Intelligent Fuzzy Research Articles

Modeling of the tunnel kiln control system taking into account the features of the pid controller and fuzzy logic

Taking into account the non-linear, time-varying temperature characteristics of the tunnel kiln, high inertia and some uncertainty of its parameters, it’s difficult to create an accurate mathematical model and get an acceptable set of the controller of the control system. The asynchronous motor actuator of the system, which regulates the amount of gas and air to change the temperature, gives the system first-order a statism. A conventional PID controller does not provide the required quality parameters of temperature control in the entire possible range of changes in the parameters of the controlled object. Thus, the synthesis of an additional fuzzy PID controller is required to ensure the required quality of regulation. Of course, PID control and fuzzy intelligence will provide effective self-tuning of the parameters of controlled PID controllers. A simulation experiment in Matlab shows that fuzzy PID control reduces the time and magnitude of overshoot, effectively increasing the stability of the system by giving it robustness properties.

Stratum Displacement Law and Intelligent Optimization Control Based on Intelligent Fuzzy Control Theory During Shield Tunneling

The laws of Stratum displacement and optimal control are critical for shield operation. This article’s focus is made on the intelligent fuzzy control theory concentrating on earth pressure, total thrust, driving speed, cutter torque, grouting pressure and grouting volume as the main elements of the study. A model of intelligent fuzzy control theory based on the model of No. 9 Line of Guangzhu Rail transit, on the Tianma river shield section. The paper also analyzes stratum displacement law due to shield tunnelling, executes & analyses intelligent controls for optimization of parameters, combining the five two-dimensional structures of the double structure of fuzzy control system. According to the observations made on the model. The model is upto date and the control of all parameters develops stably. The parameter ranges should be controlled as follows: earth pressure, 0.19 ~ 0.22Mpa; total thrust, 1100 ~ 1350T; driving speed, 38 ~ 50mm / min; cutter torque, 1600 ~ 2300 KN • m; grouting pressure, 0.19 ~ 0.25Mpa and grouting volume, 30 ~ 50L/min. Keywords: Shield tunnel, intelligent fuzzy control, Stratum displacement, optimal control DOI: 10.7176/CER/13-6-01 Publication date: October 31 st 2021

Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

As the number of students in universities continues to grow, the university academic management system has a large amount of data on student performance. However, the utilization of these data is only limited to simple query and statistical work, and there is no precedent of using these data for improving English teaching mode. With the application of fuzzy theory in machine learning and artificial intelligence, the fuzzy decision tree algorithm was born by integrating fuzzy set theory with decision tree algorithm. In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. In addition, this paper analyzes the influence of nearest neighbor distance on classification, introduces Gaussian weight function, gives different voting weights to the neighborhood according to the distance, and establishes a weighted K-nearest neighbor classification algorithm. To address the problem of low classification efficiency of K-nearest neighbor algorithm when the dataset is large, this paper further improves the algorithm and establishes the partitioned weighted K-nearest neighbor algorithm. The classification time was shortened from 11.39 seconds to 5.22 seconds, and the classification efficiency greatly improved.

Intelligent Fuzzy Control with State-Derivative Feedback for Takagi-Sugeno Fuzzy Stochastic Singular Systems

This document deals with the problem of passive fuzzy controller design with the state-derivative feedback approach for the nonlinear stochastic singular systems. Recently, the singular systems have a greater focus on literature because they can keep more physical system characteristics than conventional systems. At first, the Takagi-Sugeno fuzzy stochastic singular models are used to represent the nonlinear stochastic singular systems. Then, the state-derivative feedback approach and parallel distributed compensation method are employed to design the passive fuzzy controllers. In the design process, the Lyapunov stability conditions are developed subject to multiple performance constraints, including the passivity constraint and decay rate constraint. According to these Lyapunov stability conditions, the proposed fuzzy control problem can be effectively shifted into the linear matrix inequality problem that can be solved by using the convex optimal programming algorithm. At last, two examples are provided to verify the applicability and effectivity of the proposed passive fuzzy controller design approach.

