ANFIS-based Model Research Articles

Prognostication of vertical stress transmission in soil profile by adaptive neuro-fuzzy inference system based modeling approach

Prediction of vertical stress transmission in real soil profile using adaptive neuro-fuzzy inference system (ANFIS) is documented in this investigation. A soil bin facility holding a single-wheel tester was utilized to arrange controlled condition for exploration of the effects of wheel load, forward velocity, slippage and depth each at three different levels. A profile housing seven load cells was buried at different depths when data were transmitted to a data acquisitioning system for derivation of 81 data points and then to build ANFIS-based model. The Sugeno-type fuzzy rules were constituted with various membership functions in the representations. In the Sugeno-type fuzzy inference approach, the modal was developed according to the four input parameters. Performance evaluation criteria (i.e. MSE, MRE and R2) were incorporated in the study to find the highest quality solution. It was deduced, on the basis of performance criteria, that a Guassian membership function outperformed other tested membership functions. The results could serve as a catalyst to expedite the investigations in the realm of artificial intelligence application in prediction of soil stress transmission created by wheeled vehicle trafficking.

Predicting the Performance of Pyramidal and Corrugated Horn Antennas Using ANFIS

This letter presents the development of an adaptive neuro fuzzy inference system (ANFIS)-based model for predicting the performance of a pyramidal and a conical corrugated horn antennas. Different ANFIS-based models were developed. Using these models, horn return-loss characteristics and radiation patterns (copolarization and cross-polarization) can be obtained with great accuracy for any combination of design parameters. The results obtained using ANFIS models were compared to that of the commercial horn antenna design software and found in close agreement. The advantage of the proposed method lies with the fact that, with less time and minimum computational resources, the return-loss and radiation characteristics of a horn antenna under test can be obtained with high degree of accuracy.

Developing a turbine cycle model using adaptive neuro-fuzzy inference system for Kuosheng nuclear power plant

The objective of this study is to develop a turbine cycle model using the adaptive neuro-fuzzy inference system (ANFIS) for the Kuosheng nuclear power plant (NPP) in Taiwan. This ANFIS-based turbine cycle model is used to estimate the turbine–generator output. The plant operating data were verified using a linear regression model with a corresponding 95% confidence interval for the operating data. In this study, the key parameters were selected as inputs for the neuro-fuzzy-based turbine cycle model. After training and validating with key parameters, including turbine throttle pressure, condenser backpressure, feedwater flow rate, and final feedwater temperature, the proposed model was used to estimate the turbine–generator output. The effectiveness of the proposed ANFIS-based turbine cycle model was demonstrated using plant operating data obtained from Unit 1 of the Kuosheng NPP owned by Taiwan Power Company. The results show that this neuro-fuzzy-based turbine cycle model can be used to accurately estimate the turbine–generator output. In addition, a thermodynamic turbine cycle model was developed using a commercial software, PEPSE, in order to compare the performance of the ANFIS-based turbine cycle model. The results show that the proposed neuro-fuzzy-based turbine cycle model is capable of accurately estimating the turbine–generator output and providing more reliable results than the PEPSE turbine cycle model, with regard to estimation accuracy and clearly defined trends. The results of this study provide an alternative approach for evaluating the thermal performance of NPPs.

An ANFIS-Based Air Quality Model for Prediction of SO2 Concentration in Urban Area

This paper presents the results of attempt to perform modeling of SO2 concentration in urban area in vicinity of copper smelter in Bor (Serbia), using ANFIS methodological approach. The aim of obtained model was to develop a prediction tool that will be used to calculate potential SO2 concentration, above prescribed limitation, based on input parameters. As predictors, both technogenic and meteorological input parameters were considered. Accordingly, the dependence of SO2 concentration was modeled as the function of wind speed, wind direction, air temperature, humidity and amount sulfur emitted from the pyrometallurgical process of sulfidic copper concentration treatment.

Simulation of Hemodynamics in a Graft-To-Vein Anastomoses by Adaptive Neuro-Fuzzy Based Modeling

A new methodology for simulating the flow field inside an arteriovenous (AV) graft to vein anastomoses by the adaptive neuro fuzzy inference system (ANFIS) is presented in this study. For determining the optimal AV graft angle, an ANFIS-based model of neuro fuzzy-graft-vein (NF-GVEIN) is proposed. Therefore engineering design of the graft can be supported. The advantage of this neuro-fuzzy hybrid model is that it does not require the model structure to be known a priori, in contrast to most of the modeling techniques. A case study with real experimental data was carried out. NFGVEIN was optimized by means of selection of the algorithm among 34 ANFIS algorithms by terms of minimal error. The optimal neural network structure was determined. The optimal AV graft angle causing the least turbulence was obtained. The simulation results showed that this model is feasible for forecasting of finding the optimal AV graft angle inside AV graft to vein anastomoses with different flow rates.

Prediction of Concrete Strength Using Fuzzy Neural Networks

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

An ANFIS-based model for predicting adequacy of vancomycin regimen using improved genetic algorithm

In this paper, a model based on the adaptive network-based fuzzy inference system (ANFIS) with the improved genetic algorithm is used to predict the adequacy of vancomycin regimen. The improved genetic algorithm, i.e., hybrid Taguchi-genetic algorithm (HTGA), is applied in the ANFIS to simultaneously find the optimal premise and consequent parameters and a total output layer parameter by directly maximizing the training accuracy performance criterion. Experimental results show that the HTGA-based ANFIS model outperforms the logistic regression model in terms of prediction accuracy. Therefore, this study demonstrates the feasibility of applying the HTGA-based ANFIS as the mechanism of the decision support systems for the adequacy of vancomycin regimen for the patients based on clinical databases.

Predicting weld bead geometry and HAZ width in modified 9Cr-1Mo steel welds using ANFIS-based models

Modified 9Cr-1Mo steels are used as structural materials for stream generator components in power plants. Generally, tungsten inert gas (TIG) welding is used in the fabrication of these steels. In TIG welding of modified 9Cr-1Mo steels, the depth of penetration achievable in single pass is limited. Thus, a novel technique called as activated flux tungsten inert gas (A-TIG) welding has been developed in which application of flux over the joint area increases depth of penetration. In these weld joints, weld bead shape parameters like depth of penetration, bead width and heat affected zone (HAZ) width influences the mechanical properties and also the performance of the weld joints during high temperature service. Since, the fuzzy system lacks learning ability, a hybrid neuro-fuzzy system called as adaptive neuro-fuzzy inference system (ANFIS) has been used to develop models correlating process parameters with weld bead shape parameters and the models are also validated experimentally.

Application of Adaptive Network-Based Fuzzy Inference System for Sensorless Control of PMSG-Based Wind Turbine With Nonlinear-Load-Compensation Capabilities

The precise information of permanent-magnet synchronous generator (PMSG) rotor position and speed is essentially required to operate it on maximum power points. This paper presents an adaptive network-based fuzzy inference system (ANFIS) for speed and position estimation of PMSG, where an ANFIS-based model reference adaptive system is continuously tuned with actual PMSG to neutralize the effect of parameter variations such as stator resistance, inductance, and torque constant. This ANFIS-tuned estimator is able to estimate the rotor position and speed accurately over a wide speed range with a great immunity against parameter variation. The proposed system consists of two back-to-back connected inverters, where one controls the PMSG, while another is used for grid synchronization. Moreover, in the proposed study, the grid-side inverter is also utilized as harmonic, reactive power, and unbalanced load compensator for a three-phase, four-wire (3P4W) nonlinear load, if any, at point of common coupling (PCC). This enables the grid to always supply/absorb a balanced set of fundamental currents at unity power factor. The proposed system is developed and simulated using MATLAB/SimPowerSystem (SPS) toolbox. Besides this, a scaled laboratory hardware prototype is developed and extensive experimental study is carried out to validate the proposed control approach.

Developing operating rules for reservoirs considering the water quality issues: Application of ANFIS-based surrogate models

In this study, a methodology combining a water quality simulation model and a hybrid genetic algorithm (HGA) is developed for determining optimal operating policies for different reservoir outlets. The water quality simulation model is based on an adaptive neural fuzzy inference system (ANFIS), which is trained using the results of a numerical water quality simulated model. This ANFIS-based simulation model has an acceptable run-time and can be easily linked to the HGA-based optimization model. To further reduce the model run-time, the main problem is decomposed to a long-term and some annual optimization models. ANFIS is also utilized to develop monthly operating rules for reservoir outlets. To evaluate the efficiency of the proposed methodology, it is applied to the 15-Khordad dam located in the central part of Iran. The results show that this ANFIS-based model can significantly reduce the run-time of the previously developed models while it dose not reduce the accuracy of the reservoir operation policies. The ANFIS-based operating rules can be effectively used for real-time reservoir operation.

