Intuitionistic Fuzzy Reasoning Research Articles

An intuitionistic fuzzy probabilistic Petri net method for risk assessment on submarine pipeline leakage failure

Under the influence of various factors, the submarine pipelines will be damaged, resulting in oil and gas leakage accidents. It is essential to perform risk assessments to prevent accidents. However, accurate and sufficient historical data usually can not be obtained. To solve this problem, this study proposes a probabilistic Petri net (PPN) method based on intuitionistic fuzzy evidential reasoning (IFER), which can determine the failure probability of basic events through domain expert experience, and quantitatively evaluate the probability of submarine pipeline leakage failure. The main contributes include: according to DNV-RP-F107, a group of intuitionistic fuzzy numbers is defined and an improved probability conversion method is proposed for defuzzification; an IFER approach is proposed, which uses belief structures as expert linguistic terms and transforms them into IFNs to deal with the uncertainty of expert evaluation; a probabilistic Petri net method is proposed to quantitatively calculate the failure probability of top event and identify the critical events. Finally, the proposed intuitionistic fuzzy probabilistic Petri net (IFPPN) method is illustrated by a case study. By comparing with the existing methods, the feasibility of this method is validated, and the evaluation results can provide a reference for the safe operation of the pipeline system.

Time-Varying Intuitionistic Fuzzy Integral for Emergency Materials Demand Prediction With Case-Based Reasoning

Recently, frequent emergencies seriously impact people’s life and production. The timely emergency material can save more people. Hence, emergency material demand prediction is a core of emergency rescue. However, emergency is a continuous, uncertain evolution process. The emergency of different periods has various situations. The time-varying and uncertain characteristics of emergency have made this representation and prediction work much more complex. In order to address this concern, this article proposes a time-varying intuitionistic fuzzy set (TVIFS) to precisely depict the time-varying scenario and the uncertainty of emergencies. For utilizing the TVIFS of different time frames, we deeply explore the integral aggregation operator of TVIFS, which can take full account of decision hesitation and possible information. Due to the different time importance of different time frames, we investigate the integral of TVIFS with constant period weight in advance. Considering the time-varying period weight, the TVIFS integral with time-varying period weight is investigated. For the sake of calculation feasibility, inspired by the relationship between continuity and dispersion, we translate the TVIFS integral with variable period weight into the TVIFS integral with constant period weight. Furthermore, we develop time-varying intuitionistic fuzzy case-based reasoning (CBR) model to forecast the emergency materials demand by designing hybrid similarity between cases. Finally, we elaborate the application of time-varying intuitionistic fuzzy CBR model by using an example of forest fire and verify the validity of our proposed method.

The Relationship between Fuzzy Reasoning Methods Based on Intuitionistic Fuzzy Sets and Interval-Valued Fuzzy Sets

Two important basic inference models of fuzzy reasoning are Fuzzy Modus Ponens (FMP) and Fuzzy Modus Tollens (FMT). In order to solve FMP and FMT problems, the full implication triple I algorithm, the reverse triple I algorithm and the Subsethood Inference Subsethood (SIS for short) algorithm are proposed, respectively. Furthermore, the existing reasoning algorithms are extended to intuitionistic fuzzy sets and interval-valued fuzzy sets according to different needs. The purpose of this paper is to study the relationship between intuitionistic fuzzy reasoning algorithms and interval-valued fuzzy reasoning algorithms. It is proven that there is a bijection between the solutions of intuitionistic fuzzy triple I algorithm and the interval-valued fuzzy triple I algorithm. Then, there is a bijection between the solutions of intuitionistic fuzzy reverse triple I algorithm and the interval-valued fuzzy reverse triple I algorithm. At the same time, it is shown that there is also a bijection between the solutions of intuitionistic fuzzy SIS algorithm and interval-valued fuzzy SIS algorithm.

