Fuzzy Linguistic Model Research Articles

Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods

Classic multi-criteria group decision making models that have a high amount of alternatives are unmanageable for the experts. This is because they have to provide one value per each alternative and criteria. In this paper, we focus on solving this issue by carrying out multi-criteria group decision making methods using a different novel approach. Concretely, fuzzy ontologies reasoning procedures are used in order to automatically obtain the alternatives ranking classification. Thanks to our novel methodology, experts only need to provide the importance of a small set of criteria values making it possible for experts to perform multi-criteria group decision making procedures that have a high amount of alternatives without having to directly deal with them. Furthermore, in order to allow experts to provide their preferences in a comfortable way, multi-granular fuzzy linguistic modelling is used in order to allow each expert to choose the linguistic label set that better fits him/her.

Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks

Indoor localization is a basic process in Wireless Sensor Networks (WSN) monitoring. This paper presents a new approach for localization of mobile nodes in WSNs. The proposed approach is based on the design of an adaptive fuzzy localization system. First proposed contribution is to consider the rooms of the target environment as a fuzzy sets made by adjacent zones described by a Fuzzy Location Indicator (FLI). FLI provides a fuzzy linearization of the building map hence the creation of a fuzzy linguistic model of the system. Fingerprints of the Radio Signal Strength Indicators (RSSI) are collected from different anchors according to each FLI. A Sugeno type-0 fuzzy inference system is proposed and submitted to a supervised learning through the neuro-fuzzy ANFIS algorithm. Simulation results as well as experimentations in Cynapsys company premises have proved that a good learning process leads to high success rate. Finally, a comparative study with two fuzzy localization systems proved the lower localization error average of the proposed approach.

Selecting firms in University technoparks: A hesitant linguistic fuzzy TOPSIS model for heterogeneous contexts

Selecting firms in University technoparks: A hesitant linguistic fuzzy TOPSIS model for heterogeneous contexts

A knowledge-based outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets

The modeling and solving of multi-criteria decision-making (MCDM) problems under uncertainty is still a challenging topic. In real-life decision-making, using linguistic terms to represent experts’ judgments is suitable and straightforward since precise quantitative values may often be unavailable or the cost for their computation is too high. The introduction of hesitant fuzzy linguistic term sets (HFLTSs) was motivated by the limitations of prior linguistic fuzzy models and need for richer linguistic tools. However, since their introduction, comparing HFLTSs is still one of the major concerns of researchers in this area. The existing approaches in the literature commonly rely on (1) labels and intervals from the linguistic terms as the central elements of an envelope-based approach or (2) linguistic scale functions as the basis of a distance-based approach. The two approaches retain certain shortcomings resulting information distortion and loss which may inevitably degrade their credibility. In this paper, the authors are involved in the recent proposal of combining outranking approaches with HFLTSs in an MCDM context. After reviewing the existing approaches, an outranking method based on a novel knowledge-based paradigm for comparing HFLTSs is developed. Alternatively, the paradigm's foundations are the introduced concepts of fuzzy preference relations and profiles considering uncertainty degrees in decision makers’ assessments. The paradigm is then associated with a multi-criteria relational clustering (MCRC) algorithm that additionally extracts fuzzy preference relations between the resultant clusters. Last, an illustrative example is given to verify the appropriateness and efficacy of the developed approach and comparisons are made with other existing ones.

Research on efficiency evaluation model of integrated energy system based on hybrid multi-attribute decision-making.

The efficiency evaluation model of integrated energy system, involving many influencing factors, and the attribute values are heterogeneous and non-deterministic, usually cannot give specific numerical or accurate probability distribution characteristics, making the final evaluation result deviation. According to the characteristics of the integrated energy system, a hybrid multi-attribute decision-making model is constructed. The evaluation model considers the decision maker's risk preference. In the evaluation of the efficiency of the integrated energy system, the evaluation value of some evaluation indexes is linguistic value, or the evaluation value of the evaluation experts is not consistent. These reasons lead to ambiguity in the decision information, usually in the form of uncertain linguistic values and numerical interval values. In this paper, the risk preference of decision maker is considered when constructing the evaluation model. Interval-valued multiple-attribute decision-making method and fuzzy linguistic multiple-attribute decision-making model are proposed. Finally, the mathematical model of efficiency evaluation of integrated energy system is constructed.