Predicting physico-mechanical properties of LLDPE and PDMS rubber blends using ANFIS-based model

Blends of linear low density polyethylene (LLDPE) and poly dimethyl siloxane (PDMS) rubber have been identified as a promising material for cable insulation. The influence of processing parameters on the physico-mechanical properties of these blends has been investigated and the same has been optimised for preparing these blends. In practice, it is not possible to carry out several experiments to identify the relationship between the intermediate processing parameters and physical properties. To address this issue, the relationship between the processing parameters and the mechanical properties of the optimised LLDPE and PDMS rubber blends have been mapped using a non-linear system identification technique namely, adaptive-network-based fuzzy inference system (ANFIS) without carrying further experiments, but with the available experimental data. In this model, the effect of the number of fuzzy rules on the model performance has been investigated to predict the properties and to correlate the above relationship. This paper reports the development of ANFIS-based model for predicting the LLDPE/PDMS rubber blends in reducing the experimental trials and its success over the ANN model.

Dynamic Behavioral Modeling of Power Amplifiers Using ANFIS-Based Hammerstein

This letter presents a novel structure for the dynamic behavioral modeling of radio frequency power amplifiers (RF PAs) with ANFIS-based Hammerstein model for memory effects. The model is an adaptive neuro-fuzzy inference system (ANFIS) followed by a finite impulse response (FIR) filter. A hybrid learning algorithm is adopted to identify the parameters of the ANFIS. The parameters of the FIR filter are estimated by a straightforward least-squares method. The input and output signals of the PA excited with a three-carrier WCDMA signal were sampled for the model identification and validation in a test bench. Experimental results in the frequency and the time domains show that the proposed model was able to give an accurate approximation to characterize the wideband RF PAs.

Hindcasting of wave parameters using different soft computing methods

Hindcasting of wave parameters is necessary for many applications in coastal and offshore engineering and is generally made with the help of sophisticated numerical models. This paper presents alternative hindcast models based on Artificial Neural Networks (ANNs), Fuzzy Inference System (FIS) and Adaptive-Network-based Fuzzy Inference System (ANFIS). The data set used in this study comprises wave and wind data gathered from deep water location in Lake Ontario. Wind speed, wind direction, fetch length and wind duration were used as input variables, while significant wave height, peak spectral period and mean wave direction were the output parameters. Different topologies of ANNs were considered to predict the wave parameters and the relative importance of input parameters were determined. Finally, the results of ANNs-based models, FIS- and ANFIS-based models were compared. Results indicated that error statistics of soft computing models were similar, while ANFIS models were marginally more accurate than FIS and ANNs models.

Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system

The aim of this study is to predict the peak particle velocity (PPV) values from both presently constructed simple regression model and fuzzy-based model. For this purpose, vibrations induced by bench blasting operations were measured in an open-pit mine operated by the most important magnesite producing company (MAS) in Turkey. After gathering the ordered pairs of distance and PPV values, the site-specific parameters were determined using traditional regression method. Also, an attempt has been made to investigate the applicability of a relatively new soft computing method called as the adaptive neuro-fuzzy inference system (ANFIS) to predict PPV. To achieve this objective, data obtained from the blasting measurements were evaluated by constructing an ANFIS-based prediction model. The distance from the blasting site to the monitoring stations and the charge weight per delay were selected as the input parameters of the constructed model, the output parameter being the PPV. Valid for the site, the PPV prediction capability of the constructed ANFIS-based model has proved to be successful in terms of statistical performance indices such as variance account for (VAF), root mean square error (RMSE), standard error of estimation, and correlation between predicted and measured PPV values. Also, using these statistical performance indices, a prediction performance comparison has been made between the presently constructed ANFIS-based model and the classical regression-based prediction method, which has been widely used in the literature. Although the prediction performance of the regression-based model was high, the comparison has indicated that the proposed ANFIS-based model exhibited better prediction performance than the classical regression-based model.