Set Of Fuzzy Rules Research Articles

Design and Simulation of a Physician-Based Fuzzy System for Ventilator Adjustments in ARDS Patients to Ensure Lung Protection

The acute respiratory distress syndrome patients largely need a mechanical ventilator intervention. There are procedures that have been developed to guide the physicians during the ventilation of the patient. Berlin definition of the acute respiratory distress syndrome has been developed with ventilator adjustment settings/procedures. The procedures may however be a challenge for some physicians to remember during the intense ventilator intervention. Physicians are found to make human errors that may lead to the death of the patient. This, therefore, calls for the need of a logic system that will reason for the physician, that is, guide the physician. A fuzzy logic system was used to build the fuzzy set rules based on the Berlin definition. The MATLAB Simulink was used to simulate the system. The results show that the fuzzy-based ARDS Berlin definition can guide the physician on the adjustments to be made during the ventilation.

Adaptive Fuzzy Predictive Approach in Control

This article studies the approach to solving the problem of controlling the complex organizational and technical systems based on hybrid models. We propose a new component of intelligent decision support that is integrated with control systems. The proposed component is based on fuzzy logic and knowledge engineering. We present a model of ontology to form the context of data analysis and time series modeling. The ontological context allows us to represent trends of the analyzed object indicators. An expert can add a set of fuzzy rules to the ontology for systems control based on the fuzzy inference. The proposed approach allows reducing the time of analysis and interpretation of the results. Experimental results confirm the correctness and effectiveness of the approach proposed in this article.

An Interval Type-2 Fuzzy Logic-Based Map Matching Algorithm for Airport Ground Movements

Airports and their related operations have become the major bottlenecks to the entire air traffic management system, raising predictability, safety, and environmental concerns. One of the underpinning techniques for digital and sustainable air transport is airport ground movement optimization. Currently, real ground movement data is made freely available for the majority of aircraft at many airports. However, the recorded data is not accurate enough due to measurement errors and general uncertainties. In this article, we aim to develop a new interval type-2 fuzzy logic-based map matching algorithm, which can match each raw data point to the correct airport segment. To this aim, we first specifically design a set of interval type-2 Sugeno fuzzy rules and their associated rule weights, as well as the model output, based on preliminary experiments and sensitivity tests. Then, the fuzzy membership functions are fine-tuned by a particle swarm optimization algorithm. Moreover, an extra checking step using the available data is further integrated to improve map matching accuracy. Using the real-world aircraft movement data at Hong Kong airport, we compared the developed algorithm with other well known map matching algorithms. Experimental results show that the designed interval type-2 fuzzy rules have the potential to handle map matching uncertainties, and the extra checking step can effectively improve map matching accuracy. The proposed algorithm is demonstrated to be robust and achieve the best map matching accuracy of over 96% without compromising the run time.

Methods for optimizing data processing based on fuzzy adjustment of time series elements and identification model variables

The problem of optimal identification and processing of random time series (SVR) based on the properties of statistical, dynamic, and fuzzy models is formulated. A method for qualitative identification of SVR is proposed, which includes algorithms for fuzzy equations, logical inferences, taking into account the effects of environmental factors and non-stationary processes. A generalized algorithm for identifying SVR with adjustment and correction of variable values based on the rules of fuzzy logic, methods for searching for extrema by t -norms and s -norms is developed. Tools are designed for optimal data processing by determining an adequate model; parametric and structural identification of objects; search optimization; model training; identification of the “input and output” relationship; formation and use of a knowledge base, as well as sets of fuzzy rules, linguistic variables, membership functions, and algorithms for regulating variable values. Methods of fuzzy correction of distorted information by controlling the error of SVR identification are developed, and a software package is implemented that provides high accuracy of data processing with significantly lower costs.

Offset-free distributed predictive control based on fuzzy logic: Application to a real four-tank plant

This paper proposes an offset-free distributed implementation of a model predictive controller that employs fuzzy negotiation between agents. The scheme is based on model augmentation with additional disturbances to enable zero-offset tracking. Moreover, we code the negotiation criteria as a set of suitable fuzzy rules and consider stability and feasibility guarantees in the controller design for the linearized subsystems. We applied the method to an experimental four-tank plant, showing its effectiveness despite the coupling between subsystems and system-model mismatch.

