Fuzzy Decision System Research Articles

Improving semi-active vibration control of an 11-story structure with non-linear behavior and floating fuzzy logic algorithm

In recent years, vibration control of structures subjected to the wind, earthquakes and traffic loads has been broadly studied. Based on this, different types of dampers and control systems have been used and evaluated in the form of active, semi-active, passive, and hybrid control. In this study, to control the vibrations of an 11-story structure under the effect of seismic load, the semi-active control approach of structural vibrations is used, with the help of Magneto-Rheological, MR, damper and using fuzzy logic algorithm decision system. In addition to using fuzzy inference system to determine the appropriate control voltage of the MR damper, the floating fuzzy inference system is also used to improve the performance of control system. In other words, to improve the performance of the fuzzy system with fixed membership functions, floating fuzzy system has been used with variable range for membership functions. The proposed fuzzy system is implemented in two different decision states; first, making decision based on velocity and second, making decision based on the velocity and displacement. Implementation of fuzzy inference system is conducted in MATLAB, and linear and nonlinear structural model in OpenSEES, where TCP/IP connection between them is used. The results of incremental dynamic analysis with maximum acceleration of 0.1 g to 1.0 g with incremental step of 0.1 g indicate that using floating fuzzy control system for decision based on velocity reduces the average displacement of the roof about 17.6% in linear structure and 22% in nonlinear structure, while in decision making based on velocity and displacement, these values are about 1.3% and 1.34% in linear and nonlinear structures, respectively.

Dynamic maintenance of variable precision fuzzy neighborhood three-way regions in interval-valued fuzzy decision system

Dynamic maintenance of variable precision fuzzy neighborhood three-way regions in interval-valued fuzzy decision system

Construction and Application of Enterprise Internal Audit Data Analysis Model Based on Decision Tree Algorithm

With the systematization, informatization, and intelligence of the management and operation of communication enterprises, the amount of audit content and business data is increasing. The traditional audit work is mainly offline audit, the task is heavy and temporary, there are few auditors, and there is a lack of unified data mart and analysis model support. In order to solve many problems in traditional auditing, this paper realizes the interface of data dictionary attributes through effective domain management of multisystem data, so as to achieve the purpose of system support and effective management to improve data quality. Furthermore, the platform is extensible, which can gradually expand the data coverage and support more management fields and business systems. A method of obtaining continuous attribute clustering center points by K‐means algorithm and using triangular fuzzy numbers to fuzzify process continuous data is proposed. At the same time, a visual fuzzy decision‐making system based on Fuzzy ID3 and Min‐Ambiguity algorithm was designed. Adding the C4.5 and CART algorithms implemented in the Weka open source data mining software, through experimental analysis, the differences in the classification accuracy and the number of rules generated by the four decision tree algorithms are compared. The experiment found that the Fuzzy ID3 algorithm has a high accuracy rate on each dataset and the number of rules is large. The number of rules generated by the CART algorithm is the least because of the model characteristics of its binary tree and the characteristics of using the Gini index as the criterion for the selection of splitting attributes. Comparing Fuzzy ID3 and Min‐Ambiguity fuzzy decision tree algorithms, it is found that the overall performance of the former is better than that of the latter, and the effect of authenticity on the two algorithms is analyzed experimentally. This paper constructs the internal audit flowchart in the big data environment and studies the optimization of the internal audit process in the big data environment. We evaluate traditional internal audit processes using process optimization theory. The internal audit process in this paper refers to the internal audit process under the risk‐oriented model that is widely used at this stage. The optimization research on the internal audit process in this paper is mainly aimed at the internal audit process of general enterprises with a certain level of informatization, at a large scale, and certain foundation and requirements for the analysis and use of data. The optimization of internal audit process is feasible in research and practical application and has universal applicability and reference value.

Розробка методики вибору раціонального перевізника вантажів в міжнародному сполученні

One of the effective methods of modeling for decision-making that allows you to take into account most of the properties of the object research is fuzzy logic. The article considers the problem of making decisions in uncertain conditions based on applying the rules of fuzzy logic. Fuzzy set methods are especially useful in the absence of an accurate mathematical model of system operation. Fuzzy set theory makes it possible to apply inaccurate and subjective expertise to a subject area in decision-making without formalizing it in the form of traditional mathematical models. The most influential parameters for the choice of a carrier in an international connection have been determined with the help of expert assessments. The structure and functions of the fuzzy decision-making system regarding the choice of a rational carrier are described. The stages of fuzzy data transformation in the process of logical derivation of solutions are specified. The example of fuzzy inference of the dependence of the parameters of the choice of rational carrier of cargo is given. As a practical implementation of the proposed methodology, a model was created in the MATLAB software environment. According to the results, a rational carrier was determined to transport various goods in international traffic. With the help of MATLAB, graphs of the surface dependence of the carrier’s choice on various influencing factors such as reputation, CMR-insurance, cost of transportation, number of cases of damage and shortage of goods, delivery time were obtained. Implementation in practice shows that the use of research results can reduce the working time of staff and significantly improve the quality of their work in the development and selection of options for delivery of goods.

Evaluation of agronomic, technical, economic, and environmental issues by analytic hierarchy process for rice weeding machine

Seed is a living product that must be grown, harvested, and processed correctly in order to realize the yield potential of any rice variety. Choosing the suitable weeding machines for paddy fields is of great interest to rice farmers. This choice requires exact evaluation and identifying its criteria can be very crucial. A Fuzzy decision-making system can be designed for a weeder machine through developing fuzzy sets of criteria for selecting suitable weeder machine providing a good basis for comparison of different types of machines considering each rice farmer and paddy field. The objective of this study was to explain the evaluation criteria for rice weeding machine in paddy fields and present fuzzy sets of identified criteria of Iran. The methodology can be divided into three main parts: the examination of construct validity of instruments, prioritization, and the relationships of variables. Thus, used survey and analytical hierarchy process (AHP) methods. The tools included a researcher-made questionnaire using 5-point paired comparison statements. For the survey, the statistical population included all experts in education and research working in ministry of agriculture from which 310 peoples were selected. For AHP, 20 experts were selected by purposive sampling. The results of factor analysis showed that all criteria were strongly related. The results of AHP prioritized the criteria as agronomic, technical, economic and environmental. Considering the “agronomic” and “technical” criteria, the experts preferred engine two and three row weeder machines, while regarding “agronomic” and “environmental” criteria, hand weeder machines were preferred. Obviously, that income amount and rising costs make the user to give priority to hand machines, whereas under normal conditions, “agronomic” and “technical” criteria are considered.

Smart Homes as Enablers for Depression Pre-Diagnosis Using PHQ-9 on HMI through Fuzzy Logic Decision System.

Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders’ health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.

A novel similarity measure in intuitionistic fuzzy sets and its applications

Intuitionistic fuzzy set (IFS) is a classical branch of fuzzy set, which has advantage to deal with uncertain problems. In IFS, similarity measure is an important fundamental concept, it is used to measure consistency between different intuitionistic fuzzy sets (IFSs) and becomes a key parameter in fuzzy decision system. However, the previous methods of similarity measure do not take enough account the effect of hesitancy degree on membership degree and non-membership degree, so that produce counterintuitive results when measuring similarity. Hence, in this paper, a new similarity measure of IFS is presented. The effect of hesitancy degree on similarity measure is fully considered in the proposed method and some properties also haven been discussed to prove the reasonable of proposed method. Meanwhile, some numerical examples are analyzed to illustrate characteristics of proposed similarity measure in detail. Further, the experiments of target classification and clustering problem demonstrate effectiveness and superiority of proposed similarity measure in the environment of expert assessments and data set.

Fuzzy rough discrimination and label weighting for multi-label feature selection

Fuzzy rough set is a theoretical framework of fuzzy uncertainty management, and discernibility matrix offers a mathematical foundation for algorithm construction of feature learning. The approaches of fuzzy rough set and discernibility matrix have been successfully applied in single-label learning. However, few works have been done on investigating the foundation of fuzzy rough discernibility matrix on multi-label data. There will be two pivotal problems to be addressed when using fuzzy rough discernibility matrix for multi-label data analysis. One is how to extract sample-level and label-level correlations; and the other is how to utilize the discernibility matrix for algorithm construction. For this reason, in this paper the fuzzy rough discrimination matrix is introduced to deal with the problem of multi-label feature selection. First, the significance of labels in the label space is captured based on the label correlation. Labels with different significances contribute to different weights for measuring the similarity between samples. Hence, a sample similarity matrix in the label space is computed based on the label weighting strategy. Then, a framework of a fuzzy decision system is formalized, in which the discernibility matrix of fuzzy rough sets is introduced as a foundation to evaluate the sample discrimination ability of features. Under the discernibility matrix criterion, a multi-label learning algorithm is developed to select discriminative features from multi-label data. A series of experimental analysis verifies the effectiveness and efficiency of the proposed method.

Genetic algorithm optimized fuzzy decision system for efficient data transmission with deafness avoidance in multihop cognitive radio networks

Genetic algorithm optimized fuzzy decision system for efficient data transmission with deafness avoidance in multihop cognitive radio networks

An intelligent approach towards very short-term load forecasting

Abstract Very short term load forecasting (VSTLF) plays a pivotal role in helping the utility workers make proper decisions regarding generation scheduling, size of spinning reserve, and maintaining equilibrium between the power generated by the utility to fulfil the load demand. However, the development of an effective VSTLF model is challenging in gathering noisy real-time data and complicates features found in load demand variations from time to time. A hybrid approach for VSTLF using an incomplete fuzzy decision system (IFDS) combined with a genetic algorithm (GA) based feature selection technique for load forecasting in an hour ahead format is proposed in this research work. This proposed work aims to determine the load features and eliminate redundant features to form a less complex forecasting model. The proposed method considers the time of the day, temperature, humidity, and dew point as inputs and generates output as forecasted load. The input data and historical load data are collected from the Northern Regional Load Dispatch Centre (NRLDC) New Delhi for December 2009, January 2010 and February 2010. For validation of proposed method efficacy, it’s performance is further compared with other conventional AI techniques like ANN and ANFIS, which are integrated with genetic algorithm-based feature selection technique to boost their performance. These techniques’ accuracy is tested through their mean absolute percentage error (MAPE) and normalized root mean square error (nRMSE) value. Compared to other conventional AI techniques and other methods provided through previous studies, the proposed method is found to have acceptable accuracy for 1 h ahead of electrical load forecasting.

An SDR implementation of reliable spectrum sensing using fuzzy logic and genetic algorithm

This paper presents the design of a reliable spectrum sensing method using fuzzy logic and genetic algorithm. Fuzzy logic acts as a decision-making system. It is used to select an optimal sensing method based on the current wireless channel state, the required accuracy of sensing, and computational complexity. Three sensing algorithms named as energy detection, feature detection and cyclo-stationary detection are used. The fuzzy decision-making system analyses the present state of SNR, availability information about the primary user, application demand on delay and accuracy. It selects optimal spectrum sensing techniques based on that analysis result. A genetic algorithm is used to choose optimal parameter selection for the selected optimal algorithm to improve the reliability further. The entire scheme is implemented on the USRP RIO SDR hardware. Performance evaluation on experiments shows that the proposed system outperforms with the probability of detection 99.7%.

Development of an IoT-Based Solution Incorporating Biofeedback and Fuzzy Logic Control for Elbow Rehabilitation

The last few years have seen significant advances in neuromotor rehabilitation technologies, such as robotics and virtual reality. Rehabilitation robotics primarily focuses on devices, control strategies, scenarios and protocols aimed at recovering sensory, motor and cognitive impairments often experienced by stroke victims. Remote rehabilitation can be adopted to relieve stress in healthcare facilities by limiting the movement of patients to clinics, mainly in the current COVID-19 pandemic. In this context, we have developed a remote controlled intelligent robot for elbow rehabilitation. The proposed system offers real-time monitoring and ultimately provides an electronic health record (EHR). Rehabilitation is an area of medical practice that treats patients with pain. However, this pain can prevent a person from positively interacting with therapy. To cope with this matter, the proposed solution incorporates a cascading fuzzy decision system to estimate patient pain. Indeed, as a safety measure, when the pain exceeds a certain threshold, the robot must stop the action even if the desired angle has not yet been reached. A fusion of sensors incorporating an electromyography (EMG) signal, feedback from the current sensor and feedback from the position encoder provides the fuzzy controller with the data needed to estimate pain. This measured pain is fed back into the control loop and processed to generate safe robot actions. The main contribution was to integrate vision-based gesture control, a cascade fuzzy logic-based decision system and IoT (Internet of Things) to help therapists remotely take care of patients efficiently and reliably. Tests carried out on three different subjects showed encouraging results.

Constraint-based and Fuzzy Logic Student Modeling for Arabic Grammar

Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer) which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different questions that deal with the different concepts and have different difficulty levels. Constraint-based student modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain level the different grammar skills through the defined skill structures. The main contribution of this paper is the hierarchal representation of the system's basic grammar skills as domain knowledge. That representation is used as a mechanism for efficiently checking constraints to model the student knowledge and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number of trails the student takes for answering each question and fuzzy logic decision system are used to determine the student learning level for each lesson as a long-term model. The results of the evaluation showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its features and abilities.

A Fuzzy Decision-Making System for the Impact of Pesticides Applied in Agricultural Fields on Human Health

Farmers are widely applying chemical pesticides to the agricultural lands to kill weeds to reduce crop losses and to prevent diseases created by insects. By applying pesticides to the lands, typically have greater agricultural yield. As pesticides have toxic ingredients, they can create so many health problems to humans and will degrade the environment gradually. Since each pesticide is linked to some health hazards when the composition of the pesticides exceeds its limits, uncertainty arises in determining the human health hazards. Hence, fuzzy logic-based decision-making model is designed to diagnose the human health hazards. In the model, the linguistic terms are used to represent the association between pesticides and human health hazards with the aid of chemists and physicians. Fuzzy numbers are used to represent the values for the linguistic terms. Therefore, the intent of the paper is to analyze the human health hazards induced by applying different pesticides in the agricultural lands through the proposed fuzzy decision-making system.

A fuzzy multi-objective decision system for recoverable remanufacturing planning

This study develops a novel fuzzy multi-objective decision system (FMODS) that uses the Simplex method, Access database technology and the JAVA programming language, for recoverable product of remanufacturing planning. The system can be used to determine optimal purchase quantities and lot release sizes to make decision system for remanufacturing planning more effective, more convenient and smarter. The system has both single-objective linear programming (SOLP) and fuzzy multi-objective linear programming (FMOLP) functions to help managers in practical situations. The results in this investigation indicate that the proposed FMODS has a very favorable solution time in cases of large demand, and can significantly shorten the production planning time.

Fuzzy logic-based decision-making system design for safe forklift truck speed: cast cobblestone production application

This paper presents a model in which “fuzzy logic multi-criteria decision-making method” is suggested to determine real-time forklift speed to reduce occupational accidents caused by operators. The model developed in the study uses the variables: the weight and height of the load carried by the forklift, the number of products on its pallet, the places with high risk of accident, and the wet-dry condition of the ground. In order to evaluate the performance of the suggested model, a data set comprises 128 different conditions in cast cobblestone production. Determined forklift speeds were compared with the forklift speeds determined by fuzzy logic using statistically analyses. Results showed that fuzzy logic model has a high accuracy and low error. Fuzzy logic modeling has proved to be a good way to decide the real-time speed of the forklifts being used in production without compromising occupational safety. Friedman test and Wilcoxon test have been used to estimate the significance of fuzzy logic method. The fuzzy logic results showed that our method achieved better results compared to beginner operator.

Scalable Fuzzy Rough Set Reduct Computation Using Fuzzy Min–Max Neural Network Preprocessing

A fuzzy rough set (FRS) is a hybridization of rough sets and fuzzy sets and provides a framework for reduct (feature subset selection) computation for hybrid decision systems. However, the existing FRS-based feature selection approaches are intractable for large decision systems due to the space complexity of the FRS methodology. We propose a novel fuzzy min–max neural network (FMNN)-FRS reduct computation approach utilizing the FMNN to enhance the scalability of FRS approaches. The FMNN provides a single pass epoch learning of arriving at granules of objects in the form of fuzzy hyperboxes for multiple decision classes. In the proposed approach, the FMNN model is used to reconstruct the object-based decision system into a fuzzy hyperbox-based interval-valued decision system. Then, a novel way of constructing the fuzzy discernibility matrix (FDM) from the interval-valued decision system is introduced. A fuzzy rough approximate reduct computation algorithm is developed with the induced FDM. The FMNN-FRS approach reduces the space complexity of FRS reduct computation significantly and results in enhanced scalability. Comparative experimental analysis has been done with the existing FRS reduct approaches on benchmark hybrid decision systems and established the relevance of the FMNN-FRS approach. The FMNN-FRS approach obtained the exact reduct in most of the datasets in much lesser computational time than existing FRS approaches while preserving similar classification accuracy. The FMNN-FRS method achieved enhanced scalability to such large decision systems, at which it is not possible to obtain reduct by existing FRS approaches.

Fuzzy decision system with a single variable parameter used in the intelligent control of the processes

Based on the results obtained from the fuzzy systems presented in other published articles, a simplified fuzzy system was made, which was considered as the main factor the temperature difference. In this paper, another type of fuzzy system (single input and two outputs) has been developed and tested using the fuzzy toolbox in Matlab for viewing the resulting graphs and comparing them with the graphs resulting from the other types systems has come to the conclusion that the optimal solution is this fuzzy system with one input and two outputs.

CONSTRAINT-BASED AND FUZZY LOGIC STUDENT MODELING FOR ARABIC GRAMMAR

Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer) which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different questions that deal with the different concepts and have different difficulty levels. Constraint-based student modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain level the different grammar skills through the defined skill structures. The main contribution of this paper is the hierarchal representation of the system's basic grammar skills as domain knowledge. That representation is used as a mechanism for efficiently checking constraints to model the student knowledge and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number of trails the student takes for answering each question and fuzzy logic decision system are used to determine the student learning level for each lesson as a long-term model. The results of the evaluation showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its features and abilities.

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms.