Adaptive Network Fuzzy Inference System Research Articles

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.

Adaptive Network Fuzzy Inference System and Particle Swarm Optimization of Biohydrogen Production Process

Green hydrogen is considered to be one of the best candidates for fossil fuels in the near future. Bio-hydrogen production from the dark fermentation of organic materials, including organic wastes, is one of the most cost-effective and promising methods for hydrogen production. One of the main challenges posed by this method is the low production rate. Therefore, optimizing the operating parameters, such as the initial pH value, operating temperature, N/C ratio, and organic concentration (xylose), plays a significant role in determining the hydrogen production rate. The experimental optimization of such parameters is complex, expensive, and lengthy. The present research used an experimental data asset, adaptive network fuzzy inference system (ANFIS) modeling, and particle swarm optimization to model and optimize hydrogen production. The coupling between ANFIS and PSO demonstrated a robust effect, which was evident through the improvement in the hydrogen production based on the four input parameters. The results were compared with the experimental and RSM optimization models. The proposed method demonstrated an increase in the biohydrogen production of 100 mL/L compared to the experimental results and a 200 mL/L increase compared to the results obtained using ANOVA.

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.

Developing Cluster-Based Adaptive Network Fuzzy Inference System Tuned by Particle Swarm Optimization to Forecast Annual Automotive Sales: A Case Study in Iran Market

Developing Cluster-Based Adaptive Network Fuzzy Inference System Tuned by Particle Swarm Optimization to Forecast Annual Automotive Sales: A Case Study in Iran Market

A Q-learning fuzzy inference system based online energy management strategy for off-road hybrid electric vehicles

In this paper, a Q-learning fuzzy inference system (QLFIS)-based online control architecture is proposed and applied for the optimal control of off-road hybrid electric vehicles (HEVs) to achieve better dynamic performance, fuel economy and real-time performance. A dynamic model, including a hybrid system, vehicle dynamics and road model, is established to obtain the state feedback according to the current driving environment under command. The optimal control strategy and objective function are both constructed by an adaptive network fuzzy inference system (ANFIS) due to its strong approaching ability. The fuzzy rules and parameters are trained online through the Q-learning algorithm and gradient descent method. This control framework provides a new control idea for the control of off-road vehicles. Without knowing the driving cycle in advance, it achieves a good control effect for different driving environments through online data collection and training. The QLFIS-based control strategy is compared to dynamic programming (DP)-based and rule-based strategies based on two different off-road driving cycles through simulation. The simulation results show that the vehicle dynamic performance and fuel economy are improved with respect to the rule-based strategy, while the calculation time is greatly reduced compared to that of the DP-based strategy.