Adaptive Network Fuzzy Inference System Research Articles

Short-term hybrid prognostics of fuel cells: A comparative and improvement study

Accurately predicting the performance degradation trend of fuel cells helps take measures in advance and prolong the stack's service life, leading to a novel hybrid forecasting approach. The first grey model prediction method based on residual exponential smoothing optimization (ES-R-GM) can capture the voltage deterioration trend. We explore the integration of two different ES techniques, specifically the double ES (ES2) and cubic ES (ES3), to investigate their effect on enhancing the predictive accuracy of the GM model. The second method of the adaptive network fuzzy inference system (ANFIS) can characterize local nonlinear behavior. We utilize the simulated annealing (SA) algorithm to optimize ANFIS results under different fuzzy rule selection strategies. The outcomes of the two prediction methods mentioned above are combined to create a hybrid prediction using the data fusion method and the moving window technique. Various hybrid methods are evaluated under general conditions and further detailed optimization. The data collected from the experimental platform confirms the suggested hybrid framework. The results show that the hybrid ES3-R-GM + ANFIS-SC method outperformed the single models in final prediction accuracy and can effectively track both global trends and local changes. Simultaneously, it takes less time to calculate than the literature. Moreover, when applied to consistent public datasets, the hybrid approach maintains its robustness and accuracy compared with other hybrid prognostic methods.

Prediction of Power Equipment Emergency Repair Based on Adaptive Neural Network Fuzzy Inference Method

If the emergency repair prediction of power equipment is only made from the perspective of historical spare parts inventory data, it cannot reflect the impact of disaster-causing elements and disaster evolution on the demand for emergency repair spare parts in the future. Therefore, this paper aims to propose a reliable power equipment emergency repair prediction method, and constructs a demand prediction method for emergency repair spare parts of power equipment based on scenario analysis. Constructing a power equipment repair system through intelligent reasoning methods to improve the efficiency of power equipment repair. This article comprehensively uses methods such as literature analysis, model inference, and case simulation verification, this paper innovatively combines the adaptive neural network fuzzy inference system with expert experience. This paper validates the superiority of the prediction method constructed in this paper through comparative analysis. The results show that with the increase of the amount of data, the prediction accuracy of the method proposed in this paper will be improved, which can provide a reference for the subsequent emergency repair prediction of power equipment.

A fuzzy skyhook control strategy optimized by particle swarm strategy for magnetorheological semi-active suspension

In order to solve the problem of unsatisfactory control effect caused by uncertain parameters in the fuzzy skyhook control method, a fuzzy skyhook control strategy optimized by particle swarm strategy is proposed. The magnetorheological (MR) damper is put on the bench for mechanical characteristics experiment, and the forward model of MR damper is established. The adaptive network fuzzy inference system (ANFIS) system in simulation software is used to build its inverse model to verify the accuracy of the forward model of MR damper. A quarter vehicle suspension model is established, and a fuzzy skyhook control strategy based on particle swarm optimization is designed. Numerical simulations are carried out under the excitation of random road. The acceleration, suspension dynamic deflection and tire dynamic load are used to evaluate its performance. Compared with the passive control, the root mean square (RMS) values of the acceleration for the fuzzy skyhook control strategy are improved by 41.9% and 37.6%, the RMS values of the suspension dynamic deflection for the fuzzy skyhook control strategy are improved by 53.3% and 48.0%, and the RMS values of the tire dynamic load for the fuzzy skyhook control strategy are improved by 17.6% and 14.5% under the B-Class and C-Class road excitations. The simulations and experimental results verify the effectiveness of the controller.

