Fuzzy Logic-based Model Research Articles

Fuzzy Techniques for Decision Making

This book contains the successful invited submissions [1–21] to a Special Issue of Symmetry on the subject area of “Fuzzy Techniques for Decision Making”.[...]

Fuzzy probabilistic seismic hazard analysis with applications to Kunming city, China

China is prone to highly frequent earthquakes due to specific geographical location, which could cause significant losses to society and economy. The task of seismic hazard analysis is to estimate the potential level of ground motion parameters that would be produced by future earthquakes. In this paper, a novel method based on fuzzy logic techniques and probabilistic approach is proposed for seismic hazard analysis (FPSHA). In FPSHA, we employ fuzzy sets for quantification of earthquake magnitude and source-to-site distance, and fuzzy inference rules for ground motion attenuation relationships. The membership functions for earthquake magnitude and source-to-site distance are provided based on expert judgments, and the construction of fuzzy rules for peak ground acceleration relationships is also based on expert judgment. This methodology enables to include aleatory and epistemic uncertainty in the process of seismic hazard analysis. The advantage of the proposed method is in its efficiency, reliability, practicability, and precision. A case study is investigated for seismic hazard analysis of Kunming city in Yunnan Province, People’s Republic of China. The results of the proposed fuzzy logic-based model are compared to other models, which confirms the accuracy in predicting the probability of exceeding a certain level of the peak ground acceleration. Further, the results can provide a sound basis for decision making of disaster reduction and prevention in Yunnan province.

Fuzzy Logic-Based Model to Predict the Impact of Flow Rate and Turbidity on the Performance of Multimedia Filters

AbstractThis paper uses fuzzy logic–based models to predict and evaluate the performance of multimedia filters utilized in wastewater treatment. A fuzzy logic–based model is constructed and trained...

Design of cross-coupled CMAC for contour-following – a reinforcement-based ILC approach

ABSTRACTOne of the most popular applications of a bi-axial motion stage is precision motion control. The reduction of tracking error and contour error is one of the most coveted goals in precision motion control systems. The accuracy of a motion control system is often affected by external disturbances. In addition, system non-linearity such as friction also represents a major hurdle to motion precision. In order to deal with the aforementioned problem, this paper proposes a fuzzy logic-based Reinforcement Iterative Learning Control (RILC) and a Cross-Coupled Cerebellar Model Articulation Controller (CCCMAC). In particular, the proposed fuzzy logic-based RILC and a LuGre friction model-based compensation approach are exploited to improve motion accuracy. The fuzzy logic-based RILC aims at reducing tracking error and compensating for external disturbance, while the LuGre friction model is responsible for friction compensation. In addition, the CCCMAC consisting of a cerebellar model articulation controller and a cross-coupled controller aims at reducing contour error and dealing with the problem of dynamics mismatch between different axes. Performance comparisons between the proposed fuzzy logic-based Reinforcement Iterative Learning Cross-Coupled Cerebellar Model Articulation Controller (RIL–CCCMAC) and several existing control schemes are conducted on a bi-axial motion stage. Experimental results verify the effectiveness of the proposed RIL–CCCMAC.

A fuzzy logic-based model for analysis and evaluation of services in a manufacturing company

In an increasingly competitive market, companies must look for new ways to gain competitive advantage. One of these ways is the provision of services. This article presents a multicriteria model for analysis and evaluation of services in a manufacturing company. We divided the services that the manufacturing company can offer to its customers into pre-sales, sales and after-sales services. In the second phase we developed a specific model for the furniture industry. The model was tested with information obtained in a survey by eight kitchen manufacturers from four European countries and was applied to determine the level of services offered. Results have shown that the model can be a useful tool when evaluating the competitiveness of individual manufacturing companies in terms of service provision. Based on the results, companies can develop their business strategies to meet the needs and expectations of potential customers from this perspective as well.

Fuzzy logic-based model for predicting material removal rate and average surface roughness of machined Nimonic 80A using abrasive-mixed electro-discharge diamond surface grinding

In this paper, a fuzzy logic artificial intelligence technique is delineate to predict the material removal rate (MRR) and average surface roughness (Ra) during abrasive-mixed electro-discharge diamond surface grinding (AMEDDSG) of Nimonic 80A. Though, Nimonic 80A superalloy is extensively used in aerospace and automotive industries due to its high corrosion, fracture toughness, oxidation, and temperature resistance characteristics, being a difficult-to-cut material, its machining is a challenging job. The hybrid machining processes like AMEDDSG can be competently used for machining of Nimonic 80A. The face-centered central composite design is used consummate the experiments and then experimental data are used to establish fuzzy logic Mamdani model to predict the MRR and Ra with respect to changes in the input process parameters viz. wheel RPM, abrasive concentration, pulse current and pulse-on-time. The results of confirmation experiments reveal an agreement between the fuzzy model and experimental results with 93.89 % accuracy implying that the established fuzzy logic model can be precisely used for predicting the performance of the AMEDDSG process. An increase in wheel RPM, pulse current, and pulse-on-time from their low level to high level contributes to increased MRR by 83.89, 71.01, 17.02 %, respectively. Also, an increase in wheel RPM contributes to reduced Ra values by 5.96 %. Abrasive concentration increase from 0 to 4 g/L improves MRR by 24.03 %. The 17.10 % improvement in surface finish is achieved by increasing abrasive concentration from 0 to 8 g/L.

Fuzzy logic-based predictive model for biomass pyrolysis

As pyrolysis reaction is one of an important reaction applied to lignocellulosic biomass in order to transform it to be user-friendly energy form recognized as prospective alternative energy source, the reaction has been widely investigated in order to understand the mechanisms and kinetics of the pyrolysis. However, modeling pyrolysis of biomass is full of complication. As lignocellulosic biomass is not a homogeneous chemical source, chemical compositions in biomass are also uncertain and they vary even in the same biomass. The reactions of imprecise chemical compositions in biomass affects the capability of deterministic model in modeling chemical reaction since available deterministic models are designed to model homogeneous and precise chemical compositions. With this problem, it raises the idea of using model which has ability to calculate something ambiguous. Since the fuzzy logic-based model which is adaptive network-based fuzzy inference system (ANFIS) is built to calculate uncertainty, the model should be suitable to handle uncertainty which is imprecise chemical compositions in the reaction. The proposed model is built with four input variables: the reaction time, amount of cellulose component, amount of hemicellulose component, and amount of lignin component in biomass. The model is trained with tuning datasets which are the pyrolysis datasets of lignin, cellulose and Madhuca before applying to predict the pyrolysis reactions of Pongamia pinnata and Jatropha curcas. The comparative results show that the proposed model can correctly predict 91.82% and 97.29%, respectively, of the pyrolysis reactions of P. pinnata and J. curcas. As the ANFIS model gives good prediction in modeling pyrolysis of two different biomasses, the model can be applied to predict the pyrolysis reaction of other lignocellulosic biomass products.

Hybrid decision support system using PLSR-fuzzy model for GSM-based site-specific irrigation notification and control in precision agriculture

In this paper, a partial least square regression PLSR and fuzzy-logic based smart decision support system DSS for crop-specific irrigation notification and control in precision agriculture is proposed, and this can be implemented in farm land, green-house and poly-house. The proposed DSS model continuously acquires real-time soil and environmental data using Zigbee wireless sensor network WSN with WiFi gateway developed during this work. The collected data are used to predict hourly soil moisture content MC variation and required evapotranspiration to control farm irrigation by utilising fuzzy logic-based weather model. A comparative analysis of soil MC variations is also performed by calculating root mean square error RMSE, R-squared error RSE, mean squared error MSE, ratio of performance to deviation RPD and algorithm running time. Cropwise evapotranspiration is also calculated using Blaney-Criddle method which is attached to the proposed DSS model by taking weather, soil, water and crop data into considerations.

A fuzzy logic approach for detecting redirection spam

Redirection spam is a relatively newer technique whereby spammers redirect the search user to an unwanted webpage or download malware on the victim's machine without his consent. Spammers are making use of chained redirections to hide their nefarious activities. Detecting such malicious redirections is of prime importance for maintaining web security. In this paper, we have identified the factors that assist in detecting redirection spam and propose a fuzzy logic-based model for redirection spam detection. We validated our model against a set of URLs and were able to detect the spammed redirections with high accuracy.

Development of a Fuzzy Logic-based Model for Monitoring Cardiovascular Risk

Cardiovascular diseases (CVD) are top killers with heart failure as one of the most leading cause of death in both developed and developing countries. In Nigeria, the inability to consistently monitor the vital signs of patients has led to the hospitalization and untimely death of many as a result of heart failure. Fuzzy logic models have found relevance in healthcare services due to their ability to measure vagueness associated with uncertainty management in intelligent systems. This study aims to develop a fuzzy logic model for monitoring heart failure risk using risk indicators assessed from patients. Following interview with expert cardiologists, the different stages of heart failure was identified alongside their respective indicators. Triangular membership functions were used to fuzzify the input and output variables while the fuzzy inference engine was developed using rules elicited from cardiologists. The model was simulated using the MATLAB® Fuzzy Logic Toolbox.

Fuzzy Logic-Based Model to Stratify Cardiac Surgery Risk

Background: Medical practice is usually performed in a context of uncertainty, where expert knowledge is used for efficiencyin the decision-making process.Objective: The aim of this study was to develop and validate a fuzzy logic-based model to predict cardiac surgery mortality risk.Methods: Four hundred and fifty patients undergoing cardiac surgery were prospectively included in the study and mortalityrisk was predicted based on five scores: 1) “clinical expert” opinion, 2) fuzzy logic-based system according to expert knowledge,3) Parsonnet, 4) Ontario and 5) EuroSCORE. The fuzzy logic model was developed in the following stages: expert selectionof different mortality predictive variables, tables of influence among variables, construction of a fuzzy cognitive map (FCM)and its implementation in an artificial neuronal network, expert-determined patient risk score, test set risk calculation basedon fuzzy predictors, validation set risk using calibrated FCM, and comparison with the other scores according to the level ofagreement and precision with ROC curves.Results: The calibrated model was used to predict the outcome of the validation set (360 patients), based on the FCM scoreand risk predicted by Parsonnet, Ontario and EuroSCORE. The ROC areas showed that FCM had at least the same performanceas other scores to predict mortality (ROC=0.793 vs. 0.775, 0.767, 0.741 and 0.701 for EuroSCORE, “expert”, Ontarioand Parsonnet, respectively).Conclusions: A fuzzy logic-based system employing expert knowledge and the implementation of an expert system is postulatedto predict cardiac surgery mortality risk. The model not only mimicked the outcomes obtained by the “expert”, but had thesame performance as others risk scores.

Fuzzy Logic-based Electrothermal Life Model for Inverter-fed Induction Motor Insulation

Pulse width modulated adjustable speed drives used in industries lead to significant advantages in terms of performance, size, and efficiency. But, the output voltage and the current waveforms become non-sinusoidal leading to an increase in voltage and thermal stresses. This results into accelerated insulation aging and premature failure of the motors. With multiple stresses, the life models for insulating materials become complex and ambiguous. This article presents the fuzzy logic application to derive an electrothermal life model to investigate the synergic effects of voltage and thermal stresses on intrinsic aging of inverter-fed induction motor insulation. Three parameters, viz. voltage stress factor, waveform slope stress factor, and spike frequency factor, are proposed to describe the insulation stresses for pulse width modulated voltages. These parameters are computed from the experimental results at different switching frequencies and are used in fuzzy logic-based life estimation algorithms. The results of the fuzzy logic life model are verified by performing the accelerated aging test with the same pulse width modulated voltages on paper insulation. An electrothermal life model is derived from the fuzzy logic results, which can be directly used for the life estimation of any general purpose low-voltage inverter-fed induction motor insulation.

Fuzzy logic approach and sensitivity analysis for agent-based crowd injury modeling

A crowd is a group of people attending a public gathering with some joint purpose, such as protesting against the government or celebrating an event. In some countries, these kinds of activities are the only way to express public displeasure with their government. The government’s reactions to such activities may or may not be tolerant. For this reason, such situations must be eliminated by recognizing when and how they are likely to occur, and then providing guidelines to mitigate them. In urban areas, police and military forces use non-lethal weapons (NLWs), such as rubber bullets or clubs, to control a violent and destructive crowd. In order to estimate the results of this engagement, ensuring minimum injuries and reaching an optimal end state, simulating such actions in a virtual environment is necessary. In this work, a fuzzy logic-based crowd injury model for determining the physical effects of NLWs is proposed. Fuzzy logic concepts can be applied to a problem by using linguistic rules, which are determined by problem domain experts. A group of police and military officers were consulted for a set of injury model rules, and those rules were then included in the simulation platform. Sensitivity analysis has been conducted to analyze parameters in the model. As a proof of the concept, a prototype system was implemented using the Repast Simphony agent-based simulation toolkit. Simulation results illustrated the effectiveness of the simulation framework.

Fuzzy assessment of herbicide resistance risk: Glyphosate-resistant johnsongrass, Sorghum halepense (L.) Pers., in Argentina's croplands

A fuzzy-logic based model was built in order to assess the relative influence of different ecological and management drivers on glyphosate resistance risk (GRR) in Sorghum halepense (L.) Pers. The model was hierarchically structured in a bottom-up manner by combining 16 input variables throughout a logical network. Input data were related to 1) herbicide usage, 2) crop rotation, 3) landscape characterization, 4) weed dispersal, and 5) mean maximum and minimum seasonal temperature. Mean maximum and minimum seasonal temperatures and the dominance of glyphosate use were the variables that showed the highest sensitivity to input changes. Application of the model at a regional scale resulted in a wide range of GRR values. The lowest range values (lower than 0 and between 0 and 0.25) were represented in 5.5% and 21.5% of the cropping area, respectively. Intermediate GRR range (between 0.25 and 0.5) were assessed in 57.3% of the cropping area whilst the highest GRR range values (0.5–0.7) were represented in only 15.6% of the studied area. The assessment of trade-offs between different ecosystem functions through expert opinion can complement traditional analyses for predicting herbicide resistance risk based on solely the genetic aspect of the evolutionary process.

Fuzzy logic-based model for prediction of building wall humidity level

This paper presents the viability of using fuzzy logic expert system for the prediction of building wall’s relative humidity. In comparison with traditional logic model, fuzzy logic is more efficient in linking the multiple units to a single output and is invaluable supplements to classical hard computing techniques. The purpose of this work was to investigate the relationship between wall material composition and climate condition, as well as to evaluate the viability of using fuzzy logic expert system in predicting the building wall’s humidity level. The experimental data was taken at different time of the day for different material composition. A fuzzy logic expert system model based on Mamdani approach was subsequently developed to predict the building wall’s humidity level. Verification of the developed fuzzy logic model was carried out through various numerical error criteria. The study revealed that the developed fuzzy system was able to predict the humidity level in the wall material within high accuracy with relative error of less than the acceptable limits of 10% and a goodness of fit very close to unity.

Fuzzy Logic Model for Selection of Vertical Formwork Systems

Formwork systems have a considerable share in building construction costs. Accordingly, selecting an appropriate formwork system that respects project individualities plays an important yet challenging role in achieving significant cost savings. In Egypt, current practice in formwork selection primarily uses the subjective decisions of site engineers. This paper presents a fuzzy logic-based model that supports decision makers in selecting the appropriate vertical formwork system for projects. Ten commercial vertical formwork systems commonly used in Egypt are identified with the most important factors affecting their selection. The data were tabulated to serve as a knowledge-based system (KBS) to guide site engineers in selection. On the basis of the KBS, a fuzzy logic model with five input variables, representing the governing factors, is then developed to automate the selection process. The model is tested by applying it on a real-life case study. Results are acceptable and show a promising role for fuzzy logic in formwork selection. DOI: 10.1061/(ASCE)CO.1943-7862 .0000490. © 2012 American Society of Civil Engineers. CE Database subject headings: Fuzzy sets; Construction management; Construction costs; Formwork (concrete construction). Author keywords: Vertical formwork system; Fuzzy logic; Construction engineering; Knowledge-base.

Residential Building Products Evaluation of Towns and Villages Based on Fuzzy Logic

Product evaluation is the basis for product decisions，and it is also the basis for the correct formulation of the product strategy. The product quality of rural residential buildings related to the pace of building new socialist countryside, to improve product quality, to establish product fuzzy logic-based evaluation model, and through case demonstrates the feasibility and value.

Prediction of ternary gas permeation through synthesized PDMS membranes by using Principal Component Analysis (PCA) and fuzzy logic (FL)

Implementation of try and error method for membrane preparation procedures is a time and cost consuming technique. This study tries to present a novel idea to make membrane preparation procedure heuristic. Applying this method, helps researchers to predict performance of a membrane prior to its preparation. At first, a number of membranes are prepared and characterized. Then, their measured separation properties are used for prediction of performance of a membrane before its preparation. Furthermore, after preparation of each new membrane, its relevant data can be added to the data bank of the model to improve its capability for the next predictions. Therefore, this model will be improved step by step, after each new preparation. Fuzzy logic-based (FL) model and Principal Component Analysis (PCA) were employed to predict permeability of C 3H 8, CH 4 and H 2 in ternary gas mixtures using a membrane gas separation module. Based on Placket–Burman (P–B) design, eight different polydimethylsiloxane (PDMS) membranes were synthesized using different preparation conditions, solvent concentration, crosslinker concentration, catalyst concentration, type (or boiling point) of solvent, stirring time and synthesis times in ambient temperature and in an oven at 80 °C, and their gas permeation properties were investigated. In an innovating procedure, effects of operating conditions, including feed temperature, pressure, flow rate, C 3H 8 and H 2 concentration, as well as preparation conditions on the permeability of gasses through the synthesized membranes were investigated. Basically, in order to develop a FL model to predict permeability of gasses through all the synthesized membranes, synthesis and operating conditions should be considered, simultaneously, and this extends dimensionality of the problem. In all engineering problems, as the number of variables increases, the corresponding data matrix extends. To overcome the problem, PCA method was randomly used for seven of the prepared membranes from P–B design, and it was shown that the first four principal components could describe almost all of the variation in the data matrix. This means that the dimensionality of the problem reduced from 12 to 4. Using the first four principal components, a Sugeno type FL inference system was trained and applied to predict permeability of gasses. FL modeling results showed that there is an excellent agreement between the experimental data and the predicted values, with mean squared relative error (MSRE) of less than 0.0095. The developed model was used for the 8th membrane and its ability to predict separation parameters of this membrane was confirmed.

Credit evaluation model based on fuzzy logic

The fact that the credit speculative behavior has not been identified rightly by the credit evaluation system of Consumer to Consumer (C2C) results in a problem that transaction data can not be a true reflection of the seller's credit. In order to solve this problem,a fuzzy logic-based credit evaluation model was proposed in this paper. This model set up a domain and the fuzzy set of domain for credit factors,in which fuzzy membership function was employed to get the fuzzy evaluation of various transaction periods,and the result of credit evaluation was obtained. This empirical research shows the model completes the credit evaluation system,provides a more reliable basis for the users' trading decisions,and reduces the credit risk in the transaction.

A fuzzy logic-classification of sediments based on data from in vitro biotests

Background, aim, and scope Ecotoxicological risk assessment of sediments is usually based on a multitude of data obtained from tests with different endpoints. In the present study, a fuzzy logic-based model was developed in order to reduce the complexity of these data sets and to classify sediments on the basis of results from a battery of in vitro biotests.