A new nearest neighbor classification method based on fuzzy set theory and aggregation operators

The Fuzzy Nearest Neighbor Classification (FuzzyNNC) has been successfully used, as a tool to deal with supervised classification problems. It has significantly increased the classification accuracy by considering the uncertainty associated with the class labels of the training patterns. Nevertheless, FuzzyNNC's limited methods fail to efficiently handle the imprecision in features measurement and the uncertainty induced by the choice of the distance measure and the number of neighbors in the decision rule. In this paper, we propose a new method called Fuzzy Analogy-based Classification (FABC) to tackle the FuzzyNNC limitations. In this work, we exploit the fuzzy linguistic modeling and approximate reasoning materials in order to endow FABC with intelligent capabilities, like imprecision tolerance, optimization, adaptability and trade-off. Hence, our approach is composed of two main steps. Firstly, we describe the domain features using fuzzy linguistic variables. Secondly, we define the classification process using two intelligent aggregation operators. The first one allows the optimization of the similarity evaluation, by defining the adequate features to be considered. The second one integrates a trade-off strategy within the decision rule, by using a global voting approach with compensation property. The integration of such mechanisms will increase the classification accuracy and make the FuzzyNNC approach more useful for classification problems where imprecision and uncertainty are unavoidable. The proposed FABC is validated on the most known datasets, representing various classification difficulties and compared to the many extensions of the FuzzyNNC approach. The results obtained show that our proposed FABC method can be adapted to different classification problems and improve the classification accuracy. Thus, the FABC has the best rank value against the comparison methods with high significant level. Moreover, we conclude that our optimized similarity and global voting rule are more robust to handle the uncertainty in the classification process than those used by the comparison methods.

A Fuzzy Linguistic Extended LibQUAL+ Model to Assess Service Quality in Academic Libraries

LibQUAL+ model is the best-known method for the quality evaluation of library services, but it has two major drawbacks. First, to measure the quality, it is devised on a cardinal scale: the service levels range from 1 to 9. However, the standard representation of the concepts used by humans for communication is the natural language and, hence, users should express their judgments by using words instead of numbers. Second, it considers that all users’ opinions are equally important. Nevertheless, users do not play an equal role in assessing the service quality, i.e., the opinion given by some users should be more relevant than the opinion provided by others. To solve these drawbacks, we present an extended LibQUAL+ model representing the users’ perceptions by using a fuzzy linguistic modeling and taking into account that users’ opinions on the library services are not equally important.

A Hesitant Fuzzy Linguistic Multicriteria Decision-Making Method with Interactive Criteria and Its Application to Renewable Energy Projects Selection

A variety of multicriteria decision-making (MCDM) methods for renewable energy projects evaluation have been proposed, of which the premise of using these methods is to assume that the criteria are independent of each other. However, it may be difficult or costly to build independent criteria set in some cases because renewable energy planning is to pursue a balance of economic, social, and environmental goals, which makes the existence of interaction among criteria be of great possibility. In this paper, we consider a highly ambiguous decision situation, where the experts are allowed to give the evaluations in the form of hesitant fuzzy linguistic terms set (HFLTS). We build a hesitant fuzzy linguistic decision-making model handling the interaction among criteria from the perspective of distance measure and apply it to renewable energy projects selection. The proposed method can consider more fuzzy factors and deal with the interaction among criteria more approximately. It can reduce the decision pressure and improve the decision-making efficiency because the decision makers are allowed to express their preference in form of HFLTS and a decision criteria set of which the criteria are independent of each other is not necessary.

