Query: anfis

title: ANFIS: adaptive network based fuzzy inference system
abstract: The architecture and learning procedure underlying ANFIS (adaptive network based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input output mapping based on both human knowledge (in the form of fuzzy if then rules) and stipulated input output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested.

title: Flood susceptibility assessment using integration of adaptive network based fuzzy inference system (ANFIS) and biogeography based optimization (BBO) and BAT algorithms (BA)
abstract: This paper couples an adaptive neuro fuzzy inference system (ANFIS) with two heuristic based computation methods namely biogeography based optimization (BBO) and BAT algorithm (BA) with GIS to map flood susceptibility in a region of Iran. These algorithms have been used for flood modelling, infrequently. A total of 287 flood locations were randomly categorized into training (70% 201 floods) and validation (30% 86 floods) datasets for modelling process and evaluation. The Step wise Weight Assessment Ratio Analysis (SWARA) technique was applied to evaluate the role of nine dominant factors on flood occurrence. The results of using the ANFIS and the artificial intelligence ensemble algorithms were three flood susceptibility maps. Results indicated that the ANFIS BBO had the highest accuracy in comparison with the ANFIS and ANFIS BA models in flood modelling. In addition, BBO algorithm showed its great potential by considering higher accuracy and lower computational time, in mapping and assessment of flood susceptibility.

title: Spatial prediction of groundwater potentiality using ANFIS ensembled with teaching learning based and biogeography based optimization
abstract: Groundwater is one of the most beneficial natural resources worldwide. The main aim of the present study is the mapping of groundwater potential of the Zhangjiamao area in China using two novel hybrid data mining techniques that involve the adaptive neuro fuzzy inference system (ANFIS) ensembled with teaching learning based optimization (TLBO) and biogeography based optimization (BBO) First, 93 spring locations were identified in the study area, which were randomly divided in the ratio of 70:30 to be used for building models and validation, respectively. A total of 16 spring affecting factors viz. slope aspect, altitude, slope angle, plan curvature, profile curvature, curvature, sediment transport index, stream power index, topographic wetness index, distance to roads, distance to rivers, rainfall, lithology, soil, NDVI, and land use/land cover, were used as input variables. The probability certainty factor (PCF) method was applied for the correlation analysis between spring occurrences and the affecting factors. Subsequently, the ANFIS TLBO and ANFIS BBO models were generated to produce groundwater spring potential maps (GSPMs) Finally, the GSPMs were evaluated by using the area under the receiver operating characteristic (AUROC) The results showed that the AUROC values for the ANFIS TLBO model based on the training and validation datasets were 0.866 and 0.905, respectively. In contrast, the AUROC values for the ANFIS BBO model based on the training and validation datasets were 0.861 and 0.887, respectively. The results of ANFIS TLBO showed that 25.66% of the study area could be classified into the high and very high groundwater spring potential classes as compared to the ANFIS BBO model (30.52% The results of the present study can be useful for groundwater management.

title: Optimization of ANFIS with GA and PSO estimating a ratio in driven piles
abstract: This study aimed to optimize Adaptive Neuro Fuzzy Inferences System (ANFIS) with two optimization algorithms, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for the calculation friction capacity ratio (a) in driven shafts. Various studies are shown that both ANFIS are valuable methods for prediction of engineering problems. However, optimizing ANFIS with GA and PSO has not been used in the area of pile engineering. The training data set was collected from available full scale results of the driven piles. The input parameters used in this study were pile diameter (m) pile length (m) relative density (Id) embedment ratio (L/D) both of the pile end resistance (qc) and base resistance at relatively 10% base settlement (qb0.1) from CPT result, whereas the output was a. A learning fuzzy based algorithm was used to train the ANFIS model in the MATLAB software. The system was optimized by changing the number of clusters in the FIS and then the output was used for the GA and PSO optimization algorithm. The prediction was compared with the real monitoring field data. As a result, good agreement was attained representing reliability of all proposed models. The estimated results for the collected database were assessed based on several statistical indices such as R2, RMSE, and VAF. According to R2, RMSE, and VAF, values of (0.9439, 0.0123 and 99.91) (0.9872, 0.0117 and 99.99) and (0.9605, 0.0119 and 99.97) were obtained for testing data sets of the optimized ANFIS, GA ANFIS, and PSO ANFIS predictive models, respectively. This indicates higher reliability of the optimized GA ANFIS model in estimating a ratio in driven shafts.