Photographic Image Intelligent Fuzzy Assistant Teaching System Based on Augmented Reality and Web

Due to the limitation of time and space, the traditional photographic image intelligent fuzzy teaching system can not provide targeted auxiliary teaching for students with different learning abilities. The design of intelligent fuzzy assistant teaching system of photographic image based on augmented reality and web is carried out, including the hardware design of server and peripheral equipment selection, and the design of intelligent fuzzy assistant teaching system of photographic image based on Web and augmented reality, such as intelligent fuzzy assistant teaching display, student learning behavior evaluation software design, etc. Experiments show that, compared with the traditional system, the photographic image intelligent fuzzy assistant teaching system based on augmented reality and web has more correct allocation of teaching resources, stronger pertinence and practical value.

Evaluation of Systems Current Status by PCA-RBF Neural Network and Novel Fuzzy Intelligence Method

The health and the sustainability of a higher education system are crucial to the prosperity of a nation. It is crucial to effectively evaluate a higher education system in various aspects and promptly adjust the corresponding educational policies. Firstly, 23 higher education quality indicators with respect to more than 1,000 universities are carefully collected and converted to the corresponding indicators of 40 countries/regions. Moreover, such indicators are normalized by the range transformation method in order to facilitate subsequent analyses. Secondly, a novel fuzzy intelligence (F&I) method is proposed to model the health states of different higher education systems based on radial basis function (RBF) neural networks. This method firstly reduces the input indicators’ dimension by the principal component analysis (PCA) technique. Using the PCA results as the input layer, the RBF neural network is carefully trained and the F&I score of each country/region is therefore obtained. Next, the hierarchical cluster analysis is carried out to depict the health state of each county/region. 1, 5, 5 and 29 countries/regions are categorized as healthy, good, general and unhealthy, respectively.

An Intelligent Fuzzy Logic Based Greenhouse System Modeling for the Antalya Area

An Intelligent Fuzzy Logic Based Greenhouse System Modeling for the Antalya Area

Embedded Intelligence on FPGA: Survey, Applications and Challenges

Embedded intelligence (EI) is an emerging research field and has the objective to incorporate machine learning algorithms and intelligent decision-making capabilities into mobile and embedded devices or systems. There are several challenges to be addressed to realize efficient EI implementations in hardware such as the need for: (1) high computational processing; (2) low power consumption (or high energy efficiency); and (3) scalability to accommodate different network sizes and topologies. In recent years, an emerging hardware technology which has demonstrated strong potential and capabilities for EI implementations is the FPGA (field programmable gate array) technology. This paper presents an overview and review of embedded intelligence on FPGA with a focus on applications, platforms and challenges. There are four main classification and thematic descriptors which are reviewed and discussed in this paper for EI: (1) EI techniques including machine learning and neural networks, deep learning, expert systems, fuzzy intelligence, swarm intelligence, self-organizing map (SOM) and extreme learning; (2) applications for EI including object detection and recognition, indoor localization and surveillance monitoring, and other EI applications; (3) hardware and platforms for EI; and (4) challenges for EI. The paper aims to introduce interested researchers to this area and motivate the development of practical FPGA solutions for EI deployment.

Solving Fredholm integral inclusion for L-fuzzy mappings

The main focus of this paper is to solve the Fredholm integral inclusion for L-fuzzy mapping in the setting of complete b-metric spaces. To attain this target, we obtain some common L-fuzzy fixed point results for L-fuzzy mappings under F-contraction and derive the main results of Ahmad et al. [Fuzzy fixed points of fuzzy mappings via F-contractions and an applications, J. Intelligent Fuzzy Syst. 38 (2019), pp. 1–7] and Cosentino et al. [Solvability of integrodifferential problems via fixed point theory in b-metric spaces, Fixed Point Theor. Appl. 70 (2015), pp. 1–15]. To justify the notions and outcomes, we also demonstrate an appropriate example.

WITHDRAWN: Intelligent Fuzzy controller based N-T control of 5Φ SRM

Abstract The usage of outdated energy devices contributing to environmental pollution is becoming ever more concerned. The industry is forced to find new ideas without lowering demands. Motors are not very effective in the white good and other manufacturing applications of today. The brushed Electric engine comes here. However it is maintained highly and has a low speed due to the brushes used in elements. In recent years, the Brushless DC engine was successfully used in the electronic switching process and this motor retains the location sensors necessary for motor operation and magnet costs as main engine components. The 5μ switched reticence motor (SRM), including easy construction, high reliabilities and low cost, has a range of desired features. However the huge torque ripples, the output torque and the magnetization features are extremely non-uniform and the noise is high. Several experiments were carried out using the Direct Torque Control (DTC) technique to minimize torque ripple for SRM. This project focuses on the implementation of Five-Phase 10/8 SRM Direct Torque Controls (DTC), which provides many advantages over traditional voltage control and current chopping mode controls including convenient algorithms, lower torque and immediate torque response. The 5-phase 10/8 SRM DTC approach is proposed. The engine output is shown in Matlab/Simulink through computer simulation. The result is then evaluated by comparing the corresponding results with a 5-phase 10/8 engine response.

A Novel Method for a COVID-19 Classification of Countries Based on an Intelligent Fuzzy Fractal Approach.

We outline in this article a hybrid intelligent fuzzy fractal approach for classification of countries based on a mixture of fractal theoretical concepts and fuzzy logic mathematical constructs. The mathematical definition of the fractal dimension provides a way to estimate the complexity of the non-linear dynamic behavior exhibited by the time series of the countries. Fuzzy logic offers a way to represent and handle the inherent uncertainty of the classification problem. The hybrid intelligent approach is composed of a fuzzy system formed by a set of fuzzy rules that uses the fractal dimensions of the data as inputs and produce as a final output the classification of countries. The hybrid approach calculations are based on the COVID-19 data of confirmed and death cases. The main contribution is the proposed hybrid approach composed of the fractal dimension definition and fuzzy logic concepts for achieving an accurate classification of countries based on the complexity of the COVID-19 time series data. Publicly available datasets of 11 countries have been the basis to construct the fuzzy system and 15 different countries were considered in the validation of the proposed classification approach. Simulation results show that a classification accuracy over 93% can be achieved, which can be considered good for this complex problem.

On the Reliability of Intelligent Fuzzy System for Multivariate Pattern Scrutinization of Power Consumption Efficiency of Mechanical Front Wheel Drive Tractor

On the Reliability of Intelligent Fuzzy System for Multivariate Pattern Scrutinization of Power Consumption Efficiency of Mechanical Front Wheel Drive Tractor

Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model

Since the beginning of the new century, risk events such as the world economic crisis have occurred, which have greatly impacted the real economy of our country. A wireless network is a network implemented using wireless communication technology. It includes both global voice and data networks that allow users to establish long‐distance wireless connections, as well as infrared technology and radio frequency technology optimized for short‐distance wireless connections. These events have a great impact on many small‐ and medium‐sized listed companies, resulting to many small‐ and medium‐sized listed companies going bankrupt. Indeed, one of the important reasons for the frequent bankruptcy of small‐ and medium‐sized listed companies is the lack of awareness of risk prevention and effective financial risk early warning mechanism. The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization. This method shows many unique advantages when dealing with classification problems and has been widely used in many fields. The purpose of this article is to realize the financial risk analysis of listed companies through wireless network communication and the optimal fuzzy SVM artificial intelligence model, which help small‐ and medium‐sized listed companies find abnormalities in their business management activities in advance and deal with market risks in a timely manner. Taking 81 small‐ and medium‐sized listed companies as the research object, this paper chooses the small‐ and medium‐sized listed companies in every quarter of 2018 as the research sample. By using the financial and nonfinancial data of small‐ and medium‐sized listed companies and introducing the support vector machine (SVM) with the fuzzy method, the model of the fuzzy support vector machine (FSVM) is constructed. And the performance of the FSVM under four different kernel functions is compared and studied. At the same time, the performance of the FSVM is compared with other artificial intelligence models. The empirical results show that different kernel functions have different effects on the prediction performance of the FCM‐SVM model. Under the Gauss radial basis function, the prediction accuracy of the FCM‐SVM is over 86%. It can be seen that in predicting the financial crisis of small‐ and medium‐sized listed companies, the FCM‐SVM model with Gauss radial basis function has the best predictive performance. The FSVM model based on Gauss radial basis function not only has the advantages of linearity, being polynomial, and nonlinearity of neurons but also is significantly superior to the traditional artificial intelligence model.