Fuzzy Linguistic Recommender Systems for the Selective Diffusion of Information in Digital Libraries

The significant advances in information and communication technologies are changing the process of how information is accessed. The internet is a very important source of information and it influences the development of other media. Furthermore, the growth of digital content is a big problem for academic digital libraries, so that similar tools can be applied in this scope to provide users with access to the information. Given the importance of this, we have reviewed and analyzed several proposals that improve the processes of disseminating information in these university digital libraries and that promote access to information of interest. These proposals manage to adapt a user`s access to information according to his or her needs and preferences. As seen in the literature one of the techniques with the best results, is the application of recommender systems. These are tools whose objective is to evaluate and filter the vast amount of digital information that is accessible online in order to help users in their processes of accessing information. In particular, we are focused on the analysis of the fuzzy linguistic recommender systems (i.e., recommender systems that use fuzzy linguistic modeling tools to manage the user`s preferences and the uncertainty of the system in a qualitative way). Thus, in this work, we analyzed some proposals based on fuzzy linguistic recommender systems to help researchers, students, and teachers access resources of interest and thus, improve and complement the services provided by academic digital libraries.

Design of prisoner's dilemma-based fuzzy C-means computed torque controller for PUMA-560 robot manipulator

Robots are highly nonlinear and chaotic in position control. The present paper mainly presents the position control of PUMA-560 Robot manipulator. Computed torque controller (CTC) is one of the solutions for position control of robot manipulators. The main drawback of controller is that it fails to operate under different dynamic operating conditions. To overcome this difficulty, intelligent controllers have gained importance. In this paper a novel approach, design of prisoner's dilemma-based fuzzy C-means controller to control the position of robot manipulator is presented. This controller is employed at the inputs of computed torques for obtaining the desired position. Fuzzy C-means controller with computed torques is realised by validating the clusters to choose most contributed rules. Thus the unfired rules are eliminated from the actual rule-base. Hence, a compact fuzzy controller with minimum rule-base, fuzzy C-means computed torque controller (FCMCTC), is designed. The concept of prisoner's dilemma is introduced in this paper to improve the fuzzy strategy. The interrelations between inputs and outputs of a fuzzy linguistic model are assigned using payoff matrix through prisoner's dilemma. Simulation results prove the efficacy of proposed controller when compared to proportional derivative computed torque controller (PD-CTC), normal FLC and that of the reference signal.

Design of prisoner's dilemma-based fuzzy C-means computed torque controller for PUMA-560 robot manipulator

Robots are highly nonlinear and chaotic in position control. The present paper mainly presents the position control of PUMA-560 Robot manipulator. Computed torque controller (CTC) is one of the solutions for position control of robot manipulators. The main drawback of controller is that it fails to operate under different dynamic operating conditions. To overcome this difficulty, intelligent controllers have gained importance. In this paper a novel approach, design of prisoner's dilemma-based fuzzy C-means controller to control the position of robot manipulator is presented. This controller is employed at the inputs of computed torques for obtaining the desired position. Fuzzy C-means controller with computed torques is realised by validating the clusters to choose most contributed rules. Thus the unfired rules are eliminated from the actual rule-base. Hence, a compact fuzzy controller with minimum rule-base, fuzzy C-means computed torque controller (FCMCTC), is designed. The concept of prisoner's dilemma is introduced in this paper to improve the fuzzy strategy. The interrelations between inputs and outputs of a fuzzy linguistic model are assigned using payoff matrix through prisoner's dilemma. Simulation results prove the efficacy of proposed controller when compared to proportional derivative computed torque controller (PD-CTC), normal FLC and that of the reference signal.

Linguistic fuzzy consensus model for collaborative development of fuzzy cognitive maps: a case study in software development risks

Fuzzy cognitive maps (FCMs) have been widely used in several domains for information processing, such as, data fusion, decision making. Although several methods to automatically learn FCMs are recognized from the scientific literature, the most used approach to build an FCM relies on a collaborative task involving single person or, more suitably, group of experts. Collaborative development increases reliability and robustness of the resulting FCM, but rises some problems in terms of group decision making to aggregate different perspectives of the problem representation. This paper proposes to support collaborative development of FCMs introducing knowledge engineering process that relies on Linguistic Fuzzy Consensus Model. In the proposed approach, each expert builds the own version of the FCM. When all different versions are available, a Group Decision Making process is activated in order to reach the consensus on conflictual modeling opinions. The result is a unique final version of the FCM that is not a simple aggregation of the versions provided by the experts but is the result of a well-suited mathematical model. In addition, this work adopts consensus model with incomplete preference relations scheme to address knowledge harmonization issues. Finally, advantages and the limitations of the proposed framework are argued.

