Fuzzy Logic Algorithm Research Articles

Path Planning Technique for Mobile Robots: A Review

Mobile robot path planning involves designing optimal routes from starting points to destinations within specific environmental conditions. Even though there are well-established autonomous navigation solutions, it is worth noting that comprehensive, systematically differentiated examinations of the critical technologies underpinning both single-robot and multi-robot path planning are notably scarce. These technologies encompass aspects such as environmental modeling, criteria for evaluating path quality, the techniques employed in path planning and so on. This paper presents a thorough exploration of techniques within the realm of mobile robot path planning. Initially, we provide an overview of eight diverse methods for mapping, each mirroring the varying levels of abstraction that robots employ to interpret their surroundings. Furthermore, we furnish open-source map datasets suited for both Single-Agent Path Planning (SAPF) and Multi-Agent Path Planning (MAPF) scenarios, accompanied by an analysis of prevalent evaluation metrics for path planning. Subsequently, focusing on the distinctive features of SAPF algorithms, we categorize them into three classes: classical algorithms, intelligent optimization algorithms, and artificial intelligence algorithms. Within the classical algorithms category, we introduce graph search algorithms, random sampling algorithms, and potential field algorithms. In the intelligent optimization algorithms domain, we introduce ant colony optimization, particle swarm optimization, and genetic algorithms. Within the domain of artificial intelligence algorithms, we discuss neural network algorithms and fuzzy logic algorithms. Following this, we delve into the different approaches to MAPF planning, examining centralized planning which emphasizes decoupling conflicts, and distributed planning which prioritizes task execution. Based on these categorizations, we comprehensively compare the characteristics and applicability of both SAPF and MAPF algorithms, while highlighting the challenges that this field is currently grappling with.

A novel hybrid technique using fuzzy logic, neural networks and genetic algorithm for intrusion detection system

As the number of people who use the internet continues to rapidly increase, security has become an increasingly important concern in the online world. Many researchers in the past have developed detection systems to identify and detect intruders using data mining. These systems can be found in use today. However, the existing methods had the disadvantages in terms of detection accuracy and time overhead. To enhance the IDS detection accuracy and reduces the required time a novel intrusion detection system is proposed that will ensure the safety of data communication by locating any unauthorized users and efficiently identifying any unwanted visitors to wireless networks. In this paper, a hybrid algorithm is proposed for the removal of uncertainty and the prediction of outcomes. Fuzzy logic is utilized in the process of removing uncertainty, whereas neural networks are utilized for the purpose of prediction. The Genetic Algorithm is utilized in order to effect improvements in the accuracy of prediction results. Experiments have been carried out in order to evaluate the proposed intrusion detection system, which has an overall detection accuracy of 99.12 %. The proposed model's performance has been put to evaluation utilizing tenfold cross-validation, which has been completed.

Hierarchical energy-saving routing algorithm using fuzzy logic in wireless sensor networks

Currently, sensor energy assembly in wireless sensor networks is limited, and clustering methods are not effective to improve sensor energy consumption rate. Thus, a hierarchical energy-saving routing algorithm based on fuzzy logic was constructed by considering three aspects: residual energy value, centrality, and distance value between nodes and base stations. The remaining sensor nodes selected by fuzzy logic algorithm have a longer time to live and greater residual energy than those selected by low-power adaptive clustering hierarchical protocol algorithm, fuzzy unequal clustering algorithm, and fuzzy logic cluster head election algorithm. For network life cycle, the number of rounds in which the first dead node appears, in descending order, is studied: energy-saving routing algorithm (400 rounds) > new geographic cellular structure algorithm (300 rounds) > virtual grid based dynamic routes adjustment algorithm (100 rounds). Under the same experimental round, energy-saving routing algorithm’s remaining energy curve always reaches its maximum. The energy-saving routing algorithm by fuzzy logic constructed by this research institute can significantly improve network energy utilization, which has certain reference value.

Battery Charge Control in Solar Photovoltaic Systems Based on Fuzzy Logic and Jellyfish Optimization Algorithm

The study focuses on the integration of a fuzzy logic-based Maximum Power Point Tracking (MPPT) system, an optimized proportional Integral-based voltage controller, and the Jellyfish Optimization Algorithm into a solar PV battery setup. This integrated approach aims to enhance energy harvesting efficiency under varying environmental conditions. The study’s innovation lies in effectively addressing challenges posed by diverse environmental factors and loads. The utilization of MATLAB 2022a Simulink for modeling and the Jellyfish Optimization Algorithm for PI-controller tuning further strengthens our findings. Testing scenarios, including constant and variable irradiation, underscore the significant enhancements achieved through the integration of fuzzy MPPT and the Jellyfish Optimization Algorithm with the PI-based voltage controller. These enhancements encompass improved power extraction, optimized voltage regulation, swift settling times, and overall efficiency gains.