An intelligent fuzzy PI controller based 33 level switched capacitor multilevel inverter for PMSM drives

This paper presents an intelligent Fuzzy PI controller based switched capacitor 33 level multilevel inverter for PMSM Drives and the advantages of proposed SCMLI is proved. Compared to this suggested topology, the conventional MLI topology is compared with an indifferent viewpoint, such as the number of switches needed, output voltage levels and power supply. In order to further improve torque ripple, anti-interference capability and speed response, a new fuzzy PI vector speed control system with an adaptable proportion factor familiar by the RBF neural network must be created. The proposed design is implemented in MATLAB/ Simulink and the performance is improved for proposed circuit is achieved with experimental findings.

Redefining Critical Tasks for Responsive and Resilient Scheduling-an Intelligent Fuzzy Heuristic Approach

Rapidly changing market has accentuated the need for developing robust and responsive schedules. Intelligent heuristics techniques have been widely used to provide scheduling alternatives for resource constrained scheduling and identify critical activities essential for monitoring and controlling these processes. We argue that traditional parameters based solely on slack calculation are insufficient to characterize tasks as critical and instead define a fuzzy parameter-based approach, which relies on process and task-specific information. Proposed parameter is used to rank activities on a scale based on their criticality to develop a heuristic. It was observed that the proposed intelligent heuristic performed better on well-known benchmark datasets in terms of both lead times and computation time. An improvement of 1.5–2.5 percent was observed in traditional performance measure of order lead times while for non-traditional criterion measuring robustness, improvement of 30–35 percent was observed. Furthermore, current research provides meaningful insight on critical tasks at the shop-floor.

An Intelligent Fuzzy Logic Based Traffic Controller

Increasing road congestion, travel time, number of accidents, carbon dioxide emissions, and fuel consumption are some of the consequences of growth in the vehicle population. Therefore, intelligent traffic controllers are required to solve road traffic congestion problems. The results of prevalent methods, including preset cycle time controller and vehicle-actuated controller, indicated that they do not effectively perform at traffic peak moments. Therefore, due to the deficiency of common methods, fuzzy logic based traffic signal controllers have attracted a lot of attention among researchers. In this article, a fuzzy logic based algorithm for 4-way intersections is proposed and it consists of two main stages for sorting the phase and determining the green light duration. The proposed system is simulated in the MATLAB programming environment and the performance of the designed controller and a conventional controller is compared for some of the presumed conditions. The results of applying the proposed system indicate that this algorithm has a better performance in different traffic conditions in contrast to a preset cycle time controller and it can reduce the number of vehicles behind traffic lights at intersections and the waiting time of passengers.

A medical analytical system using intelligent fuzzy level set brain image segmentation based on improved quantum particle swarm optimization