The advanced defuzzification methods of the convex α – cut fuzzy sets

The main features of the fuzzy sets and their corresponding membership functions were presented in terms of the fuzzification process and further by the de-fuzzification operation. The convexity of the α -(alfa) cuts of the fuzzy sets is used in the decomposition of the fuzzy sets. The alfa cuts of the fuzzy sets were defined precisely in terms of the pair of functions and their lowest upper and greatest lower bounds. The convex combination of the intervals of the sub-regions of the fuzzy sets and their membership function were considered as the points of the defuzzified values of the fuzzy sets. The methods of the de-fuzzification to the crisp sets were presented by the formulas to find the defuzzification regions and de-fuzzified values. The compositional concepts of the inference as the expansion of the extension principle were introduced to formalize further the fuzzy reasoning by the set of fuzzy rules based on the approximate reasoning.

ОПРЕДЕЛЕНИЕ НЕЧЕТКИХ УСЛОВИЙ И АНАЛИЗ СУЩЕСТВУЮЩИХ РЕШЕНИЙ В ЗАДАЧЕ ЭВАКУАЦИИ ПРИ ЧРЕЗВЫЧАЙНЫХ СИТУАЦИЯХ

Quantification in collective behavior and decision-making in fuzzy conditions is crucial toensure the health and safety of the population. The task of modeling and predicting behavior infuzzy conditions, as is known, has increased complexity due to a large number of factors fromwhich an NP-complete multi-criteria problem is formed. There is a difficulty in quantifying the impact of fuzzy factors using a mathematical model. In this regard, the paper proposes a stochasticmodel of human decision-making to describe the empirical behavior of subjects in an experimentsimulating an emergency scenario. The developed fuzzy model combines fuzzy logic into aconventional model of social behavior. Unlike existing models and applications, this approachuses fuzzy sets and membership functions to describe the evacuation process in an emergencysituation. The purpose of this work is to define fuzzy rules and analyze existing solutions. The scientificnovelty lies in the formation of a set of factors that form fuzzy rules for making dynamicdecisions. The problem statement in this paper is as follows: to form a set of factors affecting thebehavior of pedestrians, which are modeled as fuzzy input data. The practical value of the worklies in the creation of a new set of fuzzy rules that allows them to be used in the evacuation algorithmfor the effective solution of the task. The fundamental difference from the known approachesis in the application of a new set of fuzzy rules, which contains factors: perception, intention, attitude.To implement the proposed model, the process of social behavior during evacuation, independentvariables are determined. These variables include measurements related to social factors,in other words, the behavior of individual subjects and individual small groups, which are fundamentalat an early stage of evacuation.

Modeling and analysis of field-oriented control based permanent magnet synchronous motor drive system using fuzzy logic controller with speed response improvement

<span lang="EN-US">The permanent magnet synchronous motor (PMSM) acts as an electrical motor mainly used in many diverse applications. The controlling of the PMSM drive is necessary due to frequent usage in various systems. The conventional proportional-integral-derivative (PID) controller’s drawbacks are overcome with fuzzy logic controller (FLC) and adopted in the PMSM drive system. In this manuscript, an efficient field-oriented control (FOC) based PMSM drive system using a fuzzy logic controller (FLC) is modeled to improve the speed and torque response of the PMSM. The PMSM drive system is modeled using abc to </span><em><span lang="EN-US">αβ</span></em><span lang="EN-US"> and </span><em><span lang="EN-US">αβ</span></em><span lang="EN-US"> to abc transformation, 2-level space vector pulse width modulation (SVPWM), AC to DC rectifier with an inverter, followed by PMSM drive, <a name="_Hlk107566912"></a>proportional integral (PI) controller along with FLC. The FLC’s improved fuzzy rule set is adopted to provide faster speed response, less % overshoot time, and minimal steady-state error of the PMSM drive system. The simulation results of speed response, torque response, speed error, and phase currents are analyzed. The FLC-based PMSM drive is compared with the conventional PID-based PMSM drive system with better improvements in performance metrics.</span>

Uncertain frequency responses of CNT – reinforced polymeric graded structure using fuzzified elastic properties – fuzzy finite element approach

A novel fuzzy finite element computational model is prepared to predict the frequency responses of the functionally graded carbon nanotube (FG-CNT) reinforced composite panel structure. The nanotube distributions in the matrix have an affinity towards the agglomeration and affect final properties. The model incurred the fuzzification of elastic properties (E 11, E 22, G 12, and υ 12) considering a ±20% as the degree of uncertainty via Mamdani’s fuzzy inference system (FIS). The extended rule of the mixture has been adopted to count the CNT properties for different grading patterns to achieve the realistic one in the framework of the higher-order polynomial model. Further, a set of fuzzy rules have been adopted to count the uncertainty in elastic properties with the help of the design of the experiment (Taguchi-L25 orthogonal array). The equation of motion is derived for the CNT-reinforced composite in association with Hamilton’s principle for the computation of natural frequency. An in-house computer code is derived in MATLAB with the help of a higher-order fuzzified finite element model (FFEM) for the computation. The suitability of the proposed model has been tested by solving a series of numerical examples and the output is discussed in detail.

Continuous Dynamic Update of Fuzzy Random Forests

Fuzzy random forests are well-known machine learning classification mechanisms based on a collection of fuzzy decision trees. An advantage of using fuzzy rules is the possibility to manage uncertainty and to work with linguistic scales. Fuzzy random forests achieve a good classification performance in many problems, but their quality decreases when they face a classification problem with imbalanced data between classes. In some applications, e.g., in medical diagnosis, the classifier is used continuously to classify new instances. In that case, it is possible to collect new examples during the use of the classifier, which can later be taken into account to improve the set of fuzzy rules. In this work, we propose a new iterative method to update the set of trees in the fuzzy random forest by considering trees generated from small sets of new examples. Experiments have been done with a dataset of diabetic patients to predict the risk of developing diabetic retinopathy, and with a dataset about occupancy of an office room. With the proposed method, it has been possible to improve the results obtained when using only standard fuzzy random forests.

APPLICATION FUZZY MAMDANI TO DETERMINE THE RIPENESS LEVEL OF CRYSTAL GUAVA FRUIT

Crystal guava is one of Indonesia's flora diversity. The rind of the unripe crystal guava fruit is green, and the rind of the ripe crystal guava fruit is yellowish green. However, it is difficult to determine the ripeness of crystal guava due to the similar color of the fruit skin. Determining fruit ripeness is uncertain and therefore requires a way to deal with this uncertainty. One of the methods you can use is fuzzy Mamdani. In this study, the ripeness level of crystal guava is determined using fuzzy Mamdani. Crystal guava fruits fall into four ripeness categories: raw, half ripe, ripe, and very ripe. The data used is in the form of an RGB image separated from the background so that only crystal guava fruit objects are captured. The image of the fruit object was then extracted by looking for the median red, green, and blue at each pixel of the image. This value is used as input for the fuzzy Mamdani process. The fuzzy set and fuzzy rules that have been formed can be applied to determine the maturity level of crystal guava fruit by validating the results of 140 image data with an accuracy of 83,5%.

Adaptive Sliding Mode Controller for Robotic Manipulator Tracking Control with Fuzzy Design

This paper introduces an adaptive sliding mode controller design based on fuzzy compensation for efficient robotic manipulator tracking control. This work introduces design of Adaptive Fuzzy Controller based on sliding control principles for Robotic Manipulators. In the work, an adaptive fuzzy sliding mode control algorithm is proposed for tracking control of robot manipulators. The fuzzy system uses a set of fuzzy rules, the parameters of which are modified in real-time by adaptive laws, to approximate unknown nonlinearities. This makes it easier to direct the nonlinear system's output to follow a specific trajectory. An adaptive control algorithm based on the adaptive fuzzy model is created using the Lyapunov approach. Both the chattering and the stable performance are assured.

A fuzzy logic technique for virtual sensor networks

A virtual sensor network is designed to infer an approximation of real data when the physical sensor network cannot provide it, due to technical or economic reasons. In this paper, we propose an approach to model virtual sensor networks based on fuzzy logic. It relies on the hypothesis that the readings of the physical sensors are correlated. In particular, the virtual sensor network is able to learn the relationships between the physical quantities detected by the sensors and model them through fuzzy rules, which provide an approximation of the real values. In the proposed model, a set of fuzzy rules is derived for each physical sensor. It will provide an accurate approximation of the sensor readings when needed (e.g. sensor malfunction). The proposed approach has been designed as an extension of the well known Tree Routing protocol, used in sensor networks for transmitting sensed data to the base station. It has been implemented in Snlog, a language derived from Datalog that supports the development of distributed algorithms for Wireless Sensor Networks in a declarative way. The experimental evaluation reported in the paper shows the effectiveness and efficacy of our solutions.

A Concise TSK Fuzzy Ensemble Classifier Integrating Dropout and Bagging for High-Dimensional Problems

Improving the tradeoff between accuracy and interpretability is essential for the problem of handling high-dimensional data in Takagi–Sugeno–Kang (TSK) fuzzy systems and providing insights into real-world tasks. However, the TSK fuzzy system becomes complex and challenging to interpret as the data dimension increases. Here, we report an ensemble classifier, which is an enhanced adaptive network-based fuzzy inference system (ANFIS) integrating improved bagging and dropout to build concise fuzzy rule sets. First, the high-dimensional feature space is decomposed into a series of low-dimensional feature subsets using the bagging and random subspace method to train multiple ANFISs. An improved dropout strategy is then applied in training ANFISs by temporarily disabling rules in each training epoch and deleting rules after training to obtain sparse rulesets with high-quality rules. These sub-models are subsequently aggregated to perform the fuzzy inference. Results on high-dimensional benchmark datasets confirm that both the bagging and dropout strategies are effective, providing high interpretability by reducing the co-firing degrees and rules of sub-models while guaranteeing accuracy at the same time.

An overview of Intrusion Detection within an Information System: The Improvment by Process Mining

Information Systems handle big amount of data within enterprises by offering the possibility to collect, treat, keep and make information avail- able. To realize these tasks, it is important to secure data from intrusions that can affect confidentiality, availability and integrity of information. Un- fortunately, with the time, technologies are more used and various types of attacks act on it to create intrusion or misuses within Information Systems. Research in intrusion detection field is still looking for solutions of such relevant problems. The purpose of this paper is to present an overview of existing intrusion detection techniques compared to a new issue based on process mining used for event logs analysis to detect abnormal events that occurs on the system. events are classified accordingly to security policy etablished with fuzzy logic to build a set of fuzzy rules, for the definition of normal and abnormal events and then reduce the high level of false alerts.

Predicting Pavement Condition Index Using Fuzzy Logic Technique

The fuzzy logic technique is one of the effective approaches for evaluating flexible and rigid pavement distress. The process of classifying pavement distress is usually performed by visual inspection of the pavement surface or using data collected by automated distress measurement equipment. Fuzzy mathematics provides a convenient tool for incorporating subjective analysis, uncertainty in pavement condition index, and maintenance-needs assessment, and can greatly improve consistency and reduce subjectivity in this process. This paper aims to develop a fuzzy logic-based system of pavement condition index and maintenance-needs evaluation for a pavement road network by utilizing pavement distress data from the U.S. and Canada. Considering rutting, fatigue cracking, block cracking, longitudinal cracking, transverse cracking, potholes, patching, bleeding, and raveling as input variables, the variables were fuzzified into fuzzy subsets. The fuzzy subsets of the variables were considered to have triangular membership functions. The relationships between nine pavement distress parameters and PCI were represented by a set of fuzzy rules. The fuzzy rules relating input variables to the output variable of sediment discharge were laid out in the IF–THEN format. The commonly used weighted average method was employed for the defuzzification procedure. The coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used as the performance indicator metrics to evaluate the performance of analytical models.

