Adaptive Neuro-fuzzy Inference System Structure Research Articles

Forecasting soybean oil extraction using cyclopentyl methyl ether through soft computing models with a density functional theory study

This work presents a thermo-statistical assessment using soft computing models to describe green soybean oil extraction by cyclopentyl methyl ether (CPME). Experimental data were collected based on an experimental factorial design and modeled by an Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS), as the empirical model was unable to accurately predict the experimental results. The ANFIS structure is related to the best statistical metrics, while the ANN achieves the best thermodynamic fit. The results suggest higher yields for higher temperatures and lower solvent-to-solid mass ratios. The extraction temperature can be significantly reduced with CPME to achieve the same yield as n-hexane. The second-order model was the most accurate (SAE = 0.1266, MSE = 5.54·10-5 and R2 = 0.9876) in representing the extraction kinetics, resulting in an extraction rate constant of 1.9782 min−1. It was noticed that small positive induced charges given by the oxygen atom of CPME could contribute to the potential of this solvent to deplete the oil matrix and that its entropy is similar to that of the n-hexane molecule. The extracted oil presented the typical constitution regarding fatty acids composition; free fatty acid, mono, di, and triacylglycerol contents; and infrared spectrum.

Predicting Australia’s Domestic Airline Passenger Demand using an Anfis Approach

Abstract The forecasting of future airline passenger demand is critical task for airline management. The objective of the present study was to develop an adaptive neuro-fuzzy inference system (ANFIS) for predicting Australia’s domestic airline passenger demand. The ANFIS model was trained, tested, and validated in the study. Sugeno fuzzy rules were used in the ANFIS structure and Gaussian membership function, and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. The results found that the mean absolute percentage error (MAPE) for the overall data set of the ANFIS model was 3.25% demonstrating that the ANFIS model has high predictive capabilities. The ANFIS model could be used in other domestic air travel markets.

Application of Adaptive Neuro–Fuzzy Inference System for Forecasting Pavement Roughness in Laos

Laos Pavement Management System (PMS) manages 7700 km of National Roads (NRs) and estimates their Maintenance and Rehabilitation (MR) needs based on assessing pavement roughness conditions. This research aims to develop two International Roughness Index (IRI) models for Double Bituminous Surface Treatment (DBST) and Asphalt Concrete (AC) pavement sections using Adaptive Neuro-Fuzzy Inference System (ANFIS). A historical database of 14 years was employed for predicting the IRI as a function of pavement age and Cumulative Equivalent Single-Axle Load (CESAL). The optimum ANFIS structure comprises a hybrid learning algorithm with six fuzzy rules of generalized bell curve membership functions (Gbellmf) for the DBST model and nine fuzzy rules of two-sided Gaussian membership functions (Gauss2mf) for the AC model. Both models used the constant membership function for the output variable (IRI). The statistical evaluation results revealed that both ANFIS models (DBST and AC) have a good prediction capacity with high values of coefficient of determination (R2 0.93 and 0.88) and low values of Mean Absolute Error (MAE 0.28 and 0.27) and Root Mean Squared Percentage Error (RMSPE 7.03 and 9.98). In addition, results revealed that ANFIS models yielded higher prediction accuracy than Multiple Linear Regression (MLR) models previously developed under the same conditions.

ANFIS Based Thermal Estimation of Ultradeep Submicron Digital Circuit Design

In this paper, the use of the Adaptive Neuro Fuzzy Inference System (ANFIS) to model the CMOS inverter is discussed as a tool for developing and simulating CMOS logic circuits at the ultradeep submicron technology node of 22nm. The ANFIS structures are built and trained using MATLAB software. The ANFIS network was trained using data obtained from the analytical model (at 298.15K and 398.15K). For training, two methodologies are used: a hybrid learning method based on back-propagation and least-squares estimation, and back-propagation. The effect of the ANFIS model's structure on the accuracy and performance of the CMOS inverter has also been investigated. Further, simulation through HSPICE using (Predictive Technology Model) PTM nominal parameters has been done to compare with ANFIS (trained using an analytical model) results. The comparison of ANFIS and HSPICE suggests the ANFIS modelling procedure's practicality and correctness. The findings demonstrate that the ANFIS simulation is significantly faster and more comparable than the HSPICE simulation and that it can be easily integrated into software tools for designing and simulating complicated CMOS logic circuits.

Classification of Teachers and Lecturers Engagement on Webinar during the Pandemic using the Utilization of Big Data

The Pandemic had a big impact on education in Indonesia and also in the world. In early 2020, during this pandemic, face-to-face meetings have turned into virtual or online meetings for both the learning process and seminars or workshops. The rapid development of technology supports this change in the world of education, this can be seen from the number of online seminars conducted to improve the competence of lecturers or teachers. The development of this online seminar allows the circulation of information that is increasingly large, fast, and almost unlimited by time and space. This causes a large amount of information to be scattered in the virtual world in various fields. With this very fast information technology, trillions of bytes of data are created every day from various sources such as on social media, especially those related to applications that are often used in website-based seminar media. This is called unstructured big data. In this study, big data will be implemented to classify educators' engagement of online seminar participants during an early pandemic. The activity stages in big data management and data processing support are acquired, accessed, analytical, and applied. The method for this study is the Adaptive Neuro-Fuzzy Inference System (ANFIS) to classify the engagement of teachers and lecturers an online seminar. The results of the training error obtained from ANFIS are 0.273482 with the ANFIS structure 4-12-12-12-1 or 4 inputs, 12 hidden layers, and 1 output.

A Novel FMEA Model Using Hybrid ANFIS–Taguchi Method

Failure mode and effects analysis (FMEA) is a useful method to analyze and then prioritize failure, but it has many drawbacks. First of them is risk factors, severity, occurrence and detection, which are considered equally important but their scores may be not equal in real-life applications. Another is that the risk factor values of risk priority number of failures are usually assessed by team member in FMEA method in incomplete information and uncertainty situations. The last one is expert’s experience which is not incorporated in effective automation of the risk assessment. In this study, it is aimed to use adaptive neuro-fuzzy inference system (ANFIS) that is a soft computing method to eliminate these drawbacks. However, there are many numbers of parameters that affect the accuracy of the prediction in ANFIS structure and training phase of the model. For this purpose, the parameter values were determined using Taguchi method. A novel FMEA model using hybrid ANFIS–Taguchi method and FMEA model using ANN were applied in furniture manufacturing, and the results were compared with traditional FMEA. The accuracy of the novel FMEA model was 100% while the FMEA–ANN model was 94.118%. It is recommended to use the novel FMEA model because this model is used with insufficient and imprecise data and needs only one expert.

A novel ANFIS-based MPPT controller for two-switch flyback inverter in photovoltaic systems

Solar energy is recognized as one of the most popular renewable energy sources due to its availability around the world. Because of the intermittent generation of solar power, it is vital to extract the maximum power available from solar panels all day long. For this purpose, Maximum Power Point Tracking (MPPT) techniques are investigated in several papers. In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller with just one current sensor has been proposed to achieve that intent. To extract the maximum power from the solar panel, a DC-AC isolated two-switch flyback inverter is connected between the solar panel and the load. To transfer the maximum power to the load, the duty cycle of this converter must be generated with the aid of the proposed ANFIS method. This ANFIS structure takes the voltage and the current of the photovoltaic (PV) module as inputs and generates the maximum power point voltage as the output. Then, with the aid of a fuzzy controller, an appropriate duty cycle will be generated to control the switches of the two-switch flyback inverter. To validate this proposed system, the MATLAB-PSIM co-simulation was done. The results prove that the proposed MPPT controller for a two-switch flyback inverter can track the maximum power under different atmospheric conditions (irradiance and temperature), as well as other advantages from our investigation.

QSPR based ANFIS model for predicting standard molar chemical exergy of organic materials

Prediction of standard molar chemical exergy value for organic compounds is investigated using a quantitative structure-property relationship (QSPR) model combined with adaptive neuro-fuzzy inference system (ANFIS) strategy. Particle swarm optimization (PSO) method is also implemented to determine the optimal ANFIS structure. The proposed model uses three constitutional descriptors in model development’s procedure. The QSPR-ANFIS model represents a great performance in prediction of the standard molar chemical exergy values with 0.9999, 44548, and 1.49 values for R2, RMSE, and %AARD, respectively.

