Neural Fuzzy System Research Articles

Proton Exchange Membrane Fuel Cells: An Effective Neural Fuzzy System for Optimal Power Tracking

Proton Exchange Membrane Fuel Cells: An Effective Neural Fuzzy System for Optimal Power Tracking

Design and application of interval type-2 fuzzy neural network systems optimized with hybrid algorithms

Interval type-2 fuzzy neural network systems (IT2 FNNSs) own the advantages of IT2 fuzzy logic systems (FLSs) and NN. The paper designs a kind of IT2 FNNSs for permanent magnetic drive (PMD) forecasting. For each rules of IT2 FNNSs, whose antecedents, consequents and input measurements are chosen as Gaussian IT2 membership functions (MFs). The proposed hybrid backpropagation (BP) algorithms and recursive least square (RLS) algorithms are adopted to tune all the parameters simultaneously. Simulation instances on the basis of data of permanent magnetic drive (PMD) torque and revolutions per minute (rpm) are adopted for testing the performances of proposed hybrid optimized IT2 FNNSs for forecasting. Convergence analysis illustrates that the IT2 FNNSs have excellent generalization capability compared with both singleton and non-singleton T1 FNNSs.

Design and application of fuzzy neural network systems optimized with hybrid algorithms

Design and application of fuzzy neural network systems optimized with hybrid algorithms

A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems

A novel approach for energy consumption management in cloud centers based on adaptive fuzzy neural systems

The method of assessing the effectiveness of the security of confidential data of the distributed information system

The paper proposes a method of assessing the effectiveness of the security of confidential data of a distributed information system, based on a model for determining the current threats to the security of confidential data in the information system, on algorithms of fuzzy inference and the theory of fuzzy neural systems, unlike known ones, it uses sufficient and necessary indicators, excludes expert errors , increases the detection of actual threats to the security of confidential information system data by 5%, reduces the cost of purchasing information protection tools from 15 to 30%. It takes into account the following factors: the IT infrastructure of the distributed information system, the capabilities of attackers and their level of motivation in the information system. The proposed approach differs from existing ones by an automated process, the need to involve highly qualified specialists in the field of information security, and low computational complexity; absence of deficiencies in expert assessments; allows you to determine the list of current information security threats in information systems of various classes and types. The task of ensuring the security of confidential data is urgent, which is due to the growth of computer attacks and leaks of information, which are reflected in the statistical data on the commission of crimes in the field of high technologies, the growth of criminal activity using modern communication devices and the Internet. The existing methods of identifying current threats to information security and assessing the effectiveness of the security of confidential data cannot be used at all stages of the life cycle of distributed information systems, they do not take into account the following indicators in the complex: IT infrastructure of distributed systems, current threats to information security, security requirements of confidential data, their cost as important indicators when solving these problems. One of the most important tasks of ensuring the security of confidential data is the evaluation of the effectiveness of the protection system. In this regard, the goal of the research is to improve the quality of the assessment of the effectiveness of the security of confidential data of a distributed information system by determining sufficient and necessary indicators using modern information technologies, which allow the most effective solution of the following tasks: determining the parameters of the adaptive production fuzzy neural systems, which most suitable for solving the tasks, the application of Data Science technologies in data processing, algorithms of fuzzy output.

AI-assisted prediction of St14 steel sheets formability: Neural-fuzzy systems and crystal plasticity assessments

We've introduced a new technique leveraging artificial intelligence to determine in-plane forming limit strains in St14 sheet metals. A neural-fuzzy AI system was devised to predict safe points on forming limit diagrams, considering aspects like crystal texture, metal sheet thickness, and spherical punch diameter. Neural-fuzzy systems could be utilized in prediction of forming limit curves using linguistic data description. To obtain the data required for training the AI network, in-depth tests, including XRD, metallography, tensile testing, and Nakazima's hemisphere evaluations, were carried out to detail the metallurgical and mechanical properties of St14 sheet metal. Data on texture, the tensile curves, and grain morphology were then incorporated in crystal plasticity models to find hardening constants for St14's single crystals. Using these determined values, Nakazima's tests were replicated through multi-crystal aggregate configurations. We then undertook a comprehensive parameter investigation via 324 crystal plasticity simulations to shed light on the effects of texture, sheet depth, and punch dimensions on St14's formability. This rich dataset paved the way for training an adaptive neural fuzzy inference system (ANFIS), leading to substantial reductions in time and computational needs. The results validate that the ANFIS model offers accurate and reliable predictions, highlighting its viability for wider use in determining forming limit diagrams.

Real time adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural network proportional-integral-derivative controller for nonlinear systems

This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties.

Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases

With the rise of social media and the internet, the rapid dissemination of information has increased the likelihood of reputation infringement. This study utilizes judicial big data and AI to analyze intrinsic connections in reputation infringement cases, aiding judges in delivering consistent rulings. The challenge lies in balancing freedom of speech with the right to reputation and addressing the ambiguity and subjectivity in infringement cases. This research constructs a structured reputation infringement case dataset from Chinese Judgments Online. It introduces a Fractional Fuzzy Neural System (FFNS) to tackle the vagueness in reputation infringement acts and judicial language, enhancing prediction accuracy for case outcomes. The FFNS, integrating fractional calculus, fuzzy logic, and neural networks, excels in adaptability and nonlinear modeling. It uses fractional order fuzzy membership functions to depict the extent and severity of reputation infringement accurately, combining these outputs with neural networks for predictive analysis. The result is a more precise adjudication tool, demonstrating significant potential for judicial application.

Adaptive Particle Swarm Optimization of a Photovoltaic System under Partial Shading

The solar photovoltaic (PV) energy is the most prevalent and popular source of energy. But the PV output characteristics are mainly depending on temperature and irradiance and are nonlinear in nature. Therefore, PV array characteristics greatly vary with change in the atmospheric condition. Under partial shading condition (PSC), PV modules will not receive the same level of incident solar irradiance throughout the system due to some obstructions such as: dust, cloudy weather, shadows of nearby objects: buildings, trees, mountains, birds etc… which causes mismatch in PV module characteristics of the PV array and losses arise in the entire PV configuration. Consequently, power extraction from the PV system is reduced and the PSC on the PV array can be minimized by the proper selection of PV configurations, physical relocation of the PV modules and maximum power point tracking techniques (MPPT) to overcome this problem. The present article studies and compares the MPPT based on the Adaptive particle swarm optimization (APSO) algorithm under partial and completely shaded. The perturbation and observation (P&O) method is widely due to its simplicity and easy implementation but the Intelligent and hybrid control such as: fuzzy logic control (FLC) and adaptive neural fuzzy inference system (ANFIS) can track the MPP with better efficiency but in a long time compared to conventional approaches. In addition, these methods need big data for good results and the data problem is regulated with the evolutionary algorithms and searching the duty cycle (d) in a shorter time than FLC and ANFIS. The principle of PSO, grey wolf optimization (GWO), and APSO techniques is the search for a global solution, and it have good behaviour under PSC but APSO can be classified as best solution between the studied approaches. The simulation results, which are presented in MATLAB/Simulink software, show the effectiveness of the proposed APSO technique.

Cloud center energy consumption control for predictability in neural fuzzy systems

Cloud computing provides tool-based IT services to customers worldwide. It is built on a fee-for-service model and enables the hosting of large programs from consumer, scientific, and commercial domains. However, cloud application hosting data centers have enormous energy consumption, which leads to high operating expenses and a carbon footprint. Cloud centers get a lot of requests from different users every day. The centers need strong servers that consume a lot of energy and peripherals in order to run in order to meet these needs. It should go without saying that reducing cloud center energy usage requires efficient resource utilization. We have taken a novel hybrid method in this study. Neural fuzzy systems have been employed for the dynamic resource allocation in the cloud with the goal of minimizing energy consumption and load prediction, and the ant colony optimization method has been utilized for the virtual machine's movement. The experimental results show that the proposed strategy works optimally, as compared to other methods in the literature that have the same objective. When compared to other comparable approaches, the suggested solution has an average resource loss of 23.1 % in 720 time periods and an average request denial rate that is 6.5 % lower. Moreover, extensive testing with the SPECpower benchmark has confirmed the proposed methodology, yielding a forecast accuracy of over 97 % for the proposed energy consumption model.

Model of current threats to security of confiden-tial data in divisional information systems

The paper proposes a model for determining actual threats to the information security of distributed information systems, based on algorithms of fuzzy inference and the theory of fuzzy neural systems, unlike known ones, uses sufficient and necessary indicators, excludes expert errors, increases the detection of the number of actual threats to information security of distributed systems by 5 %, reduces the cost of purchasing information protection equipment from 15 to 30%. It takes into account the following factors: the IT infrastructure of the distributed information system, the capabilities of attackers and their level of motivation in the distributed information system, the list of existing protection tools in the distributed information system.
 The proposed approach differs from existing ones in the following: lack of involvement of highly qualified specialists in the field of information security; the process is automated, has a low computational complexity; absence of deficiencies in expert assessments; allows you to determine the list of current information security threats in information systems of various classes and types.
 The task of ensuring the security of confidential information becomes the most urgent, which is due to the growth of computer attacks and leaks of information, which are reflected in the statistical data on the commission of crimes in the field of high technologies, the growth of criminal activity using modern communication devices and the Internet.
 Existing methods of modeling current threats to information security and assessing the effectiveness of the information protection system cannot be used at all stages of the life cycle of distributed information systems - they do not take into account the following indicators in the complex: IT infrastructure of distributed information systems, current threats to information security, security requirements for confidential information, a list of means of protecting confidential information and their value as important indicators when solving these problems.
 In order to achieve the goals of ensuring the security of confidential information, it is necessary to: organize the effective creation of an information protection system (information security system), effective modeling (identification of the list) of current threats to information security, identification of the current violator, and also provide the opportunity to conduct a qualitative assessment of the effectiveness of the information security (protection) system.
 One of the most important tasks of ensuring the security of confidential information is the assessment of the effectiveness of the protection (security) system. In this regard, the goal of the work (research) is to improve the quality of the evaluation of the effectiveness of the protection (security) systems of distributed information systems by determining sufficient and necessary evaluation indicators using modern (promising) information technologies that allow the most effective solution of the following tasks: determination operating parameters of adaptive production fuzzy neural systems, which are most suitable for solving the tasks, application of Data Science technologies in data processing, fuzzy output algorithms.

