
Oil Yield ◽  
Primary Data ◽  
Anfis Model ◽  
Inference System ◽  
Suitable Alternative ◽  
Target Values ◽  
Bio Oil ◽  
Adaptive Neurofuzzy Inference System

This paper incorporates the adaptive neurofuzzy inference system (ANFIS) technique to model the yield of bio-oil. The estimation of this parameter was performed according to pyrolysis conditions and biomass compositions of feedstock. For this purpose, this paper innovates two optimization methods including a genetic algorithm (GA) and particle swarm optimization (PSO). Primary data were gathered from previous studies and included 244 data of biodiesel oils. The findings showed a coefficient determination ( R 2 ) of 0.937 and RMSE of 2.1053 for the GA-ANFIS model, and a coefficient determination ( R 2 ) of 0.968 and RMSE of 1.4443 for PSO-ANFIS. This study indicates the capability of the PSO-ANFIS algorithm in the estimation of the bio-oil yield. According to the performed analysis, this model shows a higher ability than the previously presented models in predicting the target values and can be a suitable alternative to time-consuming and difficult experimental tests.

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.

scholarly journals Thermal Error Modelling of the Spindle Using Neurofuzzy Systems

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Jingan Feng ◽  
Xiaoqi Tang ◽  
Yanlei Li ◽  
Bao Song
Keyword(s):  
Thermal Error ◽  
Original Data ◽  
Current Sensor ◽  
Combined Model ◽  
Anfis Model ◽  
Bp Network ◽  
Inference System ◽  
Grey Models ◽  
Adaptive Neurofuzzy Inference System ◽  
Neurofuzzy Systems

This paper proposes a new combined model to predict the spindle deformation, which combines the grey models and the ANFIS (adaptive neurofuzzy inference system) model. The grey models are used to preprocess the original data, and the ANFIS model is used to adjust the combined model. The outputs of the grey models are used as the inputs of the ANFIS model to train the model. To evaluate the performance of the combined model, an experiment is implemented. Three Pt100 thermal resistances are used to monitor the spindle temperature and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that the combined model can better predict the spindle deformation compared to BP network, and it can greatly improve the performance of the spindle.

ANFIS Driven Strain Control of Thin-Shape Memory Alloy Wires Using Seebeck Voltage of a Shape Memory Alloy–Constantan Thermocouple

2015 ◽  
Vol 137 (1) ◽  
Author(s):  
V. V. N. Sriram Malladi ◽  
Pablo A. Tarazaga
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Supply Voltage ◽  
Hysteretic Behavior ◽  
Time Shift ◽  
Anfis Model ◽  
Inference System ◽  
Actuator Nonlinearities ◽  
Adaptive Neurofuzzy Inference System ◽  
The Relationship

Shape memory alloy (SMA) actuators exhibit considerable hysteresis between the supply voltage (conventionally used in resistive heating) and strain characteristics of the SMA. Hence, it is not easy to control the strain of a thin-SMA wire, unless a model is developed that can match the actuator's nonlinearities for predicting the supply voltage required by the SMA system accurately. The work presented in this paper proposes the use of a black-box technique called the adaptive neurofuzzy inference system (ANFIS) to study the hysteretic behavior of SMAs. The input parameters for such an ANFIS model would be a physical variable at time t and at a time t + n, where n is a time shift. The present work studies the effect of a time shift on the actuator nonlinearities for two ANFIS models. One of the models studies the relationship between the desired displacement of an SMA and the supply voltage across the SMA, while the other model predicts the actual displacement of an SMA from the feedback temperature. A novel SMA–Constantan thermocouple records the feedback temperature.

scholarly journals Prediction of Attendance Demand in European Football Games: Comparison of ANFIS, Fuzzy Logic, and ANN

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Mehmet Şahin ◽  
Rızvan Erol
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Optimization Methods ◽  
Ann Model ◽  
Absolute Deviation ◽  
Inference System ◽  
European Football ◽  
Artificial Neural Network Ann ◽  
Adaptive Neurofuzzy Inference System

An artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS) models, and fuzzy rule-based system (FRBS) models are developed to predict the attendance demand in European football games, in this paper. To determine the most successful method, each of the methods is analyzed under different situations. The Elman backpropagation, feed-forward backpropagation, and cascade-forward backpropagation network types are developed to determine the outperforming ANN model. The backpropagation and hybrid optimization methods are used for training fuzzy inference system (FIS) to determine the outperforming ANFIS model. The fuzzy logic model is developed after experimenting different forms of membership functions. To this end, the data of 236 soccer games are used to train the ANN and ANFIS models, and 2017/2018 season’s data of these clubs are used to test all of the models. The results of all models are compared with each other and real past data. To assess the performance of each model, two error measures that are Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) are implemented. These measures reveal that the ANN model that has Elman network type outperforms the other models. Finally, the results emphasize that the proposed ANN model can be effectively used for prediction purposes.

scholarly journals Bio-Oil Characterizations of Spirulina Platensis Residue (SPR) Pyrolysis Products for Renewable Energy Development

2020 ◽  
Vol 849 ◽  
pp. 47-52
Author(s):  
Siti Jamilatun ◽  
Aster Rahayu ◽  
Yano Surya Pradana ◽  
Budhijanto ◽  
Rochmadi ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Spirulina Platensis ◽  
Pyrolysis Temperature ◽  
Renewable Energy Sources ◽  
Fixed Bed ◽  
Fixed Bed Reactor ◽  
Oil Yield ◽  
Optimum Temperature ◽  
Pyrolysis Products ◽  
Bio Oil

Nowadays, energy consumption has increased as a population increases with socio-economic developments and improved living standards. Therefore, it is necessary to find a replacement for fossil energy with renewable energy sources, and the potential to develop is biofuels. Bio-oil, water phase, gas, and char products will be produced by utilizing Spirulina platensis (SPR) microalgae extraction residue as pyrolysis raw material. The purpose of this study is to characterize pyrolysis products and bio-oil analysis with GC-MS. Quality fuel is good if O/C is low, H/C is high, HHV is high, and oxygenate compounds are low, but aliphatic and aromatic are high. Pyrolysis was carried out at a temperature of 300-600°C with a feed of 50 grams in atmospheric conditions with a heating rate of 5-35°C/min, the equipment used was a fixed-bed reactor. The higher the pyrolysis temperature, the higher the bio-oil yield will be to an optimum temperature, then lower. The optimum temperature of pyrolysis is 550°C with a bio-oil yield of 23.99 wt%. The higher the pyrolysis temperature, the higher the H/C, the lower O/C. The optimum condition was reached at a temperature of 500°C with the values of H/C, and O/C is 1.17 and 0.47. With an increase in temperature of 300-600°C, HHV increased from 11.64 MJ/kg to 20.63 MJ/kg, the oxygenate compound decreased from 85.26 to 37.55 wt%. Aliphatics and aromatics increased, respectively, from 5.76 to 36.72 wt% and 1.67 to 6.67 wt%.

The Research of Error Compensation for the 3D Surface Measurement

2012 ◽  
Vol 482-484 ◽  
pp. 2192-2196
Author(s):  
Yuan Tian ◽  
Zi Ma ◽  
Peng Li
Keyword(s):  
Measurement Error ◽  
Fuzzy Inference System ◽  
Error Compensation ◽  
Point Cloud ◽  
Fuzzy Inference ◽  
Surface Measurement ◽  
Anfis Model ◽  
Inference System ◽  
Surface Error ◽  
3D Surface

For improving precision of 3D surface measurement equipments, which are playing important role in reverse engineering, the Adaptive Network based Fuzzy Inference System (ANFIS) is developed to reconstruct 3D surface error, and the measurement error of point cloud is compensated by the presented 3D error ANFIS model. The precision of 3D surface measurement equipments has been improved noticeably

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 Classification of Gait Patterns in Patients with Neurodegenerative Disease Using Adaptive Neuro-Fuzzy Inference System

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Qiang Ye ◽  
Yi Xia ◽  
Zhiming Yao
Keyword(s):  
Motor Neurons ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Pso Algorithm ◽  
Anfis Model ◽  
Inference System ◽  
Gait Dynamics ◽  
Double Support ◽  
Adaptive Capabilities ◽  
Neuro Fuzzy

A common feature that is typical of the patients with neurodegenerative (ND) disease is the impairment of motor function, which can interrupt the pathway from cerebrum to the muscle and thus cause movement disorders. For patients with amyotrophic lateral sclerosis disease (ALS), the impairment is caused by the loss of motor neurons. While for patients with Parkinson’s disease (PD) and Huntington’s disease (HD), it is related to the basal ganglia dysfunction. Previously studies have demonstrated the usage of gait analysis in characterizing the ND patients for the purpose of disease management. However, most studies focus on extracting characteristic features that can differentiate ND gait from normal gait. Few studies have demonstrated the feasibility of modelling the nonlinear gait dynamics in characterizing the ND gait. Therefore, in this study, a novel approach based on an adaptive neuro-fuzzy inference system (ANFIS) is presented for identification of the gait of patients with ND disease. The proposed ANFIS model combines neural network adaptive capabilities and the fuzzy logic qualitative approach. Gait dynamics such as stride intervals, stance intervals, and double support intervals were used as the input variables to the model. The particle swarm optimization (PSO) algorithm was utilized to learn the parameters of the ANFIS model. The performance of the system was evaluated in terms of sensitivity, specificity, and accuracy using the leave-one-out cross-validation method. The competitive classification results on a dataset of 13 ALS patients, 15 PD patients, 20 HD patients, and 16 healthy control subjects indicated the effectiveness of our approach in representing the gait characteristics of ND patients.

ANFIS Model for Time Series Prediction

2013 ◽  
Vol 385-386 ◽  
pp. 1411-1414 ◽  
Author(s):  
Xue Bo Jin ◽  
Jiang Feng Wang ◽  
Hui Yan Zhang ◽  
Li Hong Cao
Keyword(s):  
Time Series ◽  
Fuzzy Inference System ◽  
Prediction Error ◽  
Fuzzy Inference ◽  
Time Series Prediction ◽  
Prediction Performance ◽  
Membership Functions ◽  
Adaptive Network ◽  
Anfis Model ◽  
Inference System

This paper describes an architecture of ANFIS (adaptive network based fuzzy inference system), to the prediction of chaotic time series, where the goal is to minimize the prediction error. We consider the stock data as the time series. This paper focuses on how the stock data affect the prediction performance. In the experiments we changed the number of data as input of the ANFIS model, the type of membership functions and the desired goal error, thereby increasing the complexity of the training.

Sign in / Sign up

Export Citation Format

Share Document