Robustness of Interval-Valued Intuitionistic Fuzzy Reasoning Quintuple Implication Method

The interval-valued intuitionistic fuzzy quintuple implication algorithm, as an extension of the fuzzy reasoning algorithm, may better characterize and deal with uncertainty in the reasoning, but how to select distance measure and analyze the algorithm’s robustness is an important and unsolved topic. In this paper, a novel distance measure of interval-valued intuitionistic fuzzy sets is constructed based on interval-valued intuitionistic fuzzy biresiduum similarity. The unified form of the conclusion about the robustness of interval-valued intuitionistic fuzzy reasoning quintuple implication algorithm for interval-valued intuitionistic fuzzy modus ponens(IVIFMP) and interval-valued intuitionistic fuzzy modus tollens(IVIFMT) is obtained. Especially, the robustness of the interval-valued intuitionistic fuzzy reasoning quintuple implication algorithm based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$G\ddot {o}del$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Lukasiewicz$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Goguen$ </tex-math></inline-formula> implication operators is presented. An application example and experiment are offered to demonstrate the validity of the obtained conclusion. Furthermore, the new distance metric is compared to traditional distances, and its benefits and limits are discussed. The results show that our approach to research the robustness is simpler and more representative, and the robustness of the algorithm based on other implication operators can be obtained by simple substitution.

Sentiment Analysis of News Comments Based on Improved Intuitionistic Fuzzy Reasoning

With the development of the Internet, public opinion monitoring emphasizes the need to obtain a more accurate mood prediction model, so as to grasp the public opinion tendency and thinking process more accurately. In addition, the need to process real-world datasets of sentiment analysis without using sampling techniques can introduce noise into the data being used. In this paper, we propose an extraction method based on compact interval valued intuitionistic fuzzy rules for the classification of news comment pages based on IVFS, for modeling and predicting real-world Sentiment Analysis applications. The web pages are partitioned based on DOM tree, and the emotional tendency of news comments is classified by the improved intuitionistic fuzzy reasoning algorithm. Finally, this method is used to extract news comment web pages. To test the quality of our recommendations, we will present an experimental study that includes a dataset of 9 news pages with comments. We will demonstrate that this method is superior to the original C4.5 decision tree, Type 1 and Region 1, which are preprocessed.5. Decision tree. Type 1 corresponds to the interval value fuzzy and approximates the original fuzzy classifier. Our approach avoids a lot of unnecessary trouble and provides interpretable models that allow for more accurate results.

Intuitionistic fuzzy Logic System and its Application to Global Carbon Dioxide Emissions Prediction

In this study, a Takagi-Sugeno-Kang based intuitionistic fuzzy logic system is proposed for the prediction of global carbon dioxide emission for the first time. The intuitionistic fuzzy logic system is an integration of artificial neural network learning and intuitionistic fuzzy logic reasoning. The gradient descent back propagation is applied in the optimization of the parameters of the proposed model. The model is evaluated based on some performance metrics. Results of evaluation revealed that the intuitionistic fuzzy logic system outperforms other existing models in the literature in terms of prediction accuracy

Relief demand forecasting based on intuitionistic fuzzy case-based reasoning

Prediction for demand of relief materials is a fundamental condition of disaster relief operations, and the premise for optimal allocation of emergency resources. There are currently few research papers about demand forecasting at home and abroad. Looking at the characteristics of relief supply demand prediction with incomplete and inaccurate available information, and uncertainty of environment, we propose a demand prediction method called intuitionistic fuzzy case-based reasoning (IFCBR). This method combines the advantages of intuitionistic fuzzy theory and case-based reasoning (CBR). Also proposed in this paper are similarity calculation methods and a new weight calculation method. A case study is addressed to illustrate the prediction process of relief demand using the proposed method. Finally, the validity of the method is verified by an empirical evaluation experiment in which actual earthquake disaster cases are introduced. This forecasting method provides decision support for relief material requirements, and provides a basis for resource allocation.