Interval Type-2 Mutual Subsethood Cauchy Fuzzy Neural Inference System (IT2MSCFuNIS)

An interval type-2 (IT2) mutual subsethood Cauchy fuzzy neural inference system has been proposed in this paper. The network architecture consists of 3-layers with all connection weights being IT2 Cauchy fuzzy membership functions (CMFs). The crisp inputs to the system are fuzzified into IT2CMFs with fixed centers and uncertain spreads. The hidden layer represents the rule-based knowledge. The firing degree of the antecedent part of each rule at the hidden layer is computed by aggregating the product of the mutual subsethood similarity measures between the inputs and the connection weights. A volume defuzzification is used to compute the numeric output. A gradient descent back-propagation algorithm is used to train the model. The novelty of the proposed model is threefold. First, is enriching the theory of the mutual subsethood fuzzy neural models by adopting the Cauchy membership function (CMF) as another powerful fuzzy basis function (FBF) rather than the classical choice of Gaussian fuzzy membership functions (GMFs). Second, is the success of computing the mutual subsethood similarity measure between the IT2CMFs and all the model parameters’ updating equations in analytic closed-form formulas, not numerically or approximately. Third, is the ability to extract the type-1 (T1) mutual subsethood Cauchy fuzzy neural inference system (T1MSCFuNIS) with all its analytic closed-form formulas directly as a special case from the general formulas of IT2MSCFuNIS model. Such a novelty makes the proposed model a concrete and effective development of the theory of mutual subsethood fuzzy neural models. Both IT2MSCFuNIS and T1MSCFuNIS models have been tested using different examples from the domains of function approximation, classification, and prediction. The results ensure the efficacy of both models compared with other models reported in the literature.

Enhancing Physical Education: A Dynamic Fuzzy Neural Network-Based Information Processing System Design

Enhancing Physical Education: A Dynamic Fuzzy Neural Network-Based Information Processing System Design

Applying AI and fuzzy modeling to optimize advertising effectiveness in digital marketing strategies for the energy sector

The purpose of the study is to formulate the main aspects of the use of artificial intelligence in advertising and determine its effectiveness in the energy sector. The intensity of development of artificial intelligence technologies and its application to ensure the effectiveness of advertising activities of companies in the energy sector is emphasized. The ethical and practical aspects of using artificial intelligence in advertising are presented. An analysis of the efficiency factors of the advertising process was carried out based on the classification of tools and methods of advertising automation using artificial intelligence technologies. Practical recommendations for the use of neural networks based on artificial intelligence have been formed. The provisions of the classical and meta-heurastic methods of parametric identification of a fuzzy system, which is a model of the marketing process of companies in the energy sector, are substantiated. The effectiveness of advertising was determined using the methodology of modeling fuzzy systems and the sequence of computational steps of a fuzzy neural system. The practical significance of the results lies in the fact that metaheuristic methods make it possible to assess the effectiveness of advertising based on the use of artificial intelligence tools by companies in the energy sector.

Estimation reference crop evapotranspiration (ET0) using artificial intelligence model in an arid climate with external data

Water resource management and crop growth control require the calculation of reference evapotranspiration (ET0), but meteorological data can often be incomplete, necessitating models with minimal inputs. This study was conducted in Iran’s arid synoptic stations of Sirjan and Kerman, where data scarcity is severe. Penman–Monteith FAO-56 was selected as the target data for modeling artificial neural network (ANN), fuzzy neural adaptive inference system (ANFIS), and ANN-gray wolf optimization (ANN-GWO). The performance of these models was evaluated using an input dataset consisting of the current station’s minimum and maximum temperatures, ET0, and the wind speed of the nearby station (external data) in three different combinations. The models’ accuracy was assessed using two widely used criteria: root mean square error (RMSE) and coefficient of determination (R2), as well as the empirical Hargreaves equation. In the absence of climatic data, the ANFIS, ANN, and ANN-GWO methods using minimum and maximum temperatures, which are relatively easier to estimate, outperformed the empirical Hargreaves equation method in both stations. The results demonstrate that the ANFIS method performed better than ANN and ANN-GWO in all three input combinations. All three methods showed improvement when external data (wind speed and ET0 of the adjacent station) were used. Ultimately, the ANFIS method using minimum and maximum temperatures and the adjacent station’s ET0 in Kerman and Sirjan yielded the best results, with an RMSE of 0.33 and 0.36, respectively.