A proportional linguistic distribution based model for multiple attribute decision making under linguistic uncertainty

This paper aims at developing a proportional fuzzy linguistic distribution model for multiple attribute decision making problems, which is based on the nature of symbolic linguistic model combined with distributed assessments. Particularly, in this model the evaluation on attributes of alternatives is represented by distributions on the linguistic term set used as an instrument for assessment. In addition, this new model is also able to deal with incomplete linguistic assessments so that it allows evaluators to avoid the dilemma of having to supply complete assessments when not available. As for aggregation and ranking problems of proportional fuzzy linguistic distributions, the extension of conventional aggregation operators as well as the expected utility in this proportional fuzzy linguistic distribution model are also examined. Finally, the proposed model will be illustrated with an application in product evaluation.

Integrating Soft Set Theory and Fuzzy Linguistic Model to Evaluate the Performance of Training Simulation Systems.

The advancement of high technologies and the arrival of the information age have caused changes to the modern warfare. The military forces of many countries have replaced partially real training drills with training simulation systems to achieve combat readiness. However, considerable types of training simulation systems are used in military settings. In addition, differences in system set up time, functions, the environment, and the competency of system operators, as well as incomplete information have made it difficult to evaluate the performance of training simulation systems. To address the aforementioned problems, this study integrated analytic hierarchy process, soft set theory, and the fuzzy linguistic representation model to evaluate the performance of various training simulation systems. Furthermore, importance–performance analysis was adopted to examine the influence of saving costs and training safety of training simulation systems. The findings of this study are expected to facilitate applying military training simulation systems, avoiding wasting of resources (e.g., low utility and idle time), and providing data for subsequent applications and analysis. To verify the method proposed in this study, the numerical examples of the performance evaluation of training simulation systems were adopted and compared with the numerical results of an AHP and a novel AHP-based ranking technique. The results verified that not only could expert-provided questionnaire information be fully considered to lower the repetition rate of performance ranking, but a two-dimensional graph could also be used to help administrators allocate limited resources, thereby enhancing the investment benefits and training effectiveness of a training simulation system.

A hybrid advertising media selection model using AHP and fuzzy-based GA decision making

Advertising media selection is the process of analyzing and choosing the most cost-effective media for advertising and promotions campaign. It is generally challenging because of the multiplicity of alternatives, the variations in objectives, and the budgetary limits of organizations. Hence, developing a media selection within the framework of a decision-making procedure is a vital function of advertising. Most of the traditional media selection procedures could be simply placed in two categories: (1) models based on judgment of decision makers who have not enough ability to consider the very large number of media combinations; (2) or quantitative models which could not incorporate imprecise, uncertain and subjective criteria. In this paper, a two-phase methodology is developed for advertising media selection in which by integrating qualitative and quantitative models, human-based information is incorporated in the decision process, while the complexity associated with the media selection decision is responded. In the first phase, the top media for an advertising campaign is identified by applying analytic hierarchy process based on four criteria of AIDA’s hierarchy of effects model (AIDA stands for: A = Attention, I = Interest, D = Desire, A = Action), and then in the second phase, by integrating a fuzzy linguistic decision model with a genetic algorithm searching process, the optimum media mix of the top media is extracted under satisfying expected advertising objectives and budgetary limitations. In the end, the proposed methodology is implemented empirically in a real-world case with satisfactory results.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A new hybrid decision making system for supplier selection

The objective of this paper is to develop a hybrid decision making system using Data Envelopment Analysis (DEA) and linguistic fuzzy models for selecting the best supplier in the presence of multiple decision makers. In this hybrid system, first the weights of selected criteria are obtained from each of the decision makers as linguistic fuzzy numbers within the framework of group decision making. Then, to select the best supplier, absolute weight restriction (AWR) model is incorporated into the DEA model. A real case study demonstrates the application of the model.

