Adaptive Fuzzy Inference Research Articles

Design of Efficient Storage Unit and EP-ANFIS Controller for On-grid and Off-grid Connected PV-WT System

The controllers developed so far for the on-grid and off-grid operation is based on frequency regulation at grid and have yield poor switching by inducing oscillation. Hence to solve this problem in this paper, the switching between the on-grid and off-grid are made by the Emperor penguin based Adaptive fuzzy neuro inference system (EP-ANFIS) controller, which works based on the energy supplied to the load. To serve the transient load condition the hybrid storage unit is modelled by social-ski driver algorithm (SSD) that will schedule the energy supplement between the grid and generator. The proposed controller model provides stable operation during the switching process that is without the transient oscillation the switching is smooth. The error in data selection for the ANFIS is reduced by the Emperor penguin optimization (EPO) as 0.1 for the test data. The proposed work is implemented in Matlab/Simulink platform. The results are compared with the existing works in terms of voltage, current, power, and THD. When examining for transient load condition the settling time for the controller to the steady state is 0.78 s which is comparatively low with PID, PI, ANFIS, and ANFIS-PSO controllers. The THD is reduced to 9.11% from the existing methods by maintain the fundamental frequency of 50 Hz.

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.

Modeling and estimation of fouling factor on the hot wire probe by smart paradigms

The fouling factor is a complex non-linear function of several feature variables. Accurate estimation of this measurable factor is applicable for controlling the heat transfer efficiency of heat exchangers. Artificial neural network (ANN) and adaptive neural-based fuzzy inference system (ANFIS) techniques are accurate approaches for the understanding treatment of the most complicated systems. The cascade-feedforward (CFF), multi-layer perceptron (MLP), radial basis function (RBF), and generalized regression (GR) neural networks and ANFIS were applied for predicting the fouling factor of the hot wire probe from some measured variables using a huge databank, including 1870 empirical dataset. Pearson's and Spearman's techniques confirmed the highest relation between considered features and the first order of fouling factor. The results demonstrate that the cascade feed-forward neural network containing eight hidden neurons was the best artificial intelligence (AI) model with excellent overall AARE = 3.44 %, MSD = 0.0000315, RMSD = 0.0056, and R2 = 0.9982. This work's significance lies in presenting an applicable and accurate tool for estimating fouling factors on hot wire probes to the research community and industry. This model can be considered a reliable replacement for empirical analyses that are often expensive and time-consuming.

Genetic Algorithm-Optimized Adaptive Network Fuzzy Inference System-Based VSG Controller for Sustainable Operation of Distribution System

To achieve a more sustainable supply of electricity and reduce dependency on fuels, the application of renewable energy sources-based distribution systems (DS) is stimulating. However, the intermittent nature of renewable sources reduces the overall inertia of the power system, which in turn seriously affects the frequency stability of the power system. A virtual synchronous generator can provide inertial response support to a DS. However, existing active power controllers of VSG are not optimized to react to the variation of frequency changes in the power system. Hence this paper introduces a new controller by incorporating GA-ANFIS in the active power controller to improve the performance of the VSG. The advantage of the proposed ANFIS-based controller is its ability to optimize the membership function in order to provide a better range and accuracy for the VSG responses. Rate of change of frequency (ROCOF) and change in frequency are used as the inputs of the proposed controller to control the values of two swing equation parameters, inertia constant (J) and damping constant (D). Two objective functions are used to optimize the membership function in the ANFIS. Transient simulation is carried out in PSCAD/EMTDC to validate the performance of the controller. For all the scenarios, VSG with GA-ANFIS (VOFIS) managed to maintain the DS frequency within the safe operating limit. A comparison between three other controllers proved that the proposed VSG controller is better than the other controller, with a transient response of 22% faster compared to the other controllers.

Модель нейронной сети для прогнозирования предэвакуационного поведения людей при пожаре

This article discusses a comprehensive study of pre-evacuation behavior of people in the event of an emergency. In this regard, it is advisable to use machine learning approaches, in particular neural networks, for data mining in the field of security. Statistical data obtained in emergency situations may be limited and generally uncertain, for this reason it is recommended to choose a neural network architecture - adaptive fuzzy inference Network System (ANFIS) based on the Takagi–Sugeno fuzzy inference system. The neural network architecture considered in the article in the form of an adaptive fuzzy inference network system architecture consists of five layers, where each layer performs a well-defined function. Data on the behavioral reaction of people before the evacuation to train an artificial neural network were obtained using such approaches as interviewing, questionnaire, survey.

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.

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

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

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.

ADFIST: Adaptive Dynamic Fuzzy Inference System Tree Driven by Optimized Knowledge Base for Indoor Air Quality Assessment.