ωA-IvE methodology: Admissible interleaving entropy methods applied to video streaming traffic classification

This work focuses on the width-based interval fuzzy entropy notion, considering the interval data diameter as a measure of the lack of knowledge and uncertainty related to the precise membership degrees of elements in an interval-valued fuzzy set. The w-preserving notion relates the uncertainty from input to output in system information. Such a concept generates a new entropy by applying width-based average functions and admissible order to compare interval data and define width-based fuzzy connectives in data fuzzy computations. A new admissible total order is introduced, requesting just one injective and increasing function, illustrated by a Decimal Digit Interleaving (DDI) function. The proposal methods are based on the admissible interleaving order and related expressions for width-based interval entropy considering different conditions for composition among width-based interval fuzzy operators, as negations with equilibrium and average functions, also including idempotent aggregation function and restricted equivalence functions. Finally, we illustrate the application of the proposed methods for solving a video streaming traffic classification problem. The admissible interleaving entropy analysis is performed over the attributes modeling the FuzzyNetClass approach, a computational model for traffic classification related to video streaming, exploring the integration of inference systems based on interval-valued fuzzy logic and machine learning algorithms. The results by comparison with other width-based interval fuzzy entropy methods were promising and pointed to continuing study and research efforts.

A hybrid fuzzy logic/genetic algorithm model based on experimental data for estimation of cuttings concentration during drilling

Adequate hole cleaning is a key to economic drilling operations. However, cuttings transport during drilling is affected by different operational parameters, geometric parameters and, cuttings and drilling fluid related parameters which makes the cuttings transport phenomenon a complicated modeling exercise. This complexity has led to the use of computational intelligence (CI) methods such as fuzzy logic and genetic algorithm (GA). A total of 11 parameters are considered in the current work while developing Mamdani type fuzzy logic model using 702 experimental observations. The developed FL model (FL2) is further combined with GA to optimize fuzzy rule weights which has led to the development of a hybrid CI model termed FL3 in this paper. The performance of FL3 over two test datasets is compared with FL2 and a previously published FL model (Chowdhury and Hovda, 2022) termed FL1 (built on 509 experimental observations and 195 fuzzy rules). Model comparison using three goodness of fit metrics (R2, RMSE and MAE) shows that the hybrid CI model, FL3, outperforms both FL1 and FL2 over both test datasets. R2 value of FL3 over both test datasets is ‘very good’ following (Moriasi et al., 2007). The difference of FL3 cuttings concentration (Cc) estimates and the measured Cc values is less than 1% for more than two thirds of the experimental data for both test datasets collected at two different geographical locations by two different research groups – TUDRP and SINTEF. The reasons behind FL3 outperforming FL1 and FL2 are data randomization, modified fuzzy sets for eccentricity and drillstring rotation based on the exploratory data analysis published earlier (Chowdhury et al., 2023), use of more experimental data and the optimization of fuzzy rule weights by GA.

Development and image-guided control of a robotic endoscope system for nasotracheal intubation

Nasotracheal intubation (NTI) is an advanced airway management technique, in which a flexible endoscope is inserted into the patient’s trachea from the nasal cavity. Due to the hand-eye coordination requirement, the operation of the flexible endoscope in a narrow workspace is a challenge, even for experienced surgeons. In this study, a novel robotic nasotracheal intubation system (RNIS) is proposed to automatically steer the endoscope along the upper respiratory tract. The configuration of the RNIS meets the motion requirement of the flexible endoscope. A lumen center detection method with Kalman filter is designed to determine the position of the lumen center in endoscopic images. Then, a fuzzy logic-based control strategy is developed to enable the RNIS to achieve automatic endoscope steering. Control parameters are updated online using a fuzzy logic algorithm to handle uncertainty. Movements in three degrees of freedom of the endoscope are controlled by the RNIS using visual feedback. Finally, experiments based on a motion simulator and a training mannequin are conducted. Results demonstrate that the proposed RNIS can successfully steer the flexible endoscope along the upper respiratory tract. Automated robotic endoscopes outperform manual operations in terms of operational accuracy.