title: Developing an ANFIS based swarm concept model for estimating the relative viscosity of nanofluids
abstract: Nanofluid viscosity is an important physical property in convective heat transfer phenomena. However, the current theoretical models for nanofluid viscosity prediction are only applicable across a limited range. In this study, 1277 experimental data points of distinct nanofluid relative viscosity (NF RV) were gathered from a plenary literature review. In order to create a general model, adaptive network based fuzzy inference system (ANFIS) code was expanded based on the independent variables of temperature, nanoparticle diameter, nanofluid density, volumetric fraction, and viscosity of the base fluid. A statistical analysis of the data for training and testing (with R2 .99997) demonstrates the accuracy of the model. In addition, the results obtained from ANFIS are compared to similar experimental data and show absolute and maximum average relative deviations of about 0.42 and 6.45% respectively. Comparisons with other theoretical models from previous research is used to verify the model and prove the prediction capabilities of ANFIS. Consequently, this tool can be of huge value in helping chemists and mechanical and chemical engineers especially those who are dealing with heat transfer applications by nanofluids by providing highly accurate predictions of NF RVs.

title: Spatial Prediction of Landslide Susceptibility Using GIS Based Data Mining Techniques of ANFIS with Whale Optimization Algorithm (WOA) and Grey Wolf Optimizer (GWO)
abstract: The most dangerous landslide disasters always cause serious economic losses and human deaths. The contribution of this work is to present an integrated landslide modelling framework, in which an adaptive neuro fuzzy inference system (ANFIS) is combined with the two optimization algorithms of whale optimization algorithm (WOA) and grey wolf optimizer (GWO) at Anyuan County, China. It means that WOA and GWO are used as two meta heuristic algorithms to improve the prediction performance of the ANFIS based methods. In addition, the step wise weight assessment ratio analysis (SWARA) method is used to obtain the initial weight of each class of landslide influencing factors. To validate the effectiveness of the proposed framework, 315 landslide events in history were selected for our experiments and were randomly divided into the training and verification sets. To perform landslide susceptibility mapping, fifteen geological, hydrological, geomorphological, land cover, and other factors are considered for the modelling construction. The landslide susceptibility maps by SWARA, SWARA ANFIS, SWARA ANFIS PSO, SWARA ANFIS WOA, and SWARA ANFIS GWO models are assessed using the measures of the receiver operating characteristic (ROC) curve and root mean square error (RMSE) The experiments demonstrated that the obtained results of modelling process from the SWARA to the SAWRA ANFIS GWO model were more accurate and that the proposed methods have satisfactory prediction ability. Specifically, prediction accuracy by area under the curve (AUC) of SWARA, SWARA ANFIS, SWARA ANFIS PSO, SWARA ANFIS GWO, and SWARA ANFIS WOA models were 0.831, 0.831, 0.850, 0.856, and 0.869, respectively. Due to adaptability and usability, the proposed prediction methods can be applied to other areas for landslide management and mitigation as well as prevention throughout the world.

title: ANFIS pattern for molecular membranes separation optimization
abstract: In this work, molecular separation of aqueous organic was simulated by using combined soft computing mechanistic approaches. The considered separation system was a microporous membrane contactor for separation of benzoic acid from water by contacting with an organic phase containing extractor molecules. Indeed, extractive separation is carried out using membrane technology where complex of solute organic is formed at the interface. The main focus was to develop a simulation methodology for prediction of concentration distribution of solute (benzoic acid) in the feed side of the membrane system, as the removal efficiency of the system is determined by concentration distribution of the solute in the feed channel. The pattern of Adaptive Neuro Fuzzy Inference System (ANFIS) was optimized by finding the optimum membership function, learning percentage, and a number of rules. The ANFIS was trained using the extracted data from the CFD simulation of the membrane system. The comparisons between the predicted concentration distribution by ANFIS and CFD data revealed that the optimized ANFIS pattern can be used as a predictive tool for simulation of the process. The R2 of higher than 0.99 was obtained for the optimized ANFIS model. The main privilege of the developed methodology is its very low computational time for simulation of the system and can be used as a rigorous simulation tool for understanding and design of membrane based systems.

title: Prediction of unsaturated hydraulic conductivity using adaptive neuro fuzzy inference system (ANFIS)
abstract: This paper aims to predict the unsaturated hydraulic conductivity of soil using Adaptive Neuro fuzzy inference system (ANFIS) Multi Linear Regression (MLR) and artificial neural network (ANN) Laboratory experiments carried out on 46 samples of sand, rice husk ash and fly ash (FA) mixture. Out of 46 data set for modeling of unsaturated hydraulic conductivity 32 random data used for training and remaining 14 to the test. The results suggest improved performance by Gaussian membership function than triangular and generalized bell shaped membership based ANFIS. MLR is better than ANN and Gaussian membership function based ANFIS for unsaturated hydraulic conductivity.

title: Performance evaluation of hybrid FFA ANFIS and GA ANFIS models to predict particle size distribution of a muck pile after blasting
abstract: Accurately predicting the particle size distribution of a muck pile after blasting is always an important subject for mining industry. Adaptive neuro fuzzy inference system (ANFIS) has emerged as a synergic intelligent system. The main contribution of this paper is to optimize the premise and consequent parameters of ANFIS by firefly algorithm (FFA) and genetic algorithm (GA) To the best of our knowledge, no research has been published that assesses FFA and GA with ANFIS for fragmentation prediction and no research has tested the efficiency of these models to predict the fragmentation in different time scales as of yet. To show the effectiveness of the proposed ANFIS FFA and ANFIS GA models, their modelling accuracy has been compared with ANFIS, support vector regression (SVR) and artificial neural network (ANN) Intelligence predictions of fragmentation by ANFIS FFA, ANFIS GA, ANFIS, SVR and ANN are compared with observed values of fragmentation available in 88 blasting event of two quarry mines, Iran. According to the results, both ANFIS FFA and ANFIS GA prediction models performed satisfactorily; however, the lowest root mean square error (RMSE) and the highest correlation of determination R 2 values were obtained from ANFIS GA model. The values of R 2 and RMSE obtained from ANFIS GA, ANFIS FFA, ANFIS, SVR and ANN models were equal to (0.989, 0.974) (0.981, 1.249) (0.956, 1.591) (0.924, 2.016) and (0.948, 2.554) respectively. Consequently, the proposed ANFIS GA model has the potential to be used for predicting aims on other fields.

title: Feasibility of ANFIS PSO and ANFIS GA Models in Predicting Thermophysical Properties of Al2O3 MWCNT/Oil Hybrid Nanofluid
abstract: The main purpose of the present paper is to improve the performance of the adaptive neuro fuzzy inference system (ANFIS) in predicting the thermophysical properties of Al2O3 MWCNT/thermal oil hybrid nanofluid through mixing using metaheuristic optimization techniques. A literature survey showed that the use of an artificial neural network (ANN) is the most widely used method, although there are other methods that showed better performance. Moreover, it was found in the literature that artificial intelligence methods have been widely used for predicting the thermal conductivity of nanofluids. Thus, in the present study, genetic algorithms (GAs) and particle swarm optimization (PSO) have been utilized to search and determine the antecedent and consequent parameters of the ANFIS model. Solid concentration and temperature were considered as input variables, and thermal conductivity, dynamic viscosity, heat transfer performance, and pumping power in both the internal laminar and turbulent flow regimes were the outputs. In order to evaluate and compare the performance of the models, two statistical indices of root mean square error (RMSE) and determination coefficient (R) were utilized. Based on the results, both of the models are able to predict the thermophysical properties appropriately. However, the ANFIS PSO model had a better performance than the ANFIS GA model. Finally, the studied thermophysical properties were developed by the trained ANFIS PSO model.