Smart interpretable model (SIM) enabling subject matter experts in rule generation

Current Artificial Intelligence (AI) technologies are widely regarded as black boxes, whose internal structures are not inherently transparent, even though they provide powerful prediction capabilities. Having a transparent model that enables users to understand its inner workings allows them to appreciate the learning and inference process, leading to trust and higher confidence in the model. While methods that help with interpretability have been created, most of them require the user to have a certain level of AI knowledge and do not allow a user to fine-tune them based on prior knowledge. In this paper, we present a smart interpretable model (SIM) framework that requires little to no AI knowledge and can be used to create a set of fuzzy IF-THEN rules along with its corresponding membership functions at ease. The framework also allows users to incorporate prior knowledge during various steps in the framework and generates comprehensive insights summarized from rules and samples, allowing users to identify anomalous rules, feature contributions of each sample and confidence level for each rule. We demonstrate these capabilities and compare our model to other existing rule-based models using various datasets that have been used for rule-based model validations. Validations are then done in terms of performance and whether the rules that are generated by SIM are similar to the rules generated by other more recent rule-based models.

Fuzzy multi-agent assistance system for elderly care based on user engagement

An intelligent system intended to provide assistance to an elderly user in their home is presented. This multi-agent system is designed to monitor the interaction between the user and other agents, and regulate itself based on the estimated level of mental engagement of the user in order to contribute to help them maintain adequate levels of awareness and interaction with their home environment. The agents are devices programmed to preserve the security, comfort and health of the user, who is also conceptualized as one of the agents in the system. The user engagement level is estimated through fuzzy inference, that is, by means of a set of fuzzy membership functions and rules applied over the interaction of the user with different categories of agents. The system incorporates several dozen agents and is tested using two different datasets of real interactions between users and devices in their home environments. A uniform parameter configuration yielded very positive results for diverse scenarios and subsequently the parameters of the system were automatically optimized to show that improved performance, in the form of a more efficient system regulation and increased certainty in adjustment estimation, is obtained.

Detecting Phishing Cyber Attack Based on Fuzzy Rules and Differential Evaluation

Cyber-criminal attacks witnessed various phishing attacks that blindly attract users to use their secret personal information. This research highlights this type of attack and proposes the development of a new model to detect phishing websites. Two aspects of improvement were followed. Firstly, a new feature is inferred from a combination of features with higher classification capability than the original features. Secondly, a novel method has been proposed by using differential evaluation (DE) to select the best rules among the fuzzy rule set; the DE is a viable way to optimize the fuzzy rule, improving the classification complexity. The proposed system was evaluated by applying two malicious and legitimate datasets collected from Phishtank and Alexa websites. Results show the combination feature can include a promising method for detecting phishing attacks according to the evaluation metrics in the precision of 0.973, recall of 0.933, F-measure of 0.947, and accuracy of 0.976.

Interval Type-3 Fuzzy Aggregation of Neural Networks for Multiple Time Series Prediction: The Case of Financial Forecasting

In this work, we present an approach for fuzzy aggregation of neural networks for forecasting. The interval type-3 aggregator is used to combine the outputs of the networks to improve the quality of the prediction. This is carried out in such a way that the final output is better than the outputs of the individual modules. In our approach, a fuzzy system is used to estimate the prediction increments that will be assigned to the output in the process of combining them with a set of fuzzy rules. The uncertainty in the process of aggregation is modeled with an interval type-3 fuzzy system, which, in theory, can outperform type-2 and type-1 fuzzy systems. Publicly available data sets of COVID-19 cases and the Dow Jones index were utilized to test the proposed approach, as it has been stated that a pandemic wave can have an effect on the economies of countries. The simulation results show that the COVID-19 data does have, in fact, an influence on the Dow Jones time series and its use in the proposed model improves the forecast of the Dow Jones future values.