Improvement of Collaborative Filtering Recommendation Algorithm Based on Intuitionistic Fuzzy Reasoning Under Missing Data

Commodity recommendation plays an essential role in the marketing field in the Internet era, and collaborative filtering, as a powerful technique of commodity recommendation, has been widely concerned in both academic studies and practical applications. Existing research on collaborative filtering often uses methods such as genetic algorithm and neural network to solve the sparsity and cold start problems while ignoring the fuzziness of users' ratings on goods or services. To solve the problems, we propose a recommendation algorithm (IFR-CF) based on intuitionistic fuzzy reasoning and collaborative filtering. In this algorithm, the characteristic coefficient in intuitionistic fuzzy reasoning is used to replace the traditional similarity coefficient to determine neighbor set, and the finite prior ordering method is used to replace traditional algorithm to recommend commodity. Two groups of data are extracted from Movielens and Jester datasets for experiments, and the MAE value generated by the recommended items is taken as the metric to verify the algorithm performance. Experimental results show that compared with the traditional algorithms, our algorithm achieves lower MAE value and higher recommendation accuracy. Meanwhile, the intuitionistic index of fuzzy set is taken into account in the calculation of the hesitation coefficient, which provides a novel solution to the problem of missing scoring data of users.

Approximate Reasoning Based on IFRS and DS Theory With its Application in Threat Assessment

Threat assessment for aerial attack targets is an important aspect of air defense weapon systems in responding to multiple attacks. We establish a model based on intuitionistic fuzzy rough sets (IFRS) and D-S evidence theory for threat assessment from the required data with uncertainty in stages. Using the overall degree of dependency and attribute importance of the intuitionistic fuzzy information system as a heuristic function, we study algorithms to extract threat elements and rules based on IFRS, to generate an intuitionistic fuzzy rule base for threat assessment with degrees of belief and disbelief. Based on the threat assessment rule base, we study the BPA determination algorithm in multi-stage threat assessment. The intuitionistic fuzzy semantics of degree of belief in the rule conclusion are used to determine the focal elements corresponding to each aggregate rule, and to obtain the degree of support of the data in a stage for each threat level. A case study shows that, compared to a threat assessment method based solely on D-S evidence theory or intuitionistic fuzzy reasoning, the advantage of IFRS knowledge acquisition makes the selection of threat assessment elements and determination of BPA more objective and less dependent on domain experts, so as to yield strong, objective results.

Intuitionistic Fuzzy Dynamic Bayesian Network and its Application to Terminating Situation Assessment

The period of terminating situation in air combat is time-varying and full of information with uncertainty. In order to process the fuzzy information in time series and assess the situation effectively, Intuitionistic Fuzzy Dynamic Bayesian Network (IFDBN) is built. In this model, intuitionistic fuzzy reasoning engine is embedded into dynamic Bayesian Network as a virtual node. Besides, a new method to convert the intuitionistic fuzzy reasoning output into probability which can be input into dynamic Bayesian Network as virtual evidence is proposed. On these bases, the detailed intuitionistic fuzzy reasoning algorithms are formulated and the dynamic Bayesian Network information transferring mode is analyzed. Finally, a case study for terminating situation assessment in air combat is given to prove the feasibility of the proposed model.

PROPERTY ANALYSIS OF TRIPLE IMPLICATION METHOD FOR APPROXIMATE REASONING ON ATANASSOVS INTUITIONISTIC FUZZY SETS

Firstly, two kinds of natural distances between intuitionistic fuzzy sets are generated by the classical natural distance between fuzzy sets under a unified framework of residual intuitionistic implication operators. Secondly, the continuity and approximation property of a method for solving intuitionistic fuzzy reasoning are defined. It is proved that the triple implication method for intuitionistic fuzzy modus ponens has both continuity and approximation property with respect to these two kinds of natural distances based on {L}ukasiewicz implication, meanwhile the triple implication method for intuitionistic fuzzy modus tollens possesses unconditional continuity and conditional approximation property. Finally, some robustness results about the triple implication method of intuitionistic fuzzy reasoning are given.

Intuitionistic fuzzy reasoning using the method of optimizing the similarity of truth tables

In this paper, we extend the method of optimizing the similarity (OS method for short) of truth tables used in fuzzy reasoning, to intuitionistic fuzzy reasoning. In this regard, we introduce intuitionistic fuzzy implicators and some intuitionistic similarity measures. We explain the OS method for intuitionistic fuzzy modus ponens and intuitionistic fuzzy modus tollens. Then, we define continuous intuitionistic fuzzy implicators and give some weaker conditions under which the optimization problem has a solution. The reductive property and optimality condition for this method are investigated.

Unified forms of the CDR method of approximate reasoning on Antanassov's intuitionistic fuzzy sets and its property analysis

AbstractTwo basic models of fuzzy reasoning are fuzzy modus ponens and fuzzy modus tollens. Correspondingly, the key point of intuitionistic fuzzy reasoning is to solve the problems of intuitionistic fuzzy modus ponens (IFMP) and intuitionistic fuzzy modus tollens (IFMT). In many important algorithms of fuzzy reasoning, the Consequent Dilation Rule (CDR) method possesses the virtue of unconditional reductivity. This paper proposed an intuitionistic CDR (ICDR) method for IFMP and IFMT problems by extending the CDR method to the intuitionistic fuzzy reasoning. It was proved that the intuitionistic CDR methods, both for IFMP and IFMT, are unconditionally reductive in the circumstance of using residual intuitionistic implication operators. At the same time, the continuity, approximation properties and robustness of the CDR method in Łukasiewicz intuitionistic fuzzy reasoning space have been studied by using the new defined average natural distance between intuitionistic fuzzy sets.

Multi-attribute group decision making approach based on interval-valued intuitionistic fuzzy sets and evidential reasoning methodology

In multi-attribute group decision making methods, team often needs to deal with both quantitative data and qualitative information with uncertainty. It is essential to properly represent and use uncertain information to conduct rational decision analysis. Having regarded this fact many approaches have been proposed for solving group decision-making problems, especially in fuzzy environments, but due to their drawbacks they get an unreasonable preference order of the alternatives in some situation. Thus in this paper based on interval-valued intuitionistic fuzzy sets and evidential reasoning methodology, a new approach has been proposed for supporting such decision situation. The experimental results are examined using the proposed approach. Computation steps and analysis results are provided to demonstrate its implementation process. The proposed method can overcome the drawbacks of the existing methods for fuzzy multi-attribute group decision making in intuitionistic fuzzy environments.

Intuitionistic Fuzzy Time Series Forecasting Model Based on Intuitionistic Fuzzy Reasoning

Fuzzy sets theory cannot describe the data comprehensively, which has greatly limited the objectivity of fuzzy time series in uncertain data forecasting. In this regard, an intuitionistic fuzzy time series forecasting model is built. In the new model, a fuzzy clustering algorithm is used to divide the universe of discourse into unequal intervals, and a more objective technique for ascertaining the membership function and nonmembership function of the intuitionistic fuzzy set is proposed. On these bases, forecast rules based on intuitionistic fuzzy approximate reasoning are established. At last, contrast experiments on the enrollments of the University of Alabama and the Taiwan Stock Exchange Capitalization Weighted Stock Index are carried out. The results show that the new model has a clear advantage of improving the forecast accuracy.

Fuzzy Petri nets Using Intuitionistic Fuzzy Sets and Ordered Weighted Averaging Operators.