Prediction of Air Pollution Utilizing an Adaptive Network Fuzzy Inference System with the Aid of Genetic Algorithm

With the growth of modern lifestyles and the growing urbanization and reliance on fossil fuels, the need for tracking and monitoring air pollution has become more significant. This research used existing information on significant pollutants to forecast their future condition using time-series modeling. Most studies have used Autoregressive Integrated Moving Average (ARIMA) and Logistic Regression (LR) methods to analyze time-series data. Still, employing an Adaptive Neuro Fuzzy Inference System (ANFIS) for this purpose has been infrequent. Conventional time-series prediction approaches use the assumption that there is a linear connection among variables. However, in air pollution modeling, there are non-linear and intricate factors. This paper used an Adaptive Network Fuzzy Inference System with the help of Improved Genetic Algorithm (ANFIS-IGA) to predict air pollution. This work aimed to address this constraint by enhancing the precision of everyday air pollutant prediction via the analysis of time-series data using ANFIS modeling. Air pollution data, including Fine Particulate Matter (FPM), CO, SO2, O3, and NO2, is gathered from the Air Quality Open Data Platform. This research examines the surveillance and prediction of air pollution concentration in indoor and outdoor situations using the ANFIS-IGA model. The model's effectiveness was enhanced and optimized for using IGA. The results indicate that the proposed ANFIS-IGA framework achieved superior performance compared to other models, as shown by the Root Mean Square Error (RMSE) value of 0.052658.

Comparative evaluation of Artificial intelligence based models and kinetic studies in the prediction of biogas from anaerobic digestion of MSW

The generation of biogas from the decomposition of MSW increases greenhouse gas emissions, contributing to global warming. However, the produced biogas can be effectively captured for conversion into a renewable energy resource. This study demonstrates the utility of artificial neural networks (ANN), adaptive network-fuzzy inference systems (ANFIS), and kinetic models in predicting biogas production from the anaerobic digestion of MSW. The digestion time, volatile solids, pH, temperature, and moisture content were selected as the independent variables. Furthermore, kinetic studies were conducted to predict the biogas yield using First-order, modified Gompertz, Logistic, and Transference models. To assess the efficacy of these models, their performance was evaluated using the coefficient of determination (R2) and root mean square error (RMSE). Upon comparing the results, it was evident that for set IX under 100 % field capacity (FC), the ANFIS network exhibited superior performance with RMSE and R2 values of 0.670 and 0.999, respectively. In comparison, the ANN and modified Gompertz models yielded RMSE values of 0.863 and 21.705, and R2 values of 0.999 and 0.998, respectively. While the coefficient of determination (R2 ≥ 0.99) suggested satisfactory agreement between all the models and experimental data, the ANFIS and ANN models achieved notably lower RMSE values, demonstrating their competitive performance.

The Enhanced Wagner–Hagras OLS–BP Hybrid Algorithm for Training IT3 NSFLS-1 for Temperature Prediction in HSM Processes

This paper presents (a) a novel hybrid learning method to train interval type-1 non-singleton type-3 fuzzy logic systems (IT3 NSFLS-1), (b) a novel method, named enhanced Wagner–Hagras (EWH) applied to IT3 NSFLS-1 fuzzy systems, which includes the level alpha 0 output to calculate the output y alpha using the average of the outputs y alpha k instead of their weighted average, and (c) the novel application of the proposed methodology to solve the problem of transfer bar surface temperature prediction in a hot strip mill. The development of the proposed methodology uses the orthogonal least square (OLS) method to train the consequent parameters and the backpropagation (BP) method to train the antecedent parameters. This methodology dynamically changes the parameters of only the level alpha 0, minimizing some criterion functions as new information becomes available to each level alpha k. The precursor sets are type-2 fuzzy sets, the consequent sets are fuzzy centroids, the inputs are type-1 non-singleton fuzzy numbers with uncertain standard deviations, and the secondary membership functions are modeled as two Gaussians with uncertain standard deviation and the same mean. Based on the firing set of the level alpha 0, the proposed methodology calculates each firing set of each level alpha k to dynamically construct and update the proposed EWH IT3 NSFLS-1 (OLS–BP) system. The proposed enhanced fuzzy system and the proposed hybrid learning algorithm were applied in a hot strip mill facility to predict the transfer bar surface temperature at the finishing mill entry zone using, as inputs, (1) the surface temperature measured by the pyrometer located at the roughing mill exit and (2) the time taken to translate the transfer bar from the exit of the roughing mill to the entry of the descale breaker of the finishing mill. Several fuzzy tools were used to make the benchmarking compositions: type-1 singleton fuzzy logic systems (T1 SFLS), type-1 adaptive network fuzzy inference systems (T1 ANFIS), type-1 radial basis function neural networks (T1 RBFNN), interval singleton type-2 fuzzy logic systems (IT2 SFLS), interval type-1 non-singleton type-2 fuzzy logic systems (IT2 NSFLS-1), type-2 ANFIS (IT2 ANFIS), IT2 RBFNN, general singleton type-2 fuzzy logic systems (GT2 SFLS), general type-1 non-singleton type-2 fuzzy logic systems (GT2 NSFLS-1), interval singleton type-3 fuzzy logic systems (IT3 SFLS), and interval type-1 non-singleton type-3 fuzzy systems (IT3 NSFLS-1). The experiments show that the proposed EWH IT3 NSFLS-1 (OLS–BP) system presented superior capability to learn the knowledge and to predict the surface temperature with the lower prediction error.

A risk-based decision making framework to analyze the properties of cobalt–chromium alloys

In this study, the properties of cobalt–chromium alloys are systematically prioritized to aid in the minimization of orthopedic implant failures and risks in biomedical applications. Within the model, six main groups (including a total of 31 properties) are defined: economic aspects, design and production properties, mechanical properties, physical properties, chemical properties and biological properties. A risk-based fuzzy decision making framework is proposed for prioritization. First, a risk-management decision matrix is created by employing interval type-2 fuzzy failure modes and effects analysis. Afterward, the properties of cobalt–chromium alloys are analyzed by utilizing interval type-2 fuzzy measurement of alternatives and ranking according to compromise solution. In the last phase, a prediction model is devised with an adaptive network fuzzy inference system to save computational time and effort and to enable the incorporation of new scientific results into the biomaterial evaluation process. The results of the current study demonstrate that compatibility, osseointegration, corrosion resistance, fatigue resistance and time-dependent deformation are the top five properties contributing to potential orthopedic implant failures and risks. Furthermore, the developed model produces very satisfactory results with acceptable deviations. Consequently, this study presents a new and reliable guide for the unbiased evaluation of cobalt–chromium alloys.

Multiverse optimized ANFIS scheduled fractional ordered proportional-integral-derivative controller for mitigation of frequency excursions in AC microgrid coupled with electric vehicles

ABSTRACT Owing to the current environmental concerns, the RESs have become popular as the microgrid structures for power generation. However, due to capricious weather and loading conditions, the generated power and thereby the microgrid frequency get adversely affected. This instant study puts forth the control strategy for the mitigation of frequency excursions, arising out of step load disturbance, in AC microgrid through adaptive network fuzzy inference system (ANFIS) scheduled fractional ordered proportional-integral-derivative (PID) control optimally tuned with a multiverse optimizer. The control strategy proposition is compared against multi-verse optimized PID and fractional order PI controls. Furthermore, the study investigates as to how EV affects in stabilizing the system frequency in the backdrop of a load disturbance. For a more realistic assessment, the proposition is assessed in the face of system nonlinearities and random load perturbations also to establish its robust and stable behavior. The results prove the efficacy of the multi-verse optimized ANFIS scheduled fractional ordered PID controller. Simulations are executed using MATLAB® software. The results are also validated by experimental studies employing a hardware-in-loop configuration on the OPAL-RT real-time simulator.

Direct expansion (DX) air conditioning (A/C) system control: Hybrid BOANFIS technique

AbstractThis paper describes a novel hybrid technique with fractional order PID controller (FOPID) for simultaneously controlling the humidity of indoor air temperature and the direct expansion (DX) air conditioning (A/C) system. The proposed hybrid system is a joint performance of the butterfly optimization algorithm (BOA) and adaptive network fuzzy inference system (ANFIS), hence forth it is called BOANFIS Technique (BOANFIST). The purpose of the proposed system is to disconnect the temperature and humidity control circuits. The proposed control is modeled and replicated on MATLAB platform and is assessed using existing systems. The statistical performance of the proposed and existing systems of mean, median and standard deviation is also evaluated. It reduces computational time up to 1.01 s and also reduces energy consumption to around 16.42 KWh/day. Furthermore, the simulation outcomes suggest that the proposed technique may efficiently and accurately obtain the optimal global solutions of the proposed technique compared to existing systems.

Predict the compressive strength of ultra high-performance concrete by a hybrid method of machine learning

Ultra-high performance concrete (UHPC) benefits the construction industry due to its improved flexibility, high workability, durability, and performance compared to normal concrete. Some investigators have conducted observed papers on the UHPC’s mechanical properties for establishing a reliable analytical approach for calculating the compressive strength, tensile strength, slump, etc. However, most of these studies were performed with limited samples because of the UHPC’s high cost. This study aims to predict the compressive strength (CS) of UHPC through hybrid machine-learning approaches. The model is included Adaptive-Network Fuzzy Inference System (ANFIS). Moreover, three meta-heuristic algorithms were employed to improve the developed model's accuracy, including the Generalized Normal Distribution Optimization, the COOT optimization algorithm, and the Honey Badger Algorithm. Several metrics were used to compare and assess the performance of the hybrid models in the framework of ANGN, ANCO, and ANHB. A comparison of the predicted and measured results generally shows that the proposed developed models can reasonably estimate the mechanical properties of UHPC. The results indicated that the ANHB model could estimate the CS of UHPC with the most suitable accuracy.

Open-source real-time monitoring system of temperature and force during friction stir spot welding

With the advancements in IIoT tools and applications, real-time monitoring systems with remote access capabilities are gaining great interest. The use of these instruments in monitoring manufacturing processes can lead to enhanced performance and increased efficiency. In this paper, a real-time monitoring system using Raspberry Pi is developed for monitoring the maximum temperature and axial force during friction stir spot welding (FSSW). There are two sensors used in this experiment: a thermocouple and a load cell connected to a microcontroller via Node-RED. Through the use of the system and response surface methodology (RSM), three factors (tool rotation speed, plunge rate, and dwell time) were examined in relation to maximum temperatures and axial forces during FSSW. Models of maximum welding temperature and force were constructed based on multivariate regression and adaptive network fuzzy inference system (ANFIS). The monitoring system proved effective at measuring and tracking temperature and axial force in real time across multiple platforms.

Sustainable Irrigation Requirement Prediction Using Internet of Things and Transfer Learning

Irrigation systems are a crucial research area because it is essential to conserve fresh water and utilize it wisely. As a part of this study, the reliability of predicting the usage of water in the present and future is investigated in order to develop an effective prediction model to communicate demand. In order to improve prediction, we develop a prediction model and share the updated model with nearby farmers. In order to forecast the irrigation requirements, the recommended model utilizes the Internet of Things (IoT), k-nearest neighbours (KNN), cloud storage, long short-term memory (LSTM), and adaptive network fuzzy inference system (ANFIS) techniques. By collecting real-time environmental data, KNN identifies the closest water requirement from the roots and its surrounding. In order to predict short-term requirements, ANFIS is used. To transfer the new requirements for better prediction, transfer learning is used. Time-series-data updates are predicted using LSTM for future forecasting, and the integrated model is shared with other farmers using cloud environments to enhance forecasting and analysis. For implementation, a period of nine to ten months of data was collected from February to December 2021, and banana tree was used to implement the planned strategy. Four farms, with measurements, were considered at varying intervals to determine the minimum and maximum irrigation needs. The requirements of farms were collected over time and compared to the predictions. Future requirements at 8, 16, 24, 32, and 48 h were also anticipated. The results indicated were compared to manual water pouring, and, thus, the entire crop used less water, making our prediction model a real-world option for irrigation. The prediction model was evaluated using R2, MSLE and the average initial prediction value of R2 was 0.945. After using transfer learning, the prediction of the model of Farm-2, 3 and 4 were 0.951, 0.958 and 0.967, respectively.

Intelligent Fuzzy Models: WM, ANFIS, and Patch Learning for the Competitive Forecasting of Environmental Variables

