Fuzzy Language Research Articles

Fuzzy simulations and bisimulations between fuzzy automata

Simulations and bisimulations between two fuzzy automata over a complete residuated lattice were defined by Ćirić et al. (2012) [7,8] as fuzzy relations between the sets of states of the automata. However, they act as a crisp relationship between the automata. In particular, if there exists a (forward) bisimulation between two fuzzy automata, then the fuzzy languages recognized by them are crisply equal. Approximate simulations and bisimulations introduced by Stanimirović et al. (2022) [36] aim at fuzzifying this phenomenon. However, they are defined only for fuzzy automata over a complete Heyting algebra and do not give the exact relationship between states of the automata. In this article, we introduce and study fuzzy simulations and bisimulations between fuzzy automata over a complete residuated lattice. These notions are novel and have good properties. They are defined for fuzzy automata over any complete residuated lattice. We prove that the fuzzy language recognized by a fuzzy automaton is fuzzily preserved by fuzzy simulations and fuzzily invariant under fuzzy bisimulations. We also prove that the notions of fuzzy simulation and bisimulation have the Hennessy-Milner properties, which are a logical characterization of the greatest fuzzy simulation or bisimulation between two fuzzy automata. In addition, we provide results showing that our notions of fuzzy simulation and bisimulation are more general and refined than the notions of simulation and bisimulation introduced by Ćirić et al. and the notions of approximate simulation and bisimulation introduced by Stanimirović et al.

Personalized Recommender System for Children's Book Recommendation with A Realtime Interactive Robot

In this paper, we study the personalized book recommender system in a child–robot interactive environment. Firstly, we propose a novel text search algorithm using an inverse filtering mechanism that improves the efficiency. Secondly, we propose a user interest prediction method based on the Bayesian network and a novel feedback mechanism. According to children’s fuzzy language input, the proposed method gives the predicted interests. Thirdly, the domain-specific synonym association is proposed based on word vectorization, in order to improve the understanding of user intention. Experimental results show that the proposed recommender system has an improved performance, and it can operate on embedded consumer devices with limited computational resources.

Human reliability assessment on building construction work at height: The case of scaffolding work

The hybrid approach of CREAM (cognitive reliability and error analysis method), fuzzy theory and Bayesian network (BN) was constructed to analyze the reliability of scaffolding operators in this work. A construction site in Fuzhou City (Fujian Province, China) was targeted as an example. Firstly, seven operational and five cognitive subtasks were developed by the characteristics of scaffolding operations. Then, the relational network of the common performance conditions (CPCs) was determined by a BN. Expert evaluation can obtain a priori probabilities. Fuzzy languages were transformed into conditional probabilities by fuzzy theory. The probability distribution of the control model can be determined based on the relational network. Secondly, the human error probabilities (HEPs) of the operational subtasks were calculated based on the center of the area (COA). Based on the influence of the operational subtask on the cognitive subtask, the HEPs of the cognitive subtasks were calculated with BNs. Finally, the total HEP of the scaffolding operation was identified using a series–parallel hybrid structure. The results show that the total HEP of the scaffolding operation at this construction site is 4.30E-03. The scaffolding dismantling and supervisory inspection have a significant impact on human error. Also, site management should strengthen training and education on work methods. This method can provide a new idea for human reliability analysis, as well as a new perspective for the safety of work at height.

Speed control method of mineral lifting and transportation machinery based on single neuron PID

The current speed control methods of mineral hoisting and conveying machinery ignore the fuzzy steps of the PID control model, and the AC commutation effect of mineral hoisting and conveying machinery is poor, resulting in long delay and low accuracy of speed control. To this end, a speed control method for lifting and transporting machinery based on single neuron PID is proposed. The structure of a single neural adaptive PID speed controller is designed. A learning algorithm is introduced to optimize the AC commutation effect of the hoisting and conveying mechanical circuit. A single neuron PID controller is used to fuzzy process the input signal to form a fuzzy language set. The speed controller port is defined, and the single neuron PID control rule is designed. Using the reduction ratio of the three-phase motor speed governor, the maximum speed required for conveying minerals is calculated, and the speed control of the mineral hoisting and transporting machinery is realized. The experimental results show that the mineral lifting deviation is small, the speed regulation time is less than 4 s, the average control delay is 200 ms, and the maximum speed regulation accuracy is 99.9%. The method in this paper has certain application value and is worthy of promotion.