Assessing the Predictability of an Improved ANFIS Model for Monthly Streamflow Using Lagged Climate Indices as Predictors

The current study investigates the effect of a large climate index, such as NINO3, NINO3.4, NINO4 and PDO, on the monthly stream flow in the Aydoughmoush basin (Iran) based on an improved Adaptive Neuro Fuzzy Inference System (ANFIS) during 1987–2007. The bat algorithm (BA), particle swarm optimization (PSO) and genetic algorithm (GA) were used to obtain the ANFIS parameter for the best ANFIS structure. Principal component analysis (PCA) and Varex rotation were used to decrease the number of effective components needed for the streamflow simulation. The results showed that the large climate index with six-month lag times had the best performance, and three components (PCA1, PCA2 and PCA3) were used to simulate the monthly streamflow. The results indicated that the ANFIS-BA had better results than the ANFIS-PSO and ANFIS-GA, with a root mean square error (RMSE) 25% and 30% less than the ANFIS-PSO and ANFIS-GA, respectively. In addition, the linear error in probability space (LEPS) score for the ANFIS-BA, based on the average values for the different months, was less than the ANFIS-PSO and ANFIS-GA. Furthermore, the uncertainty values for the different ANFIS models were used and the results indicated that the monthly simulated streamflow by the ANFIS was computed well at the 95% confidence level. It can be seen that the average streamflow for the summer season is 75 m3/s, so that the stream flow for summer, based on climate indexes, is more than that in other seasons.

SOFT-COMPUTING ESTIMATION OF PRE-TREATED MANGO (MANGIFERA INDICA) KERNEL MOISTURE CONTENT IN A TRAY DRYER

This study presents Adaptive Neuro-fuzzy Inference System (ANFIS) technique for the estimation of pre-treated mango kernel moisture content in a tray dryer. The blanching and drying experiments were conducted at different drying air temperatures (45 -750C), drying time (0 - 420 minutes), blanching temperatures (40 - 1000C) and blanching time (3 - 9 minutes). The experimental input and output (moisture ratio) data were used to architect different ANFIS structures at different epoch numbers, input and output Membership Functions (MFs). The best structure for the estimation was achieved using trap input MF, constant output MF and 1500 epoch number. The Root Mean Squared Error (RMSE) and correlation coefficient R2 showed 0.0097 and 0.958 respectively for the ANFIS structure; furthermore, K value that compares ratio of the best ANFIS checking and training error is 0.87. The results of this investigation show the capability of ANFIS for the estimation of pre-treated mango kernel moisture content in a tray dryer.

Intelligent Soft Computing-Based Security Control for Energy Management Architecture of Hybrid Emergency Power System for More-Electric Aircrafts

This paper proposes an attack-resilient intelligent soft computing-based security control for energy management architecture for a hybrid emergency power system of more-electric aircrafts (MEAs). Our proposed architecture develops an adaptive neuro-fuzzy inference system (ANFIS)-based method in conjunction with a specific recurrent neural network architecture called long–short-term memory (LSTM) to evaluate the integrity of the power output of the critical components, which have higher vulnerability to the potential cyber-attacks and critical for the effective energy management and emergency control. We evaluate the integrity of the critical measurements that have high vulnerability to the potential cyber-attacks by using LSTM techniques. After identifying the compromised measurements, we continue to activate the closed-loop ANFIS mechanism in which the structure of ANFIS is controlled according to the accuracy of its current estimations by probing the physical couplings in the system. In our simulation, we evaluate the performance of our proposed LSTM-ANFIS -based method to support the integrity of the energy management strategies used in hybrid emergency power system for MEAs by using TensorFlow and MATLAB/Simulink co-simulation environment. Our simulation results illustrate the effectiveness of our proposed method in effectively evaluating the integrity of critical data and achieving resilient control.

Optimal Design of a Dragonfly-Inspired Compliant Joint for Camera Positioning System of Nanoindentation Tester Based on a Hybrid Integration of Jaya-ANFIS

Camera positioning system is a critical member of a nanoindentation tester characterizing the mechanical properties such as hardness, creep, surface roughness, or elastic modulus of a material sample. This paper presents a design optimization for a dragonfly-inspired compliant joint. This joint is used to drive the camera positioning system. A new hybrid approach of Taguchi method, adaptive neuro-fuzzy inference system (ANFIS), and Jaya algorithm is developed to solve the multi-objective optimization problem. The Taguchi method is used to build the numerical data and to find the best membership functions for the ANFIS structure by minimizing the root mean squared error. Then, the weight factor of each objective function is determined by established equations well. Subsequently, a structure of ANFIS is developed to map the design parameters and responses. Sensitivity analysis of each controllable parameter is analyzed by the statistical method. Finally, Jaya algorithm is initialized to find the optimal solution. The results found that the optimal displacement, frequency, and stress are about 12581.11 μm, 67.76 Hz, and 333.68 MPa, respectively. The proposed hybrid optimization algorithm is a robust and effective optimizer and considered as soft computing technique for engineering optimization problems.

