Adaptive Fuzzy Inference System Research Articles

Neuro-fuzzy estimation of the most influential factors of laser melt pool depth during direct deposition process

In this article the main aim was to identify the most influential attributes for optimal conditions for directed energy deposition through the melt pool optimization and monitoring. The main goal is to track the melt pool geometries such as width and depth. The goal of this study is to use an adaptive neural fuzzy inference system (ANFIS) to categorize the various melt pool depth input values. The procedure was optimized using ANFIS based on seven processing factors. Laser power (P), scanning speed (V), melt pool width (W), melt pool length (L), build height (BH), melt pool height (H), and melt pool tilt are the input parameters (I). Skillful prediction might be critical in achieving optimal circumstances throughout the deposition process. According to the findings, laser power has the greatest influence on melt pool depth. The combination of laser power and melt pool width produces the least training error and hence has the greatest impact on melt pool depth. The study, which takes into account many input parameters at the same time, is thought to be the first on a modest scale and will pique everyone's curiosity.

A novel hybrid framework based on the ANFIS, discrete wavelet transform, and optimization algorithm for the estimation of water quality parameters

Abstract Improving the performance of machine learning (ML) algorithms is essential for accurately estimating water quality parameters (WQPs). For the first time, a novel hybrid framework, namely the adaptive neural fuzzy inference system–discrete wavelet transform–gradient-based optimization (ANFIS–DWT–GBO), for estimation of electrical conductivity (EC) and total dissolved solids (TDS), is used. Before estimating WQPs, the performance of the ANFIS–DWT–GBO is proven by several benchmark data sets. In addition, three benchmark algorithms, including ANFIS, ANFIS–DWT, and ANFIS–GBO, are used to demonstrate the strength of the novel framework. The principal component analysis (PCA) method determines the best input combination in EC and TDS estimation. The consequences show that the ANFIS–DWT–GBO produces very successful and competitive results in benchmark data sets modeling and WQPs estimation compared to other algorithms. This result is due to the simultaneous use of DWT and optimization algorithm in the proposed framework. DWT can process WQP data before applying it to the algorithms. The GBO is utilized to optimize the hyperfine parameters in the ANFIS. The results show that the highest accuracy of estimating EC and TDS is in Mollasani and Gotvand stations, respectively. The correlation coefficient (R) value in the Mollasani station is 0.99, and in the Gotvand station it is 0.98.

Research on intelligent launching control of dual clutch transmissions based on adaptive neural fuzzy inference system

Research on intelligent launching control of dual clutch transmissions based on adaptive neural fuzzy inference system

A novel comparative model proposal for hospital pneumatic systems

Hospital pneumatic systems are of great importance in terms of efficient use of resources and early diagnosis of diseases. Each sample is transported at a constant and specific speed/pressure in existing pneumatic systems. This study aims to provide pressure/speed control according to the type of material being transported, prevent deterioration during transportation, and make a positive contribution to system users. We have proposed a new control model for hospital pneumatic systems. This hospital pneumatic system is modeled using fuzzy logic, adaptive neural network-based fuzzy inference system (ANFIS), and artificial neural network (ANN) methods. The parameters affecting the performance of the system are determined by experimental trials carried out. Fuzzy logic, ANFIS, and ANN models are compared using the regression coefficient (R2), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE) metrics to determine the most suitable modeling method for the system. As a result of this study, carrying the pressure value (velocity) ideal for the sample type will increase the energy efficiency of the system.