Air quality levels do not just affect climate change; rather, it leaves a significant impact on public health and wellbeing. Indoor air pollution is the major contributor to increased mortality and morbidity rates. This paper is focused on the assessment of indoor air quality based on several important pollutants (PM10, PM2.5, CO2, CO, tVOC, and NO2). These pollutants are responsible for potential health issues, including respiratory disease, central nervous system dysfunction, cardiovascular disease, and cancer. The pollutant concentrations were measured from a rural site in India using an Internet of Things-based sensor system. An Adaptive Dynamic Fuzzy Inference System Tree was implemented to process the field variables. The knowledge base for the proposed model was designed using a global optimization algorithm. However, the model was tuned using a local search algorithm to achieve enhanced prediction performance. The proposed model gives normalized root mean square error of 0.6679, 0.6218, 0.1077, 0.2585, 0.0667 and 0.0635 for PM10, PM2.5, CO2, CO, tVOC, and NO2, respectively. This approach was compared with the existing studies in the literature, and the approach was also validated against the online benchmark dataset.

Research on Traffic Accident Risk Prediction Based on Adaptive Neural Fuzzy Inference and Interpolation System

Research on Traffic Accident Risk Prediction Based on Adaptive Neural Fuzzy Inference and Interpolation System

Adaptive Neuro Fuzzy Inference System Based controller for Electric Vehicle's hybrid ABS braking

To improve braking safety, modern electric vehicles are equipped with both friction and regenerative braking systems. The combined use of these two modes is mainly carried out in ABS operation. This paper deals with the problem of electric braking torque control in ABS mode. The EV considered is equipped with a traction motor of the interior permanent magnet synchronous machine type (IPMSM). Using field-oriented control (FOC) strategy, two torque regulators, namely adaptive fuzzy PID (AFPID) and adaptive neuro-fuzzy inference system (ANFIS) are developed and their control performances are analyzed and compared. In order to perform a realistic evaluation of the two aforementioned regulators, the hybrid ABS system, consisting of a slip controller, a braking torque references distribution law (electrical/friction), a friction brake, and an electric brake (IPMSM) is considered. Based on several performance indices, e.g. torque ripple, flux ripple, inverter switching frequency, control accuracy, dynamic response, and algorithm complexity, a comparison is made by simulation using Matlab /Simulink.

Trapezoidal type-2 fuzzy inference system with tensor unfolding structure learning method

In the paper, trapezoidal type-2 fuzzy inference system (TT2FIS) is introduced. To construct the type-2 fuzzy inference system, trapezoidal type-2 fuzzy sets are adopted to construct the antecedent part of the fuzzy rules, the centers of the fuzzy sets are roughly clustered by an evolving autonomous data partitioning algorithm. To further eliminate the effect of the centers that are generated by dataset itself, and it can be easily impacted by the unbalance data, the data clouds that are generated by autonomous data partitioning algorithm are filtered by Hammersley sequence, and symmetrical trapezoidal type-2 fuzzy sets are generated by the generated cluster centers and problem-free standard deviation generating method. To the consequent part, generalized type-2 TSK consequent is formulated by tensor, which is obtained via three constituent parts of the trapezoidal type-2 fuzzy sets (lower membership function, upper membership function and type-reduction set of data samples), the parameters of consequent part are the iterative results of a matrix equation that is unfolded from the tensor. Finally, simulation results are carried out to verify the effectiveness of the proposed trapezoidal type-2 fuzzy inference system. Simulation results show that the generalization of the TT2FIS is better than the coincide type-2 fuzzy inference system or adaptive type-1 fuzzy inference system.

Estimation of the bond strength between FRP and concrete using ANFIS and hybridized ANFIS machine learning models

Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) algorithms were utilized to produce numerical tools for predicting the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets. From the relevant literature, a credible database encompassing 242 test specimens was developed, along with six input factors that primarily determine bond strength. These characteristics include the beam's width, the compressive strength of the concrete, the FRP thickness, the FRP modulus of elasticity, the FRP length, and the FRP width. Finally, using conventional statistical metrics, the outputs of the two suggested models (ANFIS and ANFIS-PSO) were compared to the experimental data. Both models were shown to be a good alternative strategy for predicting the bond strength of FRP-to-concrete.

Estimation of the bond strength between FRP and concrete using ANFIS and hybridized ANFIS machine learning models

Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) algorithms were utilized to produce numerical tools for predicting the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets. From the relevant literature, a credible database encompassing 242 test specimens was developed, along with six input factors that primarily determine bond strength. These characteristics include the beam's width, the compressive strength of the concrete, the FRP thickness, the FRP modulus of elasticity, the FRP length, and the FRP width. Finally, using conventional statistical metrics, the outputs of the two suggested models (ANFIS and ANFIS-PSO) were compared to the experimental data. Both models were shown to be a good alternative strategy for predicting the bond strength of FRP-to-concrete.

Estimation of the bond strength between FRP and concrete using ANFIS and hybridized ANFIS machine learning models

Adaptive Neuro-Based Fuzzy Inference System (ANFIS) and Particle Swarm Optimization (PSO) algorithms were utilized to produce numerical tools for predicting the bond strength between the concrete surface and carbon fiber reinforced polymer (CFRP) sheets. From the relevant literature, a credible database encompassing 242 test specimens was developed, along with six input factors that primarily determine bond strength. These characteristics include the beam's width, the compressive strength of the concrete, the FRP thickness, the FRP modulus of elasticity, the FRP length, and the FRP width. Finally, using conventional statistical metrics, the outputs of the two suggested models (ANFIS and ANFIS-PSO) were compared to the experimental data. Both models were shown to be a good alternative strategy for predicting the bond strength of FRP-to-concrete.