Evaluation of Interactive College Piano Teaching's Effect Based on Artificial Intelligence Technology

In recent years, with the economic development of various countries, education has also made great leaps, and piano teaching has been popularized and promoted in various universities. Pianos are also put into use in universities through normal channels. According to the survey, there are certain drawbacks in the teaching process of piano, which hinder the teaching effect to a great extent. In order to better adapt to the interactive teaching mode, this paper takes the flipped classroom in piano teaching as an example to conduct a systematic study based on the method of artificial intelligence. This paper introduces three typical artificial intelligence technologies and analyzes the three evaluative indicators corresponding to the effect of piano interactive teaching. The analysis results show that the prediction effect based on fuzzy neural inference system is the best. In addition, the three-dimensional display of the prediction data shows that the prediction effect obtained by artificial intelligence shows a strong consistency with the actual evaluation effect.

A Innovative Methodology to Fuzzy-Neural System Modeling

A Innovative Methodology to Fuzzy-Neural System Modeling

Prediction of drill penetration rate in drilling oil wells using mathematical and neurofuzzy modeling methods

The sustainability of oil and gas well drilling operations is a vital issue from an economic and safety point of view. One of the practical methods to predict the continuation of the drilling operation is the analysis of the penetration rate of the drilling operations. Statistical and experimental studies have shown that the drilling depth, the drill weight, and the rotary table's rotation speed are the parameters that directly affect the penetration rate. However, due to their mutual influence on each other and the occurrence of the sticking-sliding phenomenon, predicting the penetration rate in the appropriate range is a complex and challenging issue. In this paper, using the field data of oil and gas wells located in the southwest of Iran, the effect of these parameters on each other was studied in the framework of statistical modeling, neural networks, and neural fuzzy systems. Using the ANFIS method and with a correlation coefficient of over 90%, a successful four-dimensional model of drill penetration rate was presented in terms of changes in drilling depth, the weight of the drill, and the rotational speed of the rotary table. By analyzing this model, it was found that the penetration rate of the drill decreases with the increase of the drilling depth, and to keep it within the acceptable range, the rotational speed and weight of the drill should be changed based on the presented policy.

Stability of tracking wheel mobile robot with teleoperation fuzzy neural network control system

The stability of the Tracking Wheel Mobile Robot with Teleoperation System and Path Following Method is discussed in this study. The path is to be tracked by the host computer which is the master robot. The response from the robot is captured on camera. As the slave robot approaches the target position, the camera captures the response robot’s position and as well as moving trajectory. The host computer receives all of the images, enabling mobile robot deviation recoveries. The slave robot can use teleoperation to follow the sensor based on the decisions made by the master robot. The Lyapunov function in the Fuzzy Neural Network (FNN) control structure assures the system’s stability and satisfactory performance. It supports a mobile robot’s ability to adhere to a reference trajectory without deviating from it. Finally, the outcome of the simulation demonstrates that our controller is capable of tracking different environmental conditions and maintaining stability.

Assessing the impacts of climate change on streamflow dynamics: A machine learning perspective.

This study investigates changes in river flow patterns, in the Hunza Basin, Pakistan, attributed to climate change. Given the anticipated rise in extreme weather events, accurate streamflow predictions are increasingly vital. We assess three machine learning (ML) models - artificial neural network (ANN), recurrent neural network (RNN), and adaptive fuzzy neural inference system (ANFIS) - for streamflow prediction under the Coupled Model Intercomparison Project 6 (CMIP6) Shared Socioeconomic Pathways (SSPs), specifically SSP245 and SSP585. Four key performance indicators, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2), guide the evaluation. These models employ monthly precipitation, maximum and minimum temperatures as inputs, and discharge as the output, spanning 1985-2014. The ANN model with a 3-10-1 architecture outperforms RNN and ANFIS, displaying lower MSE, RMSE, MAE, and higher R2 values for both training (MSE = 20417, RMSE = 142, MAE = 71, R2 = 0.94) and testing (MSE = 9348, RMSE = 96, MAE = 108, R2 = 0.92) datasets. Subsequently, the superior ANN model predicts streamflow up to 2100 using SSP245 and SSP585 scenarios. These results underscore the potential of ANN models for robust futuristic streamflow estimation, offering valuable insights for water resource management and planning.