Unsaturated soils permeability estimation by adaptive neuro-fuzzy inference system

Unsaturated soils permeability ( $$ K_{\text{unsat}} $$ ) is a required parameter when modeling water flow and transport processes in the subsurface. Having highly nonlinear relationship with volumetric water content (θw) and suction (S), the value of $$ K_{\text{unsat}} $$ varies by several magnitudes when moving from clayey to gravel soils. On the other hand, determination of $$ K_{\text{unsat}} $$ is very difficult, costly, and time consuming. Recently, adaptive neuro-fuzzy inference system (ANFIS) has been used for modeling and prediction of such complex and nonlinear problems. Investigated in this paper is the capability of ANFIS for modeling $$ K_{\text{unsat}} $$ . The database used in ANFIS modeling is collected from SoilVision. This database contains 4347 $$ K_{\text{unsat}} $$ test records on 245 soil types collected from all around the world; it approximately covers triangular chart defined by US Department of Agriculture System for classifying mixed soils. In order to get the optimum number of ANFIS training epochs and ANFIS structure, trial and error method was used. To check the predictive capacity of the ANFIS model, several statistics such as determination coefficient (R2), Root Mean Square Error, Mean Absolute Error and Variance Account For were calculated. The results demonstrated that the ANFIS model can be successfully applied for prediction of $$ K_{\text{unsat}} $$ .

ANFIS-based comparative exhaust gases emissions prediction model of a military aircraft engine

In this paper, comparison of estimation methods for exhaust gaseous emissions developed for a military aircraft engine via adaptive neuro-fuzzy inference system (ANFIS) structure is introduced. For system identification process, combustion efficiency, engine shaft RPM and air-fuel ratio are preferred to be system inputs to obtain emission indexes of carbon monoxide, carbon dioxide, nitrogen oxides and unburned hydrocarbon as system outputs. While comparing the estimation methodologies, two clustering methods in adaptive neuro-fuzzy inference system structure, grid partitioning and subtractive clustering, are benefited to define membership functions. Hybrid optimisation is preferred in training parts. As a conclusion remark of the present study, estimation error values of both clustering methods are found for different number of membership functions with the common training method. Nonetheless, training time saving is the advantage of subtractive clustering method in our study.

ANFIS-based comparative exhaust gases emissions prediction model of a military aircraft engine

In this paper, comparison of estimation methods for exhaust gaseous emissions developed for a military aircraft engine via adaptive neuro-fuzzy inference system (ANFIS) structure is introduced. For system identification process, combustion efficiency, engine shaft RPM and air-fuel ratio are preferred to be system inputs to obtain emission indexes of carbon monoxide, carbon dioxide, nitrogen oxides and unburned hydrocarbon as system outputs. While comparing the estimation methodologies, two clustering methods in adaptive neuro-fuzzy inference system structure, grid partitioning and subtractive clustering, are benefited to define membership functions. Hybrid optimisation is preferred in training parts. As a conclusion remark of the present study, estimation error values of both clustering methods are found for different number of membership functions with the common training method. Nonetheless, training time saving is the advantage of subtractive clustering method in our study.

Prediction of geomechanical parameters using soft computing and multiple regression approach

Abstract The evaluation of geotechnical parameters of geo-materials are essential part of every geotechnical project. But sometimes, it is not possible to determine the all required parameter in the laboratory. Therefore, scientist and engineers used the statistical and empirical relation to determine the crucial parameters. The present study focused on the determination of parameters like uniaxial compressive strength ( UCS ), tensile strength ( TS ), point load index ( PLI ) and Young’s modulus ( YM ) from very easily determinable physical parameters viz. density ( DEN ), porosity ( PORO ) and compressional wave velocity ( P-WV ) using multiple variable regression analysis (MVRA) and adaptive neuro-fuzzy inference system (ANFIS). The various ANFIS structures and MVRA models were tried for prediction of desired parameters and best one was considered based on variance account for (VAF), root mean square error (RMSE) and correlation coefficient (R 2 ). ANFIS structure not only depends on the input parameters and rules, but also on the output parameter as observed in case of PLI .

Prediction of Thermal Conductivity and Viscosity of Ionic Liquid-Based Nanofluids Using Adaptive Neuro Fuzzy Inference System

ABSTRACTNowadays, ionic liquid-based nanofluids are introduced as a new class of heat transfer fluids, which exhibit superior thermal properties compared to their base ionic liquids. Potential applications of these nanofluids make it necessary to know their thermophysical properties such as thermal conductivity and viscosity. Therefore, adaptive neuro fuzzy inference system (ANFIS) has been successfully developed to predict thermal conductivity and viscosity of ionic liquid-based nanofluids. The developed models have investigated the influence of temperature, nanoparticle concentration, and ionic liquid molecular weight on the thermophysical properties of nanofluids. After developing ANFIS structure, the capability and accuracy of the developed neuro fuzzy models have been evaluated by comparison of model predictions with experimental data extracted from the literature and calculation of statistical parameters such as coefficient of determination (R2) and average relative deviation (ARD). The ARD of ANFIS model in prediction of thermal conductivity of nanofluids is 0.72%, with a high R2 of 0.9959. The values of ARD and R2 for estimation of nanofluids viscosity are 5.1% and 0.9934, respectively, which indicates a satisfactory degree of accuracy for the proposed models.

Forecasting energy consumption using ensemble ARIMA–ANFIS hybrid algorithm

Energy consumption is on the rise in developing economies. In order to improve present and future energy supplies, forecasting energy demands is essential. However, lack of accurate and comprehensive data set to predict the future demand is one of big problems in these countries. Therefore, using ensemble hybrid forecasting models that can deal with shortage of data set could be a suitable solution. In this paper, the annual energy consumption in Iran is forecasted using 3 patterns of ARIMA–ANFIS model. In the first pattern, ARIMA (Auto Regressive Integrated Moving Average) model is implemented on 4 input features, where its nonlinear residuals are forecasted by 6 different ANFIS (Adaptive Neuro Fuzzy Inference System) structures including grid partitioning, sub clustering, and fuzzy c means clustering (each with 2 training algorithms). In the second pattern, the forecasting of ARIMA in addition to 4 input features is assumed as input variables for ANFIS prediction. Therefore, four mentioned inputs beside ARIMA’s output are used in energy prediction with 6 different ANFIS structures. In the third pattern, due to dealing with data insufficiency, the second pattern is applied with AdaBoost (Adaptive Boosting) data diversification model and a novel ensemble methodology is presented.The results indicate that proposed hybrid patterns improve the accuracy of single ARIMA and ANFIS models in forecasting energy consumption, though third pattern, used diversification model, acts better than others and model’s MSE criterion was decreased to 0.026% from 0.058% of second hybrid pattern. Finally, a comprehensive comparison between other hybrid prediction models is done.

Integrating metaheuristics and ANFIS for daily mean temperature forecasting

Weather forecasting is considered as a key to successful planning for various applications such as agricultural industries. Having accurate weather forecasting allows people to make better decision on managing day to day activities. Also, it has to be underlined that forecasting is important to cope with impacts of extreme events and to adapt to climatic changes. To improve weather forecasting, we used hybrid adaptive neuro-fuzzy inference system (ANFIS), which consist in exploiting capabilities of harmony search (HS) and genetic algorithm (GA), for selecting the most relevant weather variables and simultaneously searching the most appropriate structure of ANFIS. Proposed methods are applied for six different cities of Turkey which are determined according to Aydeniz's climate classification. The results of the study showed that GA-ANFIS and HS-ANFIS yield remarkable results in daily mean temperature forecasting due to the ability of capturing the advantages of both types of methods simultaneously.

AN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR FORECASTING AUSTRALIA’S DOMESTIC LOW COST CARRIER PASSENGER DEMAND

This study has proposed and empirically tested two Adaptive Neuro-Fuzzy Inference System (ANFIS) models for the first time for predicting Australia‘s domestic low cost carriers‘ demand, as measured by enplaned passengers (PAX Model) and revenue passenger kilometres performed (RPKs Model). In the ANFIS, both the learning capabilities of an artificial neural network (ANN) and the reasoning capabilities of fuzzy logic are combined to provide enhanced prediction capabilities, as compared to using a single methodology. Sugeno fuzzy rules were used in the ANFIS structure and the Gaussian membership function and linear membership functions were also developed. The hybrid learning algorithm and the subtractive clustering partition method were used to generate the optimum ANFIS models. Data was normalized in order to increase the model‘s training performance. The results found that the mean absolute percentage error (MAPE) for the overall data set of the PAX and RPKs models was 1.52% and 1.17%, respectively. The highest R2-value for the PAX model was 0.9949 and 0.9953 for the RPKs model, demonstrating that the models have high predictive capabilities.