An Integrated of Hydrogen Fuel Cell to Distribution Network System: Challenging and Opportunity for D-STATCOM

The electric power industry sector has become increasingly aware of how counterproductive voltage sag affects distribution network systems (DNS). The voltage sag backfires disastrously at the demand load side and affects equipment in DNS. To settle the voltage sag issue, this paper achieved its primary purpose to mitigate the voltage sag based on integrating a hydrogen fuel cell (HFC) with the DNS using a distribution static synchronous compensator (D-STATCOM) system. Besides, this paper discusses the challenges and opportunities of D-STATCOM in DNS. In this paper, using HFC is well-designed, modeled, and simulated to mitigate the voltage sag in DNS with a positive impact on the environment and an immediate response to the issue of the injection of voltage. Furthermore, this modeling and controller are particularly suitable in terms of cost-effectiveness as well as reliability based on the adaptive network fuzzy inference system (ANFIS), fuzzy logic system (FLC), and proportional–integral (P-I). The effectiveness of the MATLAB simulation is confirmed by implementing the system and carrying out a DNS connection, obtaining efficiencies over 94.5% at three-phase fault for values of injection voltage in HFC D-STATCOM using a P-I controller. Moreover, the HFC D-STATCOM using FLC proved capable of supporting the network by 97.00%. The HFC D-STATCOM based ANFIS proved capable of supporting the network by 98.00% in the DNS.

Illuminant Estimation Using Adaptive Neuro-Fuzzy Inference System

Computational color constancy (CCC) is a fundamental prerequisite for many computer vision tasks. The key of CCC is to estimate illuminant color so that the image of a scene under varying illumination can be normalized to an image under the canonical illumination. As a type of solution, combination algorithms generally try to reach better illuminant estimation by weighting other unitary algorithms for a given image. However, due to the diversity of image features, applying the same weighting combination strategy to different images might result in unsound illuminant estimation. To address this problem, this study provides an effective option. A two-step strategy is first employed to cluster the training images, then for each cluster, ANFIS (adaptive neuro-network fuzzy inference system) models are effectively trained to map image features to illuminant color. While giving a test image, the fuzzy weights measuring what degrees the image belonging to each cluster are calculated, thus a reliable illuminant estimation will be obtained by weighting all ANFIS predictions. The proposed method allows illuminant estimation to be dynamic combinations of initial illumination estimates from some unitary algorithms, relying on the powerful learning and reasoning capabilities of ANFIS. Extensive experiments on typical benchmark datasets demonstrate the effectiveness of the proposed approach. In addition, although there is an initial observation that some learning-based methods outperform even the most carefully designed and tested combinations of statistical and fuzzy inference systems, the proposed method is good practice for illuminant estimation considering fuzzy inference eases to implement in imaging signal processors with if-then rules and low computation efforts.

Estimating algorithms for prediction and spread of a factor as a pandemic: a case study of global COVID-19 prevalence

Background: This paper presents open-source computer simulation programs developed for simulating, tracking, and estimating the COVID-19 outbreak. Methods: The programs consisted of two separate parts: one set of programs built in Simulink with a block diagram display, and another one coded in MATLAB as scripts. The mathematical model used in this package was the SIR, SEIR, and SEIRD models represented by a set of differential-algebraic equations. It can be easily modified to develop new models for the problem. A generalized method was adopted to simulate worldwide outbreaks in an efficient, fast, and simple way. Results: To get a good tracking of the virus spread, a sum of sigmoid functions was proposed to capture any dynamic changes in the data. The parameters used for the input (infection and recovery rate functions) are computed using the parameter estimation tool in MATLAB. Several statistic methods were applied for the rate function including linear, mean, root-mean-square, and standard deviation. In addition, an adaptive neuro-fuzzy inference system (ANFIS) is employed and proposed to train the model and predict its output. Conclusion: Due to the fact that COVID-19 disease has not been detected in a large part of the passengers, because they do not have any symptoms yet. For this purpose, in setting our forecast model, two scenarios are considered for the probability of a symptomatic person to travel until 1) the first medical visit 2) hospitalization. Therefore, in the present study, an open source toolbox for modeling, simulation and estimating the prevalence of coronavirus was presented. This procedure is presented in such a way that it can be generalized and applied in other parts and applications of estimating the scenarios of an event, including the potential of several models, including SIR, which is sensitive to pollution, etc. In addition, several statistical measures were used to determine the optimal time parameters for sigmoid functions. also, an adaptive fuzzy neural inference system was used to generate model output based on some of the training tasks applied to the system. This program can be used as an educational tool or for research studies and this article promises some lasting contributions to field of COVID-19.