Analysis of nano-particle release from the surface of structural concrete members by laser ablation using soft computing

The paper is concerned with the evaluation of laser-treated cementitious surfaces. It is part of a bigger investigation into the laser cleaning procedure and its impact on the change of the Nano particles of cementitious composites and concrete member structures. Because of the wide range of absorptivity of highly developed surfaces of concrete members and cementitious materials, their reactions to laser irradiation varied significantly. Even while laser could effectively remove the particles from the surface of concrete members, there are usually some residual surface modifications related with mortar removing, fracture development, and glazing (melted mortar). In this study, a large amount of thermal stress is created with low power densities (300 W/cm2) and large spot sizes (50 mm) that promote structural concrete member cracking. By raise of power density, the cement matrix material melts make the thermal ablation mode ineffective. This procedure is also heavily influenced by the beam diameter to aggregate size rate. As a result, due to stable size of aggregate, the procedure is not scalable. In this study, the findings of ablation experiments on cement and concrete member specimens performed with a Yb-fiber laser with a power of 1 kW and a focused beam width of 800 m have been delivered through fiber optic beam delivery. In order to know the influence of substrate composition on ablation ratio and processes, the laser–surface interaction was examined on type I Portland cement with varying amounts of sand or fine silica. The primary objective is to maximize the material removal rate (MRR) and aspect proportion of alumina, stainless steel, and chromium (Cr). The goal of this study is to use an adaptive neural fuzzy inference system (ANFIS) to categorize the multiple input variables for MRR and ratios based on process variables. Scanning speed, annealing temperature, laser power, and laser frequency are all input parameters. The prediction might be crucial in determining the ideal circumstances for micro and Nano architectures of concrete members created by laser ablation. According to the data, annealing power has the greatest impact on aspect ratio, whereas micro structure width has the most effect on MRR. Analysis revealed that contaminants of alumina, stainless steel, and chromium (Cr) were highly segregated into distinct areas of the aerosol's particle size distribution.

Effects of thickness reduction in cold rolling process on the formability of sheet metals using ANFIS

Cold rolling has detrimental effect on the formability of sheet metals. It is, however, inevitable in producing sheet high quality surfaces. The effects of cold rolling on the forming limits of stretch sheets are not investigated comprehensively in the literature. In this study, a through experimental study is conducted to observe the effect of different cold rolling thickness reduction on the formability of sheet metals. Since the experimental procedure of such tests are costly, an artificial intelligence is also adopted to predict effects of cold thickness reduction on the formability of the sheet metals. In this regard, St14 sheets are examined using tensile, metallography, cold rolling and Nakazima’s hemi-sphere punch experiments. The obtained data are further utilized to train and test an adaptive neural network fuzzy inference system (ANFIS) model. The results indicate that cold rolling reduces the formability of the sheet metals under stretch loading condition. Moreover, the tensile behavior of the sheet alters considerably due to cold thickness reduction of the same sheet metal. The trained ANFIS model also successfully trained and tested in prediction of forming limits diagrams. This model could be used to determine forming limit strains in other thickness reduction conditions. It is discussed that determination of forming limit diagrams is not an intrinsic property of a chemical composition of the sheet metals and many other factors must be taken into account.

Comparative Analysis of Conventional, Artificial Intelligence, and Hybrid-Based MPPT Technique for 852.6-Watt PV System

In this article, Matlab & Simulation software is used for analysis and comparison of 8(Eight) different MPPT. Different MPPT techniques that have been considered in this article are PWM-based, Perturb and Observation (P&O), Incremental Conductance (InC), and Modified InC (MIC) that comes under the Conventional Method. In the Artificial Intelligence, Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN) is chosen and in Hybrid method Neuro-Fuzzy Network (NFN) and Adaptive Neural Fuzzy Inference System (ANFIS) has been considered. PV module of 852.2 Watt is designed with the Boost Converter which can boost the voltage up to 185 Volt for all MPPT. A set of data has been taken for FLC, ANN, NFN, and ANFIS. After implementation, the result has been analyzed for standard test conditions and for the different environmental conditions. In this article, both irradiation and the temperature have been varied together for all MPPT rest are kept constant.