Digital transformations in drive control technology for exploration and mining equipment

Background. Technologies applied in geological exploration and mining are undergoing rapid transformations. The scientific progress in this field, which is shifting towards Asia, is characterized by the integration of network and production technologies. The modern society can be conventionally divided into the creative part, i.e. people designing digital technologies, and those operating these technologies. This has become particularly evident with the advancement of the 4th generation of automatics. The period of massive digital transformation started in 2008–2009 with the appearance of the Internet of Things (IoT). This process acquired the term Industry 4.0 or Industrial Internet of Things (IIoT).Aim. To study current digital transformations in drive control technology applied in exploration and mining equipment.Materials and methods. Methods and examples of controlling exploration and mining equipment are presented.Results. The main trends in the development of drive control technology applied in exploration and mining equipment were examined.Conclusion. Modern digital control systems are capable of re-programming controllers using an active or passive identification of multicomponent multiloop controlled objects in real time. The new Flight Contol Language (FCL) designed for programmable controllers incorporates the methods of fuzzy logic [5,8]. This fuzzy control language uses a new type of data — a linguistic variable, which is a numerical variable with a name, e.g. pressure, rotation speed, temperature, etc., involved in linguistic descriptions.

The Characteristics and Translation Strategies of Fuzzy Language in Diplomatic Communiqués

The Characteristics and Translation Strategies of Fuzzy Language in Diplomatic Communiqués

Research on rule extraction method based on concept lattice of intuitionistic fuzzy language

Abstract Studying the concept lattice of intuitionistic fuzzy language can not only enable an intuitive expression of the relationship between objects and attributes but also ensure the effectiveness of information, and it thus provides a new idea for the development of artificial intelligence. Therefore, in this paper, the rule extraction method is studied based on the concept lattice of intuitionistic fuzzy language. By combining the formal context of intuitionistic fuzzy language with the context of decision formal, the formal context of intuitionistic fuzzy language decision is put forward and the rule extraction method under this context is constructed. The problem of decision making in real life can be solved through the weighted similarity in the formal context of intuitionistic fuzzy language.

For Fuzzy Classification of Databases with Fuzzy Classification Query Language

Business information systems have extensive databases that are mainly managed in relational databases. What is often missing are automated procedures to analyze these inventories without major restructuring. Based on this, we develop the Fuzzy Classification Query Language, FCQL, which enables fuzzy queries to the extended database schema using linguistic variables and converts them into SQL statements to the database. With this, we give the user a data mining tool so that he can start extended queries on his databases based on a pre-defined fuzzy classification and obtain an improved basis for decision making. As a result, the fuzzy classification query language enables marketers to improve customer value, launch useful programs, automate overall customization, and refine business campaigns.

Research on Magnetron Fault Reasoning Based on Fault Tree Reasoning Model

Abstract Aiming at the characteristics of magnetron of airport surveillance radar, such as short operation time, complexity and nonlinear vibration signal, a fault reasoning method based on T-S fuzzy fault tree model was proposed to realize fault reasoning analysis of magnetron of radar receiving and receiving system. A T-S fuzzy fault tree fault inference model is constructed by using the field expert’s opinion expressed by fuzzy number language set. The experimental results show that compared with the traditional manual fault detection method, this method can make more comprehensive and efficient inference analysis for the magnetron fault events. This method provides a new idea for the application of magnetron fault reasoning analysis in engineering.

Comprehensive evaluation about the quality of steel strip based on combination weighting

Abstract In order to solve the problem that the quality of steel strip products is rough by manual evaluation, this paper will combine expert knowledge with quality data and establish four evaluation grades by taking surface quality, mechanical properties and dimensional accuracy as evaluation indicators. Analytic hierarchy process (AHP) and entropy weight method are used to confirm the weight of each evaluation indicator. The mechanical properties and dimensional accuracy indicators are confirmed by statistical method to find out the optimal range of indicator parameters, and the indicator deterioration degree is calculated. The deterioration degree of surface quality indicator is confirmed by fuzzy language quantization. The membership degree of each indicator parameter is calculated according to the membership degree function of ridge distribution, and then the fuzzy comprehensive evaluation result matrix of strip quality is obtained by synthesizing each indicator parameter step by step. Finally, the comprehensive score of strip steel quality is obtained by quantizing and summing the evaluation result matrix. The experimental results show that the evaluation result of the model is consistent with the actual data, so it has certain practicability.

Translation Strategies of Fuzzy Language—Taking Red Sorghum and Its Translation as Examples

Translation Strategies of Fuzzy Language—Taking Red Sorghum and Its Translation as Examples

Quality characteristic extraction for complex products with multi-granular fuzzