Fuzzy Logic Tool Research Articles

A risk-based approach to drive conceptual design taking into account low-maturity products

The presence of immature concepts makes difficult decision making at upstream phase of a development project. Rework tasks in collaborative development projects dealing with immature design concepts are very frequent and are responsible of cost overruns and schedule delays. In addition, decisions taken during conceptual design stage have the most important influence on product life cycle cost. In this context, a useful practice is to develop two or more concepts in parallel and delay decision making. Even so, a first screening of concepts is usually needed because of the substantial resources and time required in development phase. In this paper, an approach of concepts screening is proposed at conceptual design phase on the basis of two metrics. The first assesses ability of concepts to meet requirements with integration of maturity. Fuzzy logic tools are used to capture and propagate imprecision in embodiment design choices and behavior models. The second metric aims to assess expected performance of novel concepts compared with an existent product. It uses generalized ordered weighted averaging operator. A parametric aggregation function is also proposed as a support when using the two metrics. The proposed approach was applied to the development of a solar collector.

Integration of concept maturity in decision-making for engineering design: an application to a solar collector development

The presence of immature concepts makes decision-making difficult in upstream phases of a development project. A company’s enthusiasm to improve product performance by exploring novel concepts is hampered by a low maturity that results in an inherent risk of cost overruns and schedule delays. Besides this, decisions taken during conceptual design phases have a critical impact on product life-cycle cost. In this context, a useful practice is to develop two or more concepts in parallel and delay decision-making until sufficient knowledge is generated. Even so, a preliminary screening of concepts is usually needed because of the substantial resources and time required for the development activities. In this paper, a concept screening approach is proposed at the conceptual design phase on the basis of two metrics. The first assesses risk related to the non-completion of design criteria while respecting available time (consideration of deadline) and company resources. The second metric assesses the expected performance of novel concepts compared with the existing products. It uses preference functions and generalized ordered weighted averaging operators. Fuzzy logic tools are used in the two metrics to capture and propagate imprecision in the description of design and external environment. In order to prove its value, the proposed approach was applied to the development of a solar collector in an industrial environment.

A fuzzy approach to a municipality grouping model towards creation of synergies

In recent years, a trend for accelerating the economic, social and environmental development of cities through associations, organization and creation of synergies has been identified. Our investigation applies a grouping model in order to identify municipalities that could create optimal synergies towards the construction of competitive advantages. In order to achieve this task, we use tools of Fuzzy Logic to evaluate subjective and qualitative characteristic elements of different municipalities under Galois’ group theory. Results conclude on 32 different groups ordered in 7 different levels, relating 12 municipalities of a specific region according to 8 competitive variables. This work seeks to shed light in the conformation of groups under uncertain conditions and the deep examination of the characteristic competitive elements in a specific region for further policy and decision-making processes.

Component Methodology for Creating and Implementing Organizational Innovations in Business Companies

Objectives: The study considers creation and implementation of organizational innovations in strategic management mechanisms. It aims to help a company select organizational innovation applying component methodology in a company’s internal environment. Methods: Suggested methodology describes the concept and methods for implementing organizational innovations focused on attaining strategic objectives of a company. The novelty of the methodology is in consideration of the uncertainty factors in the company’s evolution applying mathematical tools of fuzzy logic, thus reducing the effects of these factors on the decision-making process in selecting management methods for developing and customizing organizational innovations in the company’s business environment. Findings: Before introducing organizational innovation, an organizational management method has to be selected among such business administration practices as business-process engineering, knowledge management, quality management, lean production and continuous improvement. The study develops the methodology for selecting optimum management method based on applying fuzzy sets. The chain of activities in developing and integrating organizational innovations in the architecture of the company management is decomposed into nested structures that consist of the sets of homogenous metrics. The obtained criterion for fuzzy optimization makes it possible to select optimum options for revising managerial methods, making them economically efficient. Innovative approach to justifying decisions under conditions of uncertainty and with insufficient initial data, based on fuzzy methods, is unparalleled by any approaches described in the works of other authors. This approach employs combination of fuzzy logic and expert evaluations. The authors of this study have tested this approach to solve a number of tasks related to the subject matters that hardly lend themselves to mathematical formalization due to their quantitative nature. Applications/Improvements: This study helps to find the ways to improve competitive power of businesses by optimizing managerial mechanism at methodological level.

Power Load Flow by Fuzzy Sets and Different Membership Functions

Abstract. Fuzzy concept is one of the new and efficient tools in many applications. In last decade’s fuzzy logic have vastly applied in control systems, pattern recognition problems, prediction problems, noise cancellation, function approximation and many other problems. The fuzzy logic tools have excellent capabilities in function approximation and solving of nonlinear problems. Therefore, in this study application of fuzzy logic tools have been proposed for power flow solution. There are some methods and algorithms for power flow solution, such as Newton method. But this methods and algorithms are very sensitive to initial solutions of problem. Therefore the algorithm simply traps to local minima. For evaluate the proposed intelligent method, the one real world power system is applied. The mentioned power system has ten generators and thirty nine terminals. The obtained results show that the proposed hybrid system has good performance and capability in solving of power flow problem.

Use of Fuzzy Tool for Example Based Machine Translation

This research paper proposes work carried in machine translation. It shows the comparison of various techniques like SMT, SSER, EBMT, RBMT etc used for machine translation. After going through the result of various techniques it conclude that still better result can be gained . Thus proposes an idea of using Fuzzy logic tool to improve the natural learning process and get better result around 81.7 percentage. This work shows the implementation of the tool for refining the result in example based machine translation using fuzzy logic.

Mathematical, Fuzzy Logic and Artificial Neural Network Modeling Techniques to Predict Drying Kinetics of Onion

In this study, the influence of drying temperature (40, 50, 60C) and airflow velocity (2 and 3 m/s) on drying onion was evaluated by a custom designed fluidized bed dryer equipped with a heat pump dehumidifier. A comparative study was performed among nonlinear regression techniques, fuzzy logic and artificial neural networks to estimate the dynamic drying behavior of onion. Among nine mathematical models, approximation of diffusion with R2 = 0.9999 and root mean square error = 0.004157 showed the best fit with experimental data. Fuzzy logic tool in MATLAB with Mamdani model in the form of If–Then rules along with triangular membership function used for simulation, interpolation and obtaining a theoretical increase in experimental moisture ratios were used. Feedforward–backpropagation neural system with application of Levenberg–Marquardt training algorithm, hyperbolic tangent sigmoid transfer function, training cycle of 1,000 epoch and 2-5-1 topology was determined as the best neural model in terms of statistical indices. Practical Applications Forecasting kinetics of food drying by application of precise mathematical and dynamic modeling techniques as well as preparing particular patterns for describing such drying behaviors of diverse food products seem unavoidable for processing factories to optimize the quality of their dried food products and reduce operational costs. For this purpose, in this study, we applied different temperatures and airflow velocities to dry onion by a custom designed fluidized bed dryer equipped with a heat pump dehumidifier, predicting its drying behavior by regression, fuzzy logic and artificial neural network techniques and comparing accuracy of those models. Also, this paper compares prediction patterns of drying onion by fluidized bed drying with those of other food products dried by various drying methods. The results of this study will be helpful for all researchers and producers who want to know more about the nuances of impacts of different fluidized bed drying variables on the patterns of heat and mass transfer in fruits and vegetables.

Risk assessment of bridges under multiple hazards in operation period

Over the past several decades, the number of disasters around the world has been amplified. Moreover, the impacts of disasters on communities have attracted extra attentions, requiring efficient measures of disaster management. Similarly, safety problems of bridges have become public topics since bridges represent essential parts of highway infrastructure that have been exposed to multiple hazards during their life cycle. In this paper, a novel framework of disaster risk assessment is proposed, by combining Fuzzy Analytical Hierarchy Process (FAHP) with fuzzy knowledge representation and fuzzy logic techniques into a single integrated approach. A survey about collapsed bridges in the past few decades has been conducted. From this investigation the potential hazards have been identified. The FAHP approach is applied to ranking risk factors since it is more systematic, accurate and effective than traditional AHP. Risk indicators, the probability of occurrence, the impacts of disasters and disaster consequences are analyzed from expert opinions. The bridge risk is computed by implementing fuzzy logic tools on the risk factors and their parameters. The proposed method is practical and efficient for a quick and reliable multi-hazards risk analysis and assessment of bridges. Based on the level of disaster risks to which the bridge is exposed, an appropriate bridge safety management plan is required to reduce or prevent the effects of the disasters. A case study is examined for a bridge in Tianjin, People’s Republic of China. The results illustrate the applicability of the proposed risk assessment method of bridges under multiple hazards.

Definition of redox and pH influence in the AMD mine system using a fuzzy qualitative tool (Iberian Pyrite Belt, SW Spain).

Poderosa Mine is an abandoned pyrite mine, located in the Iberian Pyrite Belt which pours its acid mine drainage (AMD) waters into the Odiel river (South-West Spain). This work focuses on establishing possible reasons for interdependence between the potential redox and pH, with the load of metals and sulfates, as well as a set of variables that define the physical chemistry of the water-conductivity, temperature, TDS, and dissolved oxygen-transported by a channel from Poderosa mine affected by acid mine drainage, through the use of techniques of artificial intelligence: fuzzy logic and data mining. The sampling campaign was carried out in May of 2012. There were a total of 16 sites, the first inside the tunnel and the last at the mouth of the river Odiel, with a distance of approximately 10 m between each pair of measuring stations. While the tools of classical statistics, which are widely used in this context, prove useful for defining proximity ratios between variables based on Pearson's correlations, in addition to making it easier to handle large volumes of data and producing easier-to-understand graphs, the use of fuzzy logic tools and data mining results in better definition of the variations produced by external stimuli on the set of variables. This tool is adaptable and can be extrapolated to any system polluted by acid mine drainage using simple, intuitive reasoning.

PROGNOSIS OF GLEs OF RELATIVISTIC SOLAR PROTONS

Ground level enhancements (GLEs) are relativistic solar particles measured at ground level by the worldwide network of cosmic ray detectors. These sporadic events are associated with solar flares and are assumed to be of a quasi-random nature. Studying them gives information about their source and propagation processes, the maximum capacity of the Sun as a particle accelerator engine, the magnetic structure of the medium traversed, etc. Space vehicles, as well as electric transformers and gas pipes at high latitudes may be damaged by this kind of radiation. As a result, their prediction has turned out to be very important, but because of their random occurrence, up to now few efforts toward this goal have been made. The results of these efforts have been limited to possible warnings in real time, just before a GLE occurrence, but no specific dates have been predicted well enough in advance to prevent possible hazards. In this study we show that, in spite of the quasi-stochastic nature of GLEs, it is possible to predict them with relative precision, even for future solar cycles. Additionally, a previous study establishing synchronization among some periodicities of several layers of solar atmosphere argues against the full randomness of the phenomenon of relativistic particle production. Therefore, by means of wavelet spectral analysis combined with fuzzy logic tools, we reproduce previous known GLE events and present results for future events. The next GLE is expected to occur in the first semester of 2016.

Fuzzy approach for optimum replacement time of mixed infrastructures

Investments in infrastructure assets represent a sizable portion in the governments’ public fund. Continuous maintenance, rehabilitation, and replacement are required to maintain the level of service of infrastructure assets. Knowing the replacement needs of infrastructures and the timing of replacement are challenging tasks. This paper presents a decision support tool that aids in deciding the best time to replace several types of infrastructure assets, that is, mixed infrastructure. The paper uses fuzzy logic to model uncertainties in order to identify the useful lifetime of each infrastructure asset. Infrastructure replacement decision is made based on least cost option(s). A fuzzy logic tool is applied in three steps: data fuzzification, fuzzy inference, and data defuzzification. The developments made in the fuzzy logic tool are presented. A numerical example is presented to demonstrate the practical features of the proposed tool.

Fuzzy Logic based Greedy Routing (FLGR) in Multi-Hop Vehicular Ad hoc Networks

In Vehicular Ad Hoc Network (VANET), vehicles on the road provide mainly two types of services to drivers or passengers inside vehicles, namely, safety and comfort services. Safety messages are time-critical, therefore in this paper we have proposed a novel Fuzzy Logic based Greedy Routing (FLGR) protocol which assists in delivering safety messages to the destination vehicle with minimum delay. The proposed FLGR is a multi-hop routing protocol that uses multiple metrics of neighbor vehicles to decide which neighboring vehicle will be the approximate best next-hop node for further forwarding the packets. We have used the concept of fuzzy logic that helps in decision making in VANETs. Fuzzy logic deals with reasoning that is approximate rather than fixed and exact. Fuzzy logic variables may have truth values that range in degrees between 0 and 1. The fuzzy logic inference system became important and useful when the values of the decision criteria are not only vague but uncertain in nature. FLGR is used within the communication range of the current forwarding node that employs fuzzy logic for selecting the best next-hop node in multi hop VANETs. We have used the fuzzy logic tool box of MATLAB to evaluate our protocol. Metrics such as distance, speed, direction, and position of next-hop forwarding node are put into fuzzy logic system. Thus, our protocol helps in finding the approximate best next-hop node for routing the safety messages by employing fuzzy logic. It selects the next-hop node having maximum distance, speed, and progress and minimum angular deviation of nodes towards destination from current forwarding node. Further, FLGR is more suitable for safety applications in vehicular networks.

Comparison of ANFIS and ARIMA Model for Weather forecasting

In this paper quickly illustrate the correlation investigation of Auto-Regressive Integrated Moving and Average (ARIMA) and Adaptive Network Based Fuzzy Inference System (ANFIS) models done by climate estimating .The climate determining is taken from University of Waterloo. The information is taken as Relative Humidity, Ambient Air Temperature, Barometric Pressure and Wind Direction utilized within this paper. The paper is carried out by analyzing the exhibitions are seen by demonstrating of ARIMA and ANIFIS model like with Sum of average of errors. Versatile Network Based Fuzzy Inference System (ANFIS) demonstrating is carried out by Mat lab programming and Auto-Regressive Integrated Moving and Average (ARIMA) displaying is produced by utilizing XLSTAT programming. ANFIS is carried out in Fuzzy Logic Toolbox in Mat Lab. Keywords: ARIMA, ANFIS, Fuzzy Logic Tool, Weather Forecasting, XLSTAT Software

English

For electrical drives good dynamic performance is mandatory so as to respond to the changes in command speed and torque. This requirement of AC drives can be fulfilled by the vector control method which enables the control of field and torque of induction motor independently (decoupling) by manipulating corresponding field oriented quantities. The traditional indirect vector control system uses conventional PI controller in the outer speed loop because of the ease and constancy, unexpected change in load conditions or environmental factors produce overshoot, fluctuation of motor speed, oscillation of the torque and long settling time causes deterioration of drive performance. An intelligent controller based on Fuzzy Logic can be used in the place of PI regulator to overcome. In this paper application of fuzzy logic to the intelligent SVPWM based speed control of induction motor drive is investigated. To solve the objective of this paper MATLAB/ SIMULINK software is used. The superior control performance of the proposed controller is demonstrated at SIMULINK platform using the fuzzy logic tool box.

Contribution to Assessments of Impacts on the Critical Infrastructure

Assessing the vulnerability of critical infrastructure objects is of major concern when dealing with the process of dependability and risk management. Special attention is paid to the objects of higher interest, such as nuclear power plants. In spite of the protection of these objects, there is still a certain level of a potential threat. The aim of the paper is to describe a possible way of assessments of the consequences when attacking on an object of critical infrastructure. Several characteristics of the impacts outcomes were obtained. For this reason, as well as for modelling of adversary ́s behaviour impact, fuzzy logic tools have been used.

Estimating the average length of hospitalization due to pneumonia: a fuzzy approach.

Exposure to air pollutants is associated with hospitalizations due to pneumonia in children. We hypothesized the length of hospitalization due to pneumonia may be dependent on air pollutant concentrations. Therefore, we built a computational model using fuzzy logic tools to predict the mean time of hospitalization due to pneumonia in children living in São José dos Campos, SP, Brazil. The model was built with four inputs related to pollutant concentrations and effective temperature, and the output was related to the mean length of hospitalization. Each input had two membership functions and the output had four membership functions, generating 16 rules. The model was validated against real data, and a receiver operating characteristic (ROC) curve was constructed to evaluate model performance. The values predicted by the model were significantly correlated with real data. Sulfur dioxide and particulate matter significantly predicted the mean length of hospitalization in lags 0, 1, and 2. This model can contribute to the care provided to children with pneumonia.

Fuzzy Logic Modeling for Decision Making Processes Using MATLAB

— This paper demonstrates to build a Fuzzy Inference System (FIS) for any model utilizing the Fuzzy Logic Toolbox graphical user interface (GUI) tools. A different conception for decision making process, based on the fuzzy approach, is propounded by authors of the paper.The paper is worked out in two sections. Description about the Fuzzy Logic Tool box is done in the first section.Illustration with an introductory example concludes the second section. Based on various assumptions the authors construct the rule statements which are then converted into fuzzy rules and the GUI tools of the Fuzzy Logic Toolbox built using MATLAB numeric computing environment is used to construct a fuzzy inference system for this process.The output membership functions are expected to be fuzzy sets in Mamdani-type inference.Defuzzification of fuzzy set for each output variable generated after the aggregation process has to be carried out. Application of information technology for Decisions in today's environment which is highly competitive are undeniable principles of organizations and helps managers in making useful decisions meaningfully.

Adaptive Neural Network for Estimation of Sliding Wear Behaviour of Al6061-Carbon Fiber Composites

In recent years, Al6061-carbon fiber composites are gaining wide spread popularity as they find scope in certain high-tech applications such as automobile, aerospace, transport, andprocessing industries. Thesecomposites possess high strength to weight ratio, excellent wear resistance in addition to superior mechanical properties. The experimental method of determining wear phenomenon of the developed composites is an expensive as well as a tedious process.As such engineers and scientists are focusing their attention towards developing mathematical modelsfor determining wear phenomenon. The use of mathematical modeling for prediction of wear phenomenon is an evolving research area. Hence, meager information is available as regards the mathematical model to determine wear rate of composites. Mathematical modeling is slowly gaining impetus in industries in order to assess the life of sliding components and establishing the economic loss incurred due to the wear phenomenon. In the light of the above, Al6061 carbon fiber composites were prepared by liquid metallurgical route and then machined to a standard size of pin.On the pins, sliding wear test was conducted on a pin-on-disc apparatus using C-45 steel disc as per ASTM Standard. Data generated was then used in developing AdaptiveNeuro Fuzzy Inference System (ANFIS). The ANFIS logic was created using the fuzzy logic tool box of Matlab 7.10 Version. For simulating, actual working conditions used to establish the sliding wear behaviour of Al6061-xwt%Carbon fiber composites (x=5, 10) including variable parameters such as Varying load (from 10-60N in step of 10N), Sliding distance, Weight fraction (5-10%) keeping other parameters constant such as track diameter 20 mm, Speed 500rpm and Pin diameter 8 mm were used.The adopted fuzzy model employs hybrid learning techniques for updating the premise and consequent parameter. The predicted values of sliding wear rate of Al6061-xwt% carbon fiber are in close agreement with the experimental results.

A Fuzzy Logic Based Model to Predict Weld Width – A Case Study of Hard Facing Process Using MIG Welding on Dual Plate Check Valve

Welding input parameters play a very significant role in determining the quality of a weld joint. The joint quality can be defined in terms of weld-bead geometry. The 23 replication with 4 center points experiments are performed on non-return valve material WCB by varying various MIG Welding process parameters. Here welding current, welding speed and gas flow rate are considered as input parameters with two levels. A fuzzy model is developed to predict the weld width in context of these input parameters. Fuzzy model uses fuzzy expert rules, triangular membership function and centroid area method for defuzzyfication process using MATLAB fuzzy logic tool box. The developed model is validated by performing experiments at center points. The result shows prediction may lie in the range of 95.18-100%.

Simulation Analysis of Electro-Pneumatic Proportional Fuzzy-PID Control System Based on AMESim/Simulink

The test rig for hydro-pneumatic converter used in straddle type monorail vehicles was researched, and its electro-pneumatic proportional control system was set up and simulated based on AMESim/Simulink. Compared fuzzy-PID (Proportion Integral Derivative) controller with PID controller through fuzzy logic tool box in Simulink, the results indicate that, this electro-pneumatic proportional control system can meet design requirements better, and fuzzy-PID controller has higher accuracy and stability than PID controller.