A novel semi-quantitative Fuzzy Cognitive Map model for complex systems for addressing challenging participatory real life problems

Fuzzy Cognitive Maps (FCM) are a promising approach for socio-ecological systems modelling. FCMs represent problem knowledge extracted from different stakeholders in the form of connected factors/variables with imprecise cause-effect relationships and many feedback loops. These typically large maps are condensed and aggregated to obtain a summary view of the system. However, representation, condensation and aggregation of previous FCM models are qualitative due to lack of appropriate quantitative methods. This study tackles these drawbacks by developing a semi-quantitative FCM model consisting of robust methods for adequately and accurately representing and manipulating imprecise data describing a complex problem involving stakeholders for pragmatic decision making. The model starts with collecting qualitative imprecise data from relevant stakeholders. These data are then transformed into stakeholder perceptions/FCMs with different causal relationship formats (linguistic or numeric) which the proposed model then represents in a unified format using a 2-tuple fuzzy linguistic representation model which allows combining imprecise linguistic and numeric values with different granularity and/or semantic without loss of information. The proposed model then condenses large FCMs using a semi-quantitative method that allows multi-level condensation. In each level of condensation, groups of similar variables are subjectively condensed and the corresponding imprecise connections are computationally condensed using robust calculations involving credibility weights assigned to variables (variables’ importance). The model then uses a quantitative fuzzy method to aggregate perceptions/FCMs into a stakeholder group or social perception/FCM based on the 2-tuple model and credibility weights assigned to FCMs (stakeholders’ importance). Thereafter, the structure of produced FCMs is analysed using graph theory indices to examine differences in perceptions between stakeholders or groups. Finally, the model applies various what-if policy scenario simulations on group FCMs using a dynamical systems approach with neural networks and analyses scenario outcomes to provide appropriate recommendations to decision makers. An example application illustrates method’s effectiveness and usefulness.

Design of Prisoner’s dilemma based fuzzy logic computed torque controller with Lyapunov synthesis linguistic model for PUMA-560 robot manipulator

The robot manipulators are highly nonlinear, time varying and one of the important challenges in the field of robotics is the effective control of manipulators. This paper presents a technique to extract the rules of Fuzzy Logic Computed Torque Controller for PUMA-560 robot arm with uncertainties. Fuzzy Logic Controllers are placed at the input of the PD Controller to make the gains adaptive. Prisoner's dilemma is employed to systematically tune the gains of the controller. The interrelations between inputs and outputs of a Fuzzy Linguistic Model are assigned using payoff matrix through Prisoner's Dilemma. The difficulty in designing of fuzzy controllers is the extraction of the rule base. The extraction of fuzzy control rules requires good understanding of the plant and control theory. The present paper utilizes Fuzzy Lyapunov Synthesis to constitute the rule base assuming that minimal knowledge about the plant to be controlled. Simulation results prove the effective performance of the proposed controller in minimizing the error in joint angles when compared to Proportional Derivative Computed Torque Controller (PD-CTC), normal Fuzzy Logic Controller (FLC) and that of the reference signal.

Improving product based on affordance with fuzzy theory for product development strategy

This paper presents a design strategy which combines both conceptual and mental design information to quantitative degrees of affordance using a fuzzy scale that provides user-to-designer information. The traditional communication channel has been hidden. In the past, design concepts were not a result of direct communication between designers and users. This study found that, based on users’ mental models, users can reflectively modify product characteristics to improve the convenience of usage. These insights offer a hidden communication channel for designers and are defined as generalised affordance within the context of this research. This study proposed a novel method to semi-quantify affordance using a fuzzy linguistic modelling technique to establish a fuzzy scale table that quantifies degrees of affordance. An affordance fuzzy matrix (AFM) was created to assess the effects of affordance of each task necessary to utilise each product component. The overall results of the AFM indicate the generalised affordance of a particular product. Hidden messages are the characteristic of a product improved by the user itself because of his habit, which incurs additional product features, symbols and words, and will help product designers for further design strategy and improvements. In this study, generalised affordance is applied to an electric oven as an example.