This paper focuses on the application of fuzzy modeling methods in the field of environmental engineering. Since predicting meteorological data is considered to be a challenging task, the current work aimed to assess the performance of various fuzzy models on temperature, solar radiation, and wind speed forecasting. The models studied were taken from the fuzzy systems literature, varying from well-established to the most recent methods. Four cases were considered: a Wang–Mendel (WM)-based fuzzy predictive model, an adaptive network fuzzy inference system (ANFIS), a fuzzy system ensemble, and patch learning (PL). The prediction systems were built from input/output data without any prior information, in a model-free approach. The ability of the models to display high performance on complex real datasets, provided by the National Observatory of Athens, was demonstrated through numerical studies. Patch learning managed to not only display a similar approximation ability to that of strong machine learning models, such as support vector machines and Gaussian processes, but also outperform them on the highly demanding problem of wind speed prediction. More accurately, as far as wind speed prediction is concerned, patch learning produced a 0.9211 root mean squared error for the training data and a value of 0.9841 for the testing data. The support vector machine provided a 0.9306 training root mean squared error and a 0.9891 testing value. The Gaussian process model resulted in a 0.9343 root mean squared error for the training data and a value of 0.9861 for the testing data. Finally, as shown by the numerical experiments, the fuzzy system ensemble exhibited the highest generalisation performance among all the intelligent models.

ANFIS and metaheuristic optimization for strategic joining policy with re-attempt and vacation

This research work deals with the queueing analysis and strategic behavior of primitive users (PUs) and ordinary users (OUs) in cognitive radio system (CRS). Whenever fusion center (FC) is occupied, the ordinary users must decide whether to join a retrial orbit and make re-attempts or depart from the system. The OUs residing in the retrial orbit try to access the spectrum through FC. The FC becomes inactive and considered on vacation in case if there are no users in the CRS. Returning back from the vacation, the FC renders service to the primitive users. The service of ordinary users (OUs) may be interrupted due to breakdown of FC. The probability generating function approach is used to derive the performance indices related to the queueing characterization. The joining strategic behavior of the primitive as well as ordinary users has been investigated by considering a linear reward cost function. The social welfare by formulating the cooperative strategy is studied. The hybrid soft computing technique viz. adaptive network fuzzy inference system (ANFIS) is employed for the numerical simulation and sensitivity analysis of various system metrics. The optimal service rates are determined by minimizing the total cost involved in various activities using the quasi-Newton method (QNM) and metaheuristic algorithms.

Use an adaptive network fuzzy inference system model for estimating the compressive strength of high-performance concrete with two optimizers improved Grey Wolf algorithm and Dragonfly optimization algorithm

Use an adaptive network fuzzy inference system model for estimating the compressive strength of high-performance concrete with two optimizers improved Grey Wolf algorithm and Dragonfly optimization algorithm

The capability of coupled fuzzy logic and adaptive neural network in the formability prediction of steel sheets

In this study, an adaptive neural network fuzzy inference system (ANFIS) is employed to obtain a model demonstrating a cold rolling effect on the forming limits of sheet metals. Artificial intelligence-based methods require valid datasets for training and testing designed neural networks. In this regard, comprehensive experiments are conducted to achieve different thickness reductions in cold rolling for 304L sheet metals. The effect of cold rolling on the uniaxial tensile curves is determined experimentally. In addition, metallography and tensile tests are performed to determine the stretch in grains due to cold rolling. Moreover, experimental FLDs are obtained using the hemisphere punch test. The experimental data are further utilized to train and test the ANFIS. Subsequently, the model is used to predict variations of FLD for cold rolling thickness reduction. It is shown that with extremely lower computational cost in comparison to the experimental method, ANFIS can qualitatively predict the dependency of forming limits on the cold rolling thickness reduction.

Improvement of Rooftop Solar Panels Efficiency using Maximum Power Point Tracking Based on an Adaptive Neural Network Fuzzy Inference System

Rooftop solar panels are a strategy for achieving Indonesia's renewable energy goals, but their non-linear characteristics make them difficult to control, especially in the face of extreme weather changes. An effective controller is needed to optimize the power output of solar panels. This study proposes a Maximum Power Point Tracking (MPPT) controller based on an Adaptive Neural network Fuzzy Inference System (ANFIS) to address this control problem. The capacity of the rooftop solar panels is 3,430-Watt peak (Wp) and they are connected to a 220-Volt (V) grid system. The system is designed, simulated, and analyzed using the Simulink model. The proposed ANFIS MPPT control for rooftop solar panels is compared to Perturb and Observe (P&O) MPPT and no MPPT systems. The simulation results show that in rapid changes in irradiation and extreme temperature, the efficiency of MPPT based on ANFIS is better than P&O MPPT and no MPPT by 0.4523 and 0.1115%, respectively.

Flow direction algorithm and Biogeography-based optimization via fuzzy neural network approach for the prediction of high-performance concrete strength property

Concrete is one of the most useful materials in the construction industry. Conventional concrete comprises additives such as cement, water, and aggregates. This concrete cannot be used for very important and sensitive structures. For this reason, high-performance concrete (HPC) has been used to achieve the desired and more suitable compressive strength by employing some additives. The additional variable is cement, fly ash, blast furnace slag, superplasticizer, fine aggregate, and coarse aggregate. On the other hand, to obtain a mixture of these materials, laboratory work is not economical and saves time. Therefore, soft-based modeling is the order of the day to solve this problem. The adaptive network fuzzy inference system model is one of the ways to achieve compressive strength close to the laboratory model, which is a smart modeling move. This model has to be optimized to get better and more satisfying results, which is done by two optimizers, Biogeography-Based Optimization (BBO) and Flow Direction Algorithm (FDA), which have bright created and powerful for better performance. Furthermore, in the outputs of these two models, BBO-ANFIS and FDA-ANFIS, certain errors and desired percentages are used to select the most suitable and ideal model for the desired output, i.e., the compressibility of concrete in high-performance concrete. In the relevant modeling, the number of evaluators in the ANFISBBO combined model is R2 = 0.8926, RMSE = 5.0406, MAE = 3.7145, A20-index = 0.8382 and U95 = 13.881, and in ANFISFDA, R2 = 0.912, RMSE = 4.7294, MAE = 3.5367, A20-index = 0.8414, and U95 = 13.054 is obtained. According to the obtained numbers, it is clear that the ANFISFDA combined model has been able to get better results than the BBO-ANFIS model.

Integration of renewable energy sources in the dynamic voltage restorer for improving power quality using ANFIS controller

Nowadays, grid power loss and power quality deterioration are causing many problems, especially for sensitive uses such as data centers, airports, healthcare, banking, insurance, and telecom. These issues must be resolved through energy continuity coverage and optimization. Dynamic voltage restorers (DVR) are generally used to mitigate network voltage disturbances and maintain a constant voltage value between load terminals for a short and limited duration due to limited energy storage facilities. However, this paper's proposed (DVR) system controlled by the adaptive-network fuzzy inference system (ANFIS) controller can compensate for prolonged power quality disturbances by integrating a hybrid renewable energy system (HRES) managed using classical Proportional Integral (PI) controller power management. This paper proposes a new (DVR) topology coupled with a hybrid renewable energy system (HRES) to exploit free and clean energy, consisting of a solar panel, a PEM fuel cell, and a battery storage device connected through DC-DC converters to a DC transmission, the obtained results from the simulation process of the proposed DVR system in the environment of MATLAB / Simulink showed the ability to eliminate sag that exceeded 0.9 pu in a period of more than 3 min and swells that exceeded 1.2 pu in a period that exceeded one minute. In comparison, the load voltage's total harmonic distortion THDv was reduced from 29% to 5%, and the source current total harmonic distortion THDi from 30.25% to 2.79%.

ANFIS-EKF-Based Single-Beacon Localization Algorithm for AUV

Singe-beacon localization technology can help Autonomous Underwater Vehicles (AUVs) to obtain precise positions by deploying only one beacon. It is considered as a promising way, benefiting from saving much time and labor compared with traditional Long-Baseline Localization (LBL). A typical single-beacon localization scheme contains two essential questions: the initial observability problem and long-endurance trajectory tracking problem. Aiming at these core problems, a comprehensive solution for single-beacon localization is described in this paper. An multi-hypothesis initial position discriminant method is proposed firstly, which helps to achieve accurate initial location based on observability analysis. Then, an Adaptive Network Fuzzy Inference System (ANFIS)-improved Extended Kalman Filter (EKF) method is proposed, in which single-beacon measuring information is fused with off-the-shelf sensors, including DVL, Compass, etc. ANFIS-EKF can help to improve trajectory tracking precisions by restraining the heavy loss of linearization in conventional EKF. Both simulation and field tests are conducted to verify the performance of the proposed algorithms.