Fuzzy Logic Decision Research Articles

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Applying fuzzy logic decision-making approach to analyse the students' self-management perspective in professional education

Professional education has become very much challenging as it is competitive, research-oriented and result-oriented. The students have to struggle and toil to cope with their academic schedule. As the day to day activities are hectic and tightly scheduled, the students have to balance their academic and personal life, for which emotional stability is very much essential. Stress is the main factor responsible for weakening the emotional balance of students. This paper has made an attempt to peruse and analyse the causes and effects of stress using an optimisation technique with fuzzy logic decision-making (FLDM). This paper has applied multi-criteria decision-making (MCDM) tool as demonstrated by Tseng and Klein (1992) for analysing the above scenario and has offered valid findings and valuable suggestions. The purpose of this paper is to understand and demonstrate the concepts of fuzzy logic decision-making (FLDM) approach. The methodology involves identifying the key parameters and developing the fuzzy sets for the same. From the developed fuzzy sets membership grades are calculated, tabulated and ranked as per fuzzy norms. The study is confined to the students of the engineering (UG all years of information technology and mechanical engineering). The study reveals the various stress causing factors and suggests some stress relieving methods.

Applying fuzzy logic decision-making approach to analyse the students' self-management perspective in professional education

Professional education has become very much challenging as it is competitive, research-oriented and result-oriented. The students have to struggle and toil to cope with their academic schedule. As the day to day activities are hectic and tightly scheduled, the students have to balance their academic and personal life, for which emotional stability is very much essential. Stress is the main factor responsible for weakening the emotional balance of students. This paper has made an attempt to peruse and analyse the causes and effects of stress using an optimisation technique with fuzzy logic decision-making (FLDM). This paper has applied multi-criteria decision-making (MCDM) tool as demonstrated by Tseng and Klein (1992) for analysing the above scenario and has offered valid findings and valuable suggestions. The purpose of this paper is to understand and demonstrate the concepts of fuzzy logic decision-making (FLDM) approach. The methodology involves identifying the key parameters and developing the fuzzy sets for the same. From the developed fuzzy sets membership grades are calculated, tabulated and ranked as per fuzzy norms. The study is confined to the students of the engineering (UG all years of information technology and mechanical engineering). The study reveals the various stress causing factors and suggests some stress relieving methods.

Placement of Fault Current Limiters in Power System through a Two-stage Optimization Approach

With the sustainable growth in the number of new power plants and scale of transmission systems, the probability of the current exceeding the short-circuit rating of circuit breakers increases. A fault current limiter (FCL), which can be used to reduce current surges, has attracted considerable attention from utilities. However, the appropriate placement of FCLs in a power system for obtaining the most effective cost-to-benefit solution is crucial. This paper proposes a two-stage placement approach, where Stage I combines the hierarchical fuzzy logic decision (HFLD) method and Hashing-integrated generic algorithm (HIGA). The HFLD method is used for sorting feasible solutions, and the HIGA determines an optimal FCL placement in the reduced search space. Particle swarm optimization is then employed in Stage II for optimizing the FCL parameters. To verify the effectiveness of the proposed approach in solving the optimal FCL placement problem, the method is verified using the IEEE 30-bus system and system of a manufacturing factory in Taiwan. The numerical results show that the proposed method achieves a favorable solution in a short time with fewer placements of FCLs.

Putting Urban Regeneration Projects in Order: An Example of a Decision Support System

There is an urgent need of renewal of metropolitan cities in Turkey which heavily consist of irregular, low quality building stock which have occurred in the last 60 years. However urban regeneration projects are large and expensive construction projects. Therefore, due to technical and economic barriers, it is not possible for regeneration authorities to start the projects for the whole city in the same time. Thus they carry out projects step by step, one after another. Projects are started from the most problematic and urgent ones to the least problematic ones. However, regeneration authorities face with problems when putting regeneration projects in order, i.e. determining which neighborhood should be regenerated earlier than the other one. In addition to that, they are having difficulties when explaining their decisions to the public. At this point, a computer-based Fuzzy Logic decision support system can help them reach more convenient decisions. Also, with this way, the decisions will have a scientific basis, therefore it can be easier to announce them to the public and avoid speculation. In this paper, an example of a decision support system based on Fuzzy Logic for putting urban regeneration project areas in order was presented.

Fuzzy Based Congestion Control for Backpressure Routing Algorithm in Wireless Sensor Networks

The main aim of this study is to detect congestion and provide efficient route recovery mechanism using back pressure technique. In this study, we propose a fuzzy based congestion control and backpressure routing technique in wireless sensor networks. In the Fuzzy based congestion control technique, Fuzzy logic decision model is used to estimate the congestion status of each node based on the parameters number of contenders, buffer occupancy percentage of parent nodes and traffic load. In cluster based backpressure routing, clusters are formed and cluster heads are elected based on the congestion status of the nodes. By simulation results, we show that the proposed algorithm reduces the overhead and increases the routing efficiency.

Predictive demand side management of a residential house under intermittent primary energy source conditions

This paper targets the process of optimizing the operation of a PV-battery backup system under intermittent grid electricity supply. A predictive scheduling layer is developed as a part of a complete load management process. The main objective of the study is to ensure a permanent power supply for a high energy consuming residential application. The control algorithm plans the activation of predictable loads 24h ahead through compromising between a decrease in the resulting discomfort levels and the conservation of a high autonomy of the system. The strength of the developed control lies in ensuring the complete coordination between all the components of the installation: the grid, PV panels, battery storage, and the load demand. No loss of power supply is allowed during the day and realistic and technical constraints are applied. The demand side management program is formulated as a multi-objective optimization algorithm solved using the Non-dominated Sorting Genetic Algorithm (NSGA-II) technique. A fuzzy logic decision maker is developed for an automatic trade-off process implementing the residents’ preferences. The simulation results show excellent performance and flexibility of the proposed algorithm. The benefits of the load management are proved to have a great impact on the backup installation sizing, which leads to notable reduction of its price.

Aplikasi Pengambilan Keputusan dengan Metode Tsukamoto pada Penentuan Tingkat Kepuasan pelanggan

Tsukamoto method is one method of fuzzy inference system on fuzzy logic for decision making. Steps of the decision making in this method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules, fuzzy logic analysis, defuzzyfication (affirmation), as well as the conclusion and interpretation of the results. The results from this research are steps of the decision making in Tsukamoto method, namely fuzzyfication (process changing the input into kabur), the establishment of fuzzy rules by the general form IF a is A THEN B is B, fuzzy logic analysis to get alpha in every rule, defuzzyfication (affirmation) by weighted average method, as well as the conclusion and interpretation of the results. On customers at the case, in value of 16 the quality of services, the value of 17 the quality of goods, and value of 16 a price, a value of the results is 45,29063 and the level is low satisfaction

Design of an intelligent decision support system for global outsourcing decisions in the apparel industry

Global outsourcing can be defined as assigning a specific business component to a global supplier. While global outsourcing enhances the capability of the enterprise, it also facilitates flexible enterprise structures. Many factors need to be considered simultaneously for global outsourcing decisions, and purchasing managers need a structured method of using these criteria in their decision-making. The scope of this study was to develop a fuzzy logic-based global outsourcing decision support system to evaluate the “make or buy” decision in the apparel industry. A fuzzy logic-based decision support system is constructed due to the similarity between fuzzy logic and human decision-making systems. The proposed approach decreases the dependency of the decision-making process on the decision-maker, establishing an effective system that can easily be applicable to most of the apparel industry.

FUZZY SETS AND FUZZY LOGIC IN MULTI-CRITERIA DECISION MAKING. THE 50TH ANNIVERSARY OF PROF. LOTFI ZADEH’S THEORY: INTRODUCTION

„Fuzzy sets and fuzzy logic in multi-criteria decision making. The 50th anniversary of Prof. Lotfi Zadeh’s theory: introduction" Technological and Economic Development of Economy, 21(5), pp. 677–683

Screening Diagnosis of Gastric Cancer Based on Fuzzy Models of Blood Composition and on Patient’s Anamnesis

Synthesis of fuzzy models based on equations derived by specialized software for group methods of data handling, which reflect the structural changes of blood composition in case of gastric cancer, and history of the subject using fuzzy decision logic for early diagnosis of the disease is described.

Fuzzy Logic based Assessment Model Proposal for Online Problem-based Learning

In this research, problem based learning over web is suggested. This model includes fuzzy logic and MYCIN trust factor. Through the model, MYCIN trust factor and the number of attempts to solve the problem are used in order to identify students' learning levels. MYCIN confidence factor value and number of attempts to solve the problem are to be entered to fuzzy logic decision system. The output of the fuzzy logic decision system becomes the new level of learning. This level of learning is calculated through fuzzy logic in linguistic term as well as numeric expression. In every try, a hint given to student. Each used hint lowers the score of the student. Thus the students who solved the problem in one try will score more than the ones who solved the problem in more than one try.

Bioengineering System for Prediction and Early Prenosological Diagnostics of Stomach Diseases based on Energy Characteristics of Bioactive Points with Fuzzy Logic

We apply mathematical models for the interaction of the internal and biologically active points of meridian structures. Amongst the diseases for which reflex diagnostics are effective are those of the stomach disease. It is shown that use of fuzzy logic decision-making yields good results for the prediction and early diagnosis of stomach diseases, depending on the reaction energy of biologically active points (acupuncture points). It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained by using fuzzy logic decision-making. Analysis of resistances lower and higher than the nominal values allowed us to specify the prognostic and diagnostic models.

A New Energy Management Technique for PV/Wind/Grid Renewable Energy System

An intelligent energy management system (IEMS) for maintaining the energy sustainability in renewable energy systems (RES) is introduced here. It consists of wind and photovoltaic (PV) solar panels are established and used to test the proposed IEMS. Since the wind and solar sources are not reliable in terms of sustainability and power quality, a management system is required for supplying the load power demand. The power generated by RES is collected on a common DC bus as a renewable green power pool to be used for supplying power to loads. The renewable DC power bus is operated in a way that there is always a base power available for permanent loads. Then the additional power requirement is supplied from either wind or PV or both depending upon the availability of these power sources. The decision about operating these systems is given by an IEMS with fuzzy logic decision maker proposed in this study. Using the generated and required power information from the wind/PV and load sides, the fuzzy reasoning based IEMS determines the amount of power to be supplied from each or both sources. Besides, the IEMS tracks the maximum power operating point of the wind energy system.

Availability Principle Applied to a Fuzzy Supervisory Control of a Pilot Flotation Column

This work presents a study of fuzzy expert control system, based on the availability principle, of a pilot flotation column at the Hybrid Process Control Laboratory in Santa Maria University. Sensors and actuators are communicated to a programmable Logic Controller (PLC) where calibrations and local control are implemented, and the data is sent to a PC network, where a Distributed Control System (DCS) is running. Process model predictions are obtained by running a simulator fed with actual operating variables and virtual variables associated to feed characteristics. All data is displayed at PC stations and historical data files are also built for further analysis. Operating variables (real and virtual) can also be analyzed off-line by advanced software tools. Supervisory control running on PC network writes DCS set points on PLC.The supervisory control performance was experimentally evaluated by keeping the mineralogical objectives of concentrate grade and recovery in narrow bands. The operation of the supervisor was compared to a base case where there is no supervisory control, and each experiment was subjected to stochastic auto-correlated disturbances in the feed conditions. The fuzzy supervisory control, unlike conventional control, is more capable of maintaining the metallurgical results within control limits during almost 90% of the operating time. An availability principle for manipulated variables was successfully tested acting on top of the fuzzy logic decision system.

Fusing fixed and hint landmarks on crowd paths for automatically constructing Wi-Fi fingerprint database

In typical Wi-Fi based indoor positioning systems employing fingerprint model, plentiful fingerprints need to be trained by trained experts or technician, which extends labor costs and restricts their promotion. In this paper, a novel approach based on crowd paths to solve this problem is presented, which collects and constructs automatically fingerprints database for anonymous buildings through common crowd customers. However, the accuracy degradation problem may be introduced as crowd customers are not professional trained and equipped. Therefore, we define two concepts: fixed landmark and hint landmark, to rectify the fingerprint database in the practical system, in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint landmark. Machine-learning techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces space. Besides, the particle filter algorithm is also introduced to smooth the sample points in crowd paths. We implemented the approach on off-the-shelf smartphones and evaluate the performance. Experimental results indicate that the approach can availably construct Wi-Fi fingerprint database without reduce the localization accuracy.

Construction Contractors Ranking Method Using Multiple Decision-Makers and Multiattribute Fuzzy Weighted Average

The contractor selection decision is important and greatly impacts the overall success of any project; therefore, there is a need for quantitative methods to help stakeholders, including owners, make better decisions about contractor selection. Although previous studies have presented different methodologies for contractor selection, the subject of contractor selection remains attractive to many researchers. This paper enhances the construction management profession by introducing a new contractor selection method that incorporates multiple decision-makers. This paper introduces a quantitative fuzzy-set method to assist in the contractor selection process by implementing multiattribute decision-making that integrates a group of decision makers. Although the preferred attributes that contribute to contractor selection are dependent on the project, the method presented in this paper can be implemented for any attributes determined important to contractor selection in a specific case. The method introduced in the research reported in this paper is intended to help decision makers rank different contractors based on various attributes. The triangular fuzzy-set model is implemented to define the linguistic terms used to describe subjective judgment related to decision makers’ experience level, attribute weight assessment, and selection attributes rating. Many uncertainties and ambiguities arise in contractor selection as this decision is a multiattribute group decision with time pressure; therefore, the fuzzy weighted-average method can be used to simulate the grouping of multiple decision-makers. The research reported in this paper models different levels of experience for decision makers involved in the evaluation process (up to 10 decision-makers) using fuzzy membership functions to express their experience level. Different decision-makers input their beliefs about the importance of a set of attributes that contribute to the decision to prequalify a given contractor using linguistic terms, which are converted into membership functions. The decision makers rate different contractors (up to five contractors) using linguistic terms, which are converted into membership functions that represent the degree to which decision makers believe a contractor meets the set of criteria important to the prequalification decision. The results of the research reported in this paper provide project stakeholders with valuable insights into the contractor selection problem. A computer program which uses the fuzzy-logic approach to modeling the multiattribute decision-making process is introduced. This program can be implemented by stakeholders to make better contractor-selection decisions. This computer program has been validated by five developers in a real case study. The results of the computer program agreed with the actual contractor selected by the developers of the project. The new contribution of this paper lies in the fact that it implements subjective judgment using fuzzy logic in the contractor selection decision and introduces multiple decision-makers into the quantitative solution introduced in the research reported in this paper.

Assessment of Transmission Line Condition Based on Neural Networks and Fuzzy Logic Decision

Accurate assessment of the operational status of transmission line,and the line status timely warning, can protect the stable operation of the transmission line. Not high accuracy assessment for the current state of the transmission line,assessment model of transmission line state based on neural networks and fuzzy logic decisions are put forward,built on online monitoring system, Considering the conductor temperature, angle, tension and other parameters.Using BP neural network convergence line fault membership values, and the output state is line fault probability. Then ,the uncertainty of the state probability output line integrated assessmen through Fuzzy Reasoning.

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class

Using Fuzzy Logic Decision Support System to Predict the Lifted Weight for Students at Weightlifting Class