Forecasting of groundwater level fluctuations using a hybrid of multi-discrete wavelet transforms with artificial intelligence models

Abstract Groundwater is often one of the significant natural sources of freshwater supply, especially in arid and semi-arid regions, and is of paramount importance. This study provides a new and high accurate technique for forecasting groundwater level (GWL). The artificial intelligence (AI) models include the artificial neural network (ANN) of multi-layer perceptron (MLP) and radial basis function network (RBF), and adaptive neural-fuzzy inference system (ANFIS) models. Input data to the models is the monthly average GWL of 17 piezometers. In this study, a preprocessing of data including the discrete wavelet transform (DWT) and multi-discrete wavelet transform (M-DWT) simultaneously was utilized. The results showed that the hybrid M-DWT-ANN, M-DWT-RBF, and M-DWT-ANFIS models compared to the DWT-ANN, DWT-RBF, and DWT-ANFIS models as well as than regular ANN, RBF, and ANFIS models, had the highest accuracy in forecasting GWL for the 1-, 2-, 3-, and 6-months ahead. Also, the M-DWT-ANN model had the best performance. Overall, the results illustrated that using the M-DWT method as preprocessing of input data can be a valuable tool to increase the predictive model's accuracy and efficiency. The results of this study indicate the potential of M-DWT-AI hybrid models to improve GWL forecasting.

A Novel SINS/USBL Tightly Integrated Navigation Strategy Based on Improved ANFIS

Strapdown inertial navigation system (SINS)/ ultra- short baseline (USBL) integrated navigation system is widely used in navigation and positioning of underwater vehicle. In an unknown and complex underwater environment, unstable USBL measurement information can lead to decreased navigation accuracy. In the traditional method, the interference to USBL is uniformly modeled as outliers, but in practice, it is also represented as slowly varying errors, update irregular errors. The latter two errors are not easy to be detected at the beginning of occurrence. Therefore, a SINS/USBL integrated navigation technology based on novel adaptive neural fuzzy inference system (ANFIS) is proposed. This model can detect and compensate abnormal USBL information and maximize USBL information. In order to improve the prediction accuracy of ANFIS, an ANFIS algorithm based on variational Bayesian Kalman filter and principal component analysis (PCA) is proposed to avoid the decrease of positioning accuracy caused by false compensation. The simulation shows that the designed ANFIS algorithm can accurately predict the above anomalies. And it can overcome the problem that USBL measurement anomalies are difficult to be detected at the initial stage. Combined with the proposed integrated navigation technology, it obtain the same positioning accuracy in complex environment as in good environment. The effectiveness and robustness of the proposed method are verified by river experiment.

Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation

Adaptive Neural-Based Fuzzy Inference System and Cooperation Search Algorithm for Simulating and Predicting Discharge Time Series Under Hydropower Reservoir Operation

Compressive strength evaluation of concrete confined with spiral stirrups by using adaptive neuro-fuzzy inference system (ANFIS)

The compressive strength of concrete confined with spiral stirrups was an important parameter to evaluate the load-bearing capacity of concrete columns. The confinement provided by spiral stirrups let concrete under the triaxial compression state and improved the compressive strength of concrete. However, the relationships between concrete and stirrups were complex and the existing prediction models for evaluating the compressive strength of confined concrete were various. In this paper, an adaptive neural-fuzzy inference system (ANFIS) model was developed to evaluate the compressive strength of concrete confined with stirrups. A set of 231 experimental results of concrete confined with spiral stirrups were collected from the previous studies to establish a reliable database. The investigated parameters included the aspect ratio of specimens, the diameter, spacing, yield strength, and volumetric ratio of stirrups, the ratio of longitudinal reinforcement, and the compressive strength of concrete. The results showed that the ANFIS model predicted the compressive strength of confined concrete accurately. By comparing with existing models, the proposed ANFIS model had high applicable and reliability. The effects of the investigated parameters on the compressive strength of concrete were analyzed based on the proposed ANFIS model.

Comparative evaluation of AI ‐based intelligent GEP and ANFIS models in prediction of thermophysical properties of Fe 3 O 4 ‐coated MWCNT hybrid nanofluids for potential application in energy systems

Hybrid nanofluids are gaining popularity owing to the synergistic effects of nanoparticles, which provide them with better heat transfer capabilities than base fluids and normal nanofluids. The thermophysical characteristics of hybrid nanofluids are critical in shaping heat transmission properties. As a result, before using thermophysical qualities in industrial applications, an in-depth investigation of thermophysical properties is required. In this paper, a metamodel framework is constructed to forecast the effect of nanofluid temperature and concentration on numerous thermophysical parameters of Fe3O4-coated MWCNT hybrid nanofluids. Evolutionary gene expression programming (GEP) and an adaptive neural fuzzy inference system (ANFIS) were employed to develop the prediction models. The model was trained using 70% of the datasets, with the remaining 15% used for testing and validation. A variety of statistical measurements and Taylor's diagrams were used to assess the proposed models. The Pearson's correlation coefficient (R), coefficient of determination (R2) was used for the regression index, the error in the model was evaluated with root mean squared error (RMSE). The model's comprehensive assessment additionally includes modern model efficiency indices such as Kling-Gupta efficiency (KGE) and Nash-Sutcliffe efficiency (NSCE). The proposed models demonstrated impressive prediction capabilities. However, the GEP model (R > 0.9825, R2 > 0.9654, RMSE = 0.7929, KGE > 0.9188, and NSCE > 0.9566) outperformed the ANFIS model (R > 0.9601, R2 > 0.9218, RMSE = 1.495, KGE > 0.8015, and NSCE > 0.8745) for the majority of the findings. The generated metamodel was robust enough to replace the repetitive expensive lab procedures required to measure thermophysical properties. Highlights Predictions of thermophysical properties of Fe3O4-coated MWCNT hybrid nanofluid AI-based ANFIS and GEP models performed well on statistical indices ANFIS and GEP-based prognostic models validated and compared with Taylor diagrams

Adaptive neuro fuzzy inference system for predicting sub-daily Zenith Wet Delay

In recent years, the focus of tropospheric studies has evolved to GNSS meteorology and weather forecasting. The Zenith Wet Delay (ZWD), which might be assembled to the Integrated Water Vapour (IWV), is an essential component of the tropospheric delay. Acquiring predicted the ZWD with the required level of accuracy is crucial for weather forecasting. The scope of this study is to use the adaptive neural fuzzy inference system (ANFIS) to predict the ZWD for the following six-hour epoch based exclusively on the present the ZWD value. It was developed and verified using 505 geographically and internationally distributed stations which were used for training and testing from 2008 to 2019. It was assessed based on two criteria. First, the correlation coefficient (R) values were found to be more than 0.8 in 98% of the stations, including those with highest and lowest latitudes, and the remaining 2% of stations located in coastal areas. Second, the Root Mean Square Error (RMSE) values of the differences between the predicted and the actual ZWD were considered to be the more important finding of the study. That is, 99.21% of the 505 stations had the RMSE values equal to or less than 3 cm, with only 4 stations having the RMSE values higher (0.2 mm) than 3 cm. Since the results of this study achieved the required degree of accuracy from the predicted ZWD to be utilized in weather forecasting, they may also be beneficial for GNSS meteorology.

Adaptive neuro fuzzy selection of important factors for prediction of plasmons in silver nanorods.

The major goal of this study was to find predictors of plasmon positions in silver nanorod (NR) optical absorption spectra. The goal of this study is to use an adaptive neural fuzzy inference system to identify the various input parameters for longitudinal surface plasmon resonance (LSPR) and transverse surface plasmon resonance (TSP). A seed strategy has been used for preparation of the silver NRs. During the preparation, the seed particles are synthesized in the presence of cetyltrimethylammonium bromide (CTAB). To produce the silver NRs, metal salt (AgNO3) has been added, as well as ascorbic acid (AA) and CTAB. Skillful prediction could play a pivotal role in the plasmon NR production management. The combination of CTAB and the seeds has the largest influence on the TSPR. The combination of CTAB and AA has the largest influence on the LSPR. The study considering different input parameters simultaneously, to the best of our knowledge, is the first on a small scale and should attract great general interest.

Artificial intelligence in energy industry: forecasting electricity consumption through cohort intelligence & adaptive neural fuzzy inference system

ABSTRACT Demand forecasting is critical for energy systems, as energy is difficult to store and should only be supplied as needed. Researchers attempted to improve forecasts of energy consumption. However, they assume independent factors increase at a constant growth rate, which is unrealistic. Existing methods are designed to determine annual consumption, whereas energy-planning organizations rely on short- or medium-term consumption values. Therefore, we propose a new forecasting framework that introduces new models and scenarios. We apply a cohort intelligence-based adaptive neuro-fuzzy inference system (CI-ANFIS) with a subtractive clustering and grid partition approach to forecast net electricity consumption. One challenge in accurately predicting electricity consumption for specific projection intervals is missing values for factors independent of those known for existing net consumption. Then, we utilize a regression equation scenario approach. We test our framework using a real-world energy consumption dataset and show that our proposed framework outperforms the existing methods.

ANFIS with input space division for modeling magnetorheological energy absorber

A magnetorheological energy absorber (MREA) is a promising actuator for vibration and shock mitigation due to its continuous and controllable damping force. To take full advantage of this feature, a high-fidelity model that can accurately predict the MREA dynamic force is required. Therefore, this study focuses on proposing adaptive neural-fuzzy inference system (ANFIS) models for MREA that use the impact speed, displacement, velocity, acceleration and current as inputs and the MREA force as output. Three types of ANFIS models are developed based on different input space division methods, namely, the grid partition (GP) method, subtractive clustering (SC) algorithm and fuzzy c-means (FCM) algorithm. The best ANFIS model is chosen from each category by six criteria, i.e., mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), coefficient of variation (cov), coefficient of determination (R2) and training time. In addition, the desirability function is proposed to further decide the best model because these criteria do not have a uniform trend. Then, a comprehensive comparative analysis of the three best models is performed. The results show that the ANFIS model with 53 FIS rules generated by the FCM algorithm has the largest global desirability of 0.66782, demonstrating that it is the best one to predict the dynamic force of MREA under high-speed impacts. Accordingly, the values of MAE, MAPE, RMSE, cov and R2 of this model varied in ranges of 0.31679-0.57118, 4.34181-8.43942, 0.44808-1.03095, 6.14134-15.23283 and 0.98973-0.99834, respectively, for various data sets.

Application of neuro-fuzzy estimation in prediction of shear bond strength between concrete layers through the efficient laser roughness analyzer

To ensure the monolithic behavior of reinforced concrete composite parts, the bond strength at the interface between concrete layers cast at various ages must be high. Reinforced concrete composite members include precast beams with cast-in-place slabs, bridge decks strengthened by adding a new concrete layer, and repair and strengthening of existing concrete structural members by adding a new concrete layer. in this study, the shear bond strength (SBS) of reinforcing steel on Portland cement and a hybrid cement including slag and Portland cement activated with sodium carbonate is investigated. The pull-out test was used to determine SBS; also a subsequent test data is evaluated using ANFIS, as well as surface classification utilizing a laser roughness analyzer designed particularly to assess the roughness of the concrete substrate. As a result, this research tried to create an in situ non-destructive approach for assessment of concrete surfaces and its influence on shear bond strength measurement of the concrete layers. All test data is analyzed using an adaptive neural fuzzy inference system (ANFIS) to classify the different input variables for determining the shear bond strength between concrete layers using the mean and maximum surface roughness (SR) height parameters Ra and Rt in X and Y direction. ANFIS was used to optimize the process based on five processing parameters. Skillful prediction could play a pivotal role in the optimal conditions during laser cutting process. Based on results, laser speed is the most influential on the Ra in X and Y direction (RMSE: 0.3255, RMSE: 0.6869, respectively). The most influential parameter on the Rt in X direction is laser power (RMSE: 1.5611), while the most influential parameter on the Rt in Y direction is laser speed (RMSE: 2.0781), resulting that the roughness of the substrate surface highly affects the shear bond strength of concrete interfaces.