Fuzzy Petri nets (FPNs) are an important modeling tool for knowledge representation and reasoning, which have been extensively used in a lot of fields. However, the conventional FPN models have been criticized as having many shortcomings in the literature. Many different models have been suggested to enhance the performance of FPNs, but deficiencies still exist in these models. First, various types of uncertain knowledge information provided by domain experts are very hard to be modeled by the existing FPN models. Second, the traditional FPNs determine the results of knowledge reasoning using the min, max, and product operators, which may not work well in many practical applications. In this paper, we propose a new type of FPN model based on intuitionistic fuzzy sets and ordered weighted averaging operators to deal with the problems and improve the effectiveness of the conventional FPNs. Moreover, a max-algebra-based reasoning algorithm is developed in order to implement the intuitionistic fuzzy reasoning formally and automatically. Finally, a case study concerning fault diagnosis of aircraft generator is presented to demonstrate the proposed intuitionistic FPN model. Numerical experiments show that the new FPN model is feasible and quite effective for knowledge representation and reasoning of intuitionistic fuzzy expert systems.

The Evidential Reasoning Approach to Medical Diagnosis using Intuitionistic Fuzzy Dempster-Shafer Theory

AbstractFor medical diagnosis, fuzzy Dempster-Shafer theory is extended to model domain knowledge under probabilistic and fuzzy uncertainty. However, there are some information loss using discrete fuzzy sets and traditional matching degree method. This study aims to provide a new evidential structure to reduce information loss. This paper proposes a new intuitionistic fuzzy evidential reasoning (IFER) approach which combines intuitionistic trapezoidal fuzzy numbers and inclusion measure to improve the accuracy of representation and reasoning. The proposed approach has been validated by a stroke diagnosis. It is shown that the IFER approach leads to more accurate results.

Triple I method of approximate reasoning on Atanassov's intuitionistic fuzzy sets

Two basic inference models of fuzzy reasoning are fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT). The Triple I method is a very important method to solve the problems of FMP and FMT. The aim of this paper is to extend the Triple I method of approximate reasoning on Atanassov's intuitionistic fuzzy sets. In the paper, we first investigate the algebra operators' properties on the lattice structure of intuitionistic fuzzy information and provide the unified form of residual implications which indicates the relationship between intuitionistic fuzzy implications and fuzzy implications. Then we present the intuitionistic fuzzy reasoning version of the Triple I principles based on the models of intuitionistic fuzzy modus ponens (IFMP) and intuitionistic fuzzy modus tollens (IFMT) and give the Triple I method of intuitionistic fuzzy reasoning for residual implications. Moreover, we discuss the reductivity of the Triple I methods for IFMP and IFMT. Finally, we propose α-Triple I method of intuitionistic fuzzy reasoning.

Sentiment Orientation Classification of Webpage Online Commentary Based on Intuitionistic Fuzzy Reasoning

An approach of sentiment classification for online comments based on intuitionistic fuzzy reasoning is presented on the basis of the analysis of characteristics of sentiment classification. The approach employs membership function, non-membership function and hesitant function to depict uncertainties of features, quantitatively, by sample training, as well as sentiment expressions influenced by adverbs of degree, conjunctions and negative words are considered. Then the semantic orientation of a text is synthesized on the level of phrases, sentences and texts in sequence by means of aggregations of intuitionistic fuzzy information of features. The presented approach obtains high precision and recall when test using public corpus

剩余型直觉模糊推理的三I方法

This paper investigates the properties of the intuitionistic triangular norm induced by the left-continuous t-norm, and presents the equivalent characterization of intuitionistic residual implicator adjoint in-tuitionistic triangular norms, which clari es the relationship between intuitionistic fuzzy implications and fuzzy implications. According to the triple I method of fuzzy reasoning, this paper proposes the triple I and decomposi-tion methods for solving the IFMP problem of intuitionistic fuzzy reasoning for a residual intuitionistic implicator, and proposes two types of solving formulas. Moreover, this paper discusses the reductivity of the methods and shows that the triple I and decomposition methods for the IFMP problem possess the virtue of reductivity. Finally, the paper proposes the α-triple I method for the IFMP problem and provides a corresponding example.