OPTIMIZING PLANT DISEASE PREDICTION: A NEURO-FUZZY-GENETIC ALGORITHM APPROACH

In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time.

Efficient combined fuzzy logic and LMS algorithm for smart antenna

Efficient combined fuzzy logic and LMS algorithm for smart antenna

ADVANCEMENTS IN MEDICAL DIAGNOSTICS THROUGH FUZZY LOGIC: A COMPREHENSIVE REVIEW

Fuzzy logic has emerged as a groundbreaking tool in medical diagnostics, addressing the complexities and uncertainties inherent in clinical data. Developed by Lotfi Zadeh in the 1960s, fuzzy logic extends classical logic to handle vagueness and imprecision, making it particularly valuable in interpreting diverse and often ambiguous clinical information. Unlike traditional models that rely on precise data, fuzzy logic employs degrees of truth, allowing for a more flexible and realistic analysis of patients' health conditions. Recent studies have demonstrated the effectiveness of fuzzy logic in enhancing diagnostic processes. Ahmadi et al. (2018) conducted a systematic review that highlighted the significance of fuzzy logic in reducing diagnostic ambiguity and uncertainty, offering insights into its impact on various diseases and identifying under-researched areas. Uzoka et al. (2011) compared fuzzy logic with the fuzzy-analytic hierarchy process (fuzzy-AHP), finding that fuzzy logic slightly outperformed AHP in correlating with conventional diagnoses. Alam et al. (2022) explored the integration of fuzzy inference systems (FIS) with the Internet of Things (IoT) for smart disease diagnosis, showing improvements in accuracy and efficiency for diseases like COVID-19 and malaria. Sudha and Girijamma (2017) introduced an enhanced fuzzy logic algorithm for clustering complex medical data, outperforming traditional methods in response time and classification accuracy. Thomas, Goni, and Emeje (2019) reviewed various fuzzy models, emphasizing their ability to manage nonlinear relationships and uncertainties in medical diagnosis. Shetty (2018) proposed a methodology for predicting heart disease using fuzzy logic, incorporating a web app prototype for wider accessibility. Collectively, these studies highlight fuzzy logic’s potential to improve diagnostic accuracy and efficiency by effectively managing uncertainties and integrating diverse data sources. As technology advances, fuzzy logic is expected to play an increasingly crucial role in enhancing medical diagnostics and patient care.

Short-Term Forecasting of Electricity Consumption Using Fuzzy Logic

The high demand for electricity in the production process at PT Semen Padang requires a system that can cope with various kinds of disturbances. The problem is that the need for electrical loads is dynamic, especially in the short term, allowing fluctuations between electrical loads at uncertain times. A short-term electric energy consumption forecasting method is needed to determine load growth and distributed power supply. This research aims to use a fuzzy logic algorithm to perform short-term electrical energy consumption forecasting and compare the forecasting results with the actual load at PT Semen Padang. The results showed that short-term load forecasting for seven days using the fuzzy Mamdani method, namely Smallest of Maximum (SoM), obtained a percentage MAPE value of 8.15%. Meanwhile, the Weight of Average (WoA) Sugeno defuzzification method gets a portion of the MAPE value of 9.51%. The Sugeno method is more accurate than the Mamdani method in short-term electricity load forecasting for PPI Indarung V PT Semen Padang. If based on the time category, then forecasting the electricity load on holidays is better than predicting on weekdays. However, when viewed in terms of per day, in Wednesday has the smallest average MAPE value of 5.05%.

SOFT COMPUTING IN PREVENTING RANSOMWARE RELYING ON LARGER-SCALE DATA AND ANALYSIS

Ransomware attacks continue to pose a significant threat to organizations and individuals worldwide. The attackers’ ability to constantly evolve and adapt their tactics challenges traditional cybersecurity approaches to keep pace. Ransomware attacks targeting the healthcare industry accounted for 45% of all reported cyberattacks. The nature and scale of attacks and the increasing healthcare technology adoption will continue to pose ransomware attack risks. However, by collecting and analyzing large volumes of data and applying soft computing techniques, cybersecurity experts can improve their ability to detect and prevent ransomware attacks. As a result, soft computing offers options for detecting and preventing malware attacks. Using methods from the field of soft computing, such as fuzzy logic, neural networks, and genetic algorithms, makes it possible to conduct a thorough analysis of large data sets. These can yield insightful information that can help recognize and react to ransomware attacks. These techniques can also help to decrypt files that have been encrypted using ransomware.

An Energy-Efficient Routing Algorithm for WSNs Using Fuzzy Logic.