Diagnosis of proton exchange membrane fuel cell system based on adaptive neural fuzzy inference system and electrochemical impedance spectroscopy

A new diagnostic method based on adaptive neural fuzzy inference system (ANFIS) and electrochemical impedance spectroscopy (EIS) is proposed for the proton exchange membrane fuel cell (PEMFC) system. Firstly, a new parameter identification method that combines genetic algorithm (GA) and Levenberg–Marquardt (LM) algorithm is proposed to identify the fractional-order equivalent circuit model (ECM), in which the anode impedance, cathode impedance, and mass transfer are all considered. This new method allows better exploitation of the EIS diagrams, and the internal relationships between the fault conditions and the ECM parameters are thoroughly analyzed according to it. Then, based on these relationships, a new diagnostic algorithm based on k-means clustering and ANFIS is designed to precisely identify several faults that can occur in the PEMFC, such as membrane flooding, drying, and mass transfer fault. Finally, the effectiveness of this method is demonstrated experimentally through the exploitation of EIS data under different faults and operating conditions of the PEMFC.

An Adaptive Neural-Fuzzy Inference System for Prediction of Muscle Strength of Farmers in India

In the present study, 13 anthropometric hand dimensions, hand grip strength, push strength, and pull strength of 90 male farmers of Odisha in India were statistically analyzed and then, fuzzy logic toolbox of MATLAB version 2010 was used in order to create the fuzzy inference system (FIS) using ANFIS. The mean hand grip strength with standard deviation was found to be 255.21 N ± 75.46. The average push strength in standing posture for farmers was found to be 193.12 N ± 76.12, whereas pull strength in standing posture was 200.59 N ± 64.02. Very high correlation coefficient i.e. 0.977, 0.994 and 0.990 was obtained between “hand length and hand grip strength”, “hand breadth with thumb and push strength”, and “hand length and pull strength”, respectively. Finally, from the obtained ANFIS models for the prediction of muscle strength, it was concluded that ANFIS could well predict the farmers muscle strength with minimum errors. This will help to evaluate muscle capabilities to avoid musculoskeletal disorders, and in ergonomic design of tools and equipment as a health-care initiative.

Investigation of the Effect of Normalization Methods on ANFIS Success: Forestfire and Diabets Datasets

Machine learning and artificial intelligence techniques are more and more in our lives and studies in this field are increasing day by day. Data is vital for these studies. In order to draw meaningful conclusions from the available data, new methods are proposed and successful results are obtained. The preparation of the obtained data is very important in the studies to be carried out. Data preprocessing is very important in the preparation of data. The most critical stage of the data preprocessing process is the scaling or normalization of the data. Machine learning libraries such as scikit-learn and programming languages such as R provide the necessary libraries to scale data. However, it is not known exactly which normalization method will be applied and which will yield more successful results. The success of these normalization methods has been investigated on many different methods, but such a study has not been done on the adaptive neural fuzzy inference system (ANFIS). The aim of this study is to examine the success of normalization methods on ANFIS in terms of both classification and regression problems. So, for studies using the Anfis method, guidance will be provided on which normalization process will give better results in the data preprocessing stage. Four different normalization methods in the scikit-learn library were applied on the Diabets and Forestfire datasets in the UCI database. The results are presented separately for both classification and regression. It has been determined that min-max normalization in classification problems and working with original data in regression problems are more successful.