Medical image segmentation demonstrates a significant part in curative image exploration and dispensation, is a multifaceted and perplexing assignment for reckoning efficiency and dissection precision. Segmenting an image is essential to dissection different components of the image, which is prominent fact to identify region of defect accurately. An Intelligent Fuzzy Level Set Method (IFLSM) along with an over-all search proficiency of Improved Quantum Particle Swarm Optimization (IQPSO) for image segmentation is proposed to improve the steadiness and meticulousness thus aiming at reduction of opening sensitivity. The proposed algorithm aims at optimizing the opening contours by utilizing the IQPSO method in addition with intelligent fuzzy clustering method, and segments the image using enhanced Level Set Method (LSM). A stable cluster head is identified using the comprehensive quest aptitude of IQPSO. The iteration period will also provide a pre-segmentation contour which is nearer to Region of Interest (ROI). The implementation of the proposed work for segmenting brain tissues through Magnetic Image Resonance (MRI) images provides an optimized result which is 15% more than the original FLSM algorithm. The obtained contours from the proposed work shows more stability than the original FLSM. The proposed work shows a promising significant improvement in the image segmentation process.

An Intelligent Fuzzy Logic-Based Content and Channel Aware Downlink Scheduler for Scalable Video over OFDMA Wireless Systems

The recent advancements of wireless technology and applications make downlink scheduling and resource allocations an important research topic. In this paper, we consider the problem of downlink scheduling for multi-user scalable video streaming over orthogonal frequency division multiple access (OFDMA) channels. The video streams are precoded using a scalable video coding (SVC) scheme. We propose a fuzzy logic-based scheduling algorithm, which prioritises the transmission to different users by considering video content, and channel conditions. Furthermore, a novel analytical model and a new performance metric have been developed for the performance analysis of the proposed scheduling algorithm. The obtained results show that the proposed algorithm outperforms the content-blind/channel aware scheduling algorithms with a gain of as much as 19% in terms of the number of supported users. The proposed algorithm allows for a fairer allocation of resources among users across the entire sector coverage, allowing for the enhancement of video quality at edges of the cell while minimising the degradation of users closer to the base station.

Design and implementation of fuzzy logic based modified real-reactive power control of inverter for low voltage ride through enhancement in grid connected solar PV system

In a grid connected solar PV system, during grid faults, increase in grid current, dip in voltage at point of common coupling and overshoot in dc link voltage in the inverter side are observed. The solar PV system gets disconnected from the grid whenever the grid voltage goes below a certain nominal value in order to safeguard the cascaded power converter systems from high currents and over voltages. This paper proposes an intelligent fuzzy based real and reactive power control of inverter for effective Low Voltage Ride Through (LVRT) capability during grid faults. The proposed fuzzy logic based modified inverter real and reactive power control strategy, stabilizes the grid voltage by injecting reactive current and limits the grid over current by reducing active power flow into the grid. To regulate the overshoot in dc link voltage during grid faults, a braking chopper and dynamic braking resistor strategies are included in the proposed LVRT scheme. The performance of the proposed inverter control strategy is studied through simulation of 100 kW grid connected solar PV system in MATLAB/SIMULINK 2018a platform. The algorithm is validated in real time hardware of 1 kW grid connected solar PV system. The simulation and hardware results validate the superior performance of the proposed inverter LVRT control strategy in comparison with other conventional schemes during grid faults.

Power Quality Improvement using Intelligent Fuzzy-VLLMS Based Shunt Active Filter

The power quality problem in the power system is increased with the use of non-linear devices. Due to the use of non-linear devices like power electronic converters, there is an increase in harmonic content in the source current. Due to this there is an increase in the losses, instability and poor voltage waveform. To mitigate the harmonics and provide the reactive power compensation, we use filters. There are different filters used in the power system. Passive filters provide limited compensation, so active filters can be used for variable compensation. In this paper, a shunt active filter has been made adaptive using a Variable Leaky Least Mean Square (VLLMS) based controller. Proposed adaptive controller can be able to compensate for harmonic currents, power factor and nonlinear load unbalance. DC capacitor voltage has been regulated at a desired level using a PI controller and a self-charging circuit technique. But, this scheme has two disadvantages such as, tuning issues of current controller pre-requisites the traditional PI controller, which is controlled by intelligent based Fuzzy-Logic controller for achieving good performance features. The design concept of proposed intelligent Fuzzy controller for shunt active filter has been verified through simulation analysis and results are presented with proper comparisons.