Battery replacement or recharging is essential for sensor nodes because they are typically powered by batteries in wireless sensor network (WSN) applications. Therefore, creating an energy-efficient data transfer technique is required. The base station (BS) receives data from one sensor node and routes the data to another sensor node. As a result, an energy-efficient routing algorithm using fuzzy logic (EERF) represents a novel approach that is suggested in this study. One of the reasoning techniques utilized in scenarios where there is a lot of ambiguity is fuzzy logic. The remaining energy, the distance between the sensor node and the base station, and the total number of connected sensor nodes are all inputs given to the fuzzy system of the proposed EERF algorithm. The proposed EERF is contrasted with the current systems, like the energy-aware unequal clustering using fuzzy logic (EAUCF) and distributed unequal clustering using fuzzy logic (DUCF) algorithms, in terms of evaluation criteria, including energy consumption, the number of active sensor nodes for each round in the network, and network stability. EAUCF and DUCF were outperformed by EERF.

Trajectory control of a hydraulic system using intelligent control approach based on adaptive prediction model

Electro-hydraulic actuators (EHAs) have become a preferred alternative to traditional hydraulic actuators with valve control systems due to their numerous advantages, making them an ideal choice for applications requiring high-precision force or position control. However, the highly complex nonlinear nature of EHAs makes modelling and controlling them a challenging task. To address this challenge, a new position control approach has been proposed for an EHA system using a combination of a feedforward online-tuning PID (FOPID) controller and an adaptive grey predictor (AGP), known as the feedforward online-tuning adaptive grey predictor (FOAGP). The FOPID controller is constructed based on PID controller and fuzzy logic algorithm to control the EHA system towards referred trajectory, while an updating rule that consists of robust checking terms optimizes its parameters online to minimize control error. The AGP predictor is an important aspect of the proposed approach. It consists of a self-tuning step size mechanism, which estimates the performance of the plant to tune the parameters of the controller and create an additive control signal that is used to counteract environment noises and perturbations. This approach significantly improves control performance by reducing the effect of disturbances and sensor noises on the system. The FOAGP approach was tested in simulation to investigate its effectiveness. The results showed that the proposed approach outperformed other existing control methods, with a higher accuracy and better control performance. One of the significant advantages of the FOAGP approach is its ability to learn and adapt to changing system dynamics. The learning mechanism used in the FOPID controller allows the system to optimize its parameters online, which is especially useful in systems with varying operating conditions. The AGP predictor also continuously adjusts its parameters to accurately estimate the system output, making it an effective tool for controlling EHAs. The proposed approach offers a significant improvement in control performance, making it a better alternative to traditional control methods. This approach can be applied to various EHA systems, including those used in aerospace, automobile, and robotic applications, among others.

Contingency-resilient PMU placement using Fuzzy Logic and Discrete Artificial Bee Colony algorithm for comprehensive network observability

In transforming traditional grid systems into intelligent and microgrid-based systems, the Phasor Measurement Units (PMUs) play a prominent role in enhancing monitoring, assessment, control, and protection by estimating voltage and current phasors correctly and quickly. However, since PMUs are not cost-effective, the decision to take the requisite number and their placement is a significant issue and needs to focus on a better approach. This study addresses the solution methodology to compute the optimal locations of a reduced PMU number to have complete network observability. A new hybrid optimization strategy based on the Fuzzy Logic (FL) and Discrete Artificial Bee Colony (DABC) algorithm has been suggested. The suggested method is titled FL-DABC algorithm, which has been applied to determining the optimal placement of PMUs in microgrid-based systems. The suggested approach tackles the problem of fully network-observable cost-effective PMU deployment. The effectiveness of the Algorithm is evaluated through simulations under various operating limitations, such as zero injection buses, a single PMU, and line outage scenarios. A topological observability analysis validates full network observability with the computed PMU placement. The study also includes branch PMU placement set computation and a comparative analysis with other techniques using standard IEEE test networks for simulations.

Application of fuzzy logic algorithms for predicting the pipe-culvert technical condition on railways

This article is part of the author’s dissertation research in the field of developing a methodology for predicting the pipe-culvert technical condition on railways using the mathematical apparatus of fuzzy logic. One of the common methods for assessing engineering structures is the point rating method. During the examination, each identified defect is assigned a certain number of points. Then the number of points is summed up according to the developed methodology and, based on the obtained result, a conclusion is drawn about the condition of the object. However, in conditions of significant subjectivity and limited input data, the issue of predicting the structure technical condition is quite labor-intensive. As an addition to existing methods for predicting the structure technical condition, in particular pipe-culvert, it is proposed to apply a method based on the fuzzy logic principles, which is a generalization of fuzzy logic and set theory. In fuzzy logic, unlike classical logic, instead of the values true and false, the truth value degree is used, which takes any value from an infinite set from 0 to 1 inclusive. To predict changes in condition and determine the deterioration rate of the technical characteristics of a pipe-culvert, it is necessary to create a model in which scoring methods are transformed into estimates presented in terms of fuzzy logic. The article presents a methodology for using fuzzy logic algorithms to predict the pipe-culvert technical condition on railways. As a result of the research, numerical gradations of the term «pipe-сulvert Age» were obtained. With limited and subjective input data, expressed numerically in terms of fuzzy logic — «pipe-culvert Age» and «pipe-culvert Condition», it is possible to calculate the values of the term «pipe-culvert Degradation Rate». The author presents a method for predicting the technical condition of railway pipe-culverts, based on the principles of fuzzy logic, founded on fundamental set theory. The developed mathematical model makes it possible to calculate the structure transition time from one technical state to another. To implement fuzzy logical inference of the presented model, the Fuzzy Logic Toolbox package is used in the MATLAB software environment.

Development of a real-time Printed Circuit board (PCB) visual inspection system using You Only Look Once (YOLO) and fuzzy logic algorithms

Automated visual inspection on PCB boards is a critical process in electronic industries. Misalignment component detection is one of the challenging tasks in the PCB inspection process. Defects during the production process might include missing and misaligned components as well as poor solder connections. Inspection of PCB is therefore required to create practically defect-free products. There are various methods have been developed to perform this task in literature. The significance of this research is to propose an efficient with low-cost system is still require in small scale manufacturing to perform the misalignment or missing component detection on PCB boards. However, an efficient, low-cost system is still required in small-scale manufacturing to perform the misalignment or missing component detection on PCB boards. In this study, a real-time visual inspection system is developed for misalignment component detection. The proposed system consists of hardware and software frameworks. The hardware framework involves the setup of devices and modules. The software framework is composed of pre-processing and post-processing. In pre-processing, image enhancement is applied to remove noises from captured images and You Only Look Once (YOLO) object detector for components detection. Subsequently, the detected components are compared to the corresponding defined pattern using a template-matching algorithm. As experimental shown, the proposed system satisfies the requirement of missing component detection on PCB boards.

Enhanced Power Quality and Forecasting for PV-Wind Microgrid Using Proactive Shunt Power Filter and Neural Network-Based Time Series Forecasting

This research proposes a proactive Shunt power filter to enhance the energy quality of a microgrid’s distribution. The study aims to identify a suitable controller technique for increasing the compensating capacity of the active filter in a PV-Wind hybrid energy production system, enabling voltage management and stabilization in direct current energy conversion facilities. The proposed control system utilizes a modified fuzzy logic algorithm for an enhanced performance active shunt energy filter. Simulations in the SIMULINK and MATLAB model environment validate the effectiveness of the methods. Additionally, a neural network model with a scrolling time frame is proposed to address the nonlinearity and time-varying nature of electricity generation from PV modules. The adaptable forecasting model using the neural network outperforms fixed prediction models in accuracy and computational efficiency. This approach has the potential to significantly improve power quality and forecasting accuracy for real-world PV power production.

Fuzzy Logic Algorithm of Sugeno Method for Controlling Line Follower Mobile Robot

The industrial world has been increasingly using robots for production purposes. One type of robot used is a line follower robot for the purposes of transportation of production materials. Various researches and competitions of line follower robots were held to improve its performance. This study proposed a fuzzy logic algorithm using Sugeno method for a line follower mobile robot. This algorithm received input from the readings of 8 sensors mounted on the bottom of the robot and generated the speed of each left and right motor. This speed was used to keep the robot on track. The performance of this algorithm was compared with the fuzzy logic algorithm of the Mamdani method. The proposed fuzzy logic algorithm was better in terms of speed. The results of this study can be used as material to study the application of fuzzy logic algorithm in real time.

Empowering Financial Management in Educational Institutions: A Multi-Objective Decision-Making System using Intelligent Fuzzy Logic Algorithm and Digital Marketing

Empowering Financial Management in Educational Institutions: A Multi-Objective Decision-Making System using Intelligent Fuzzy Logic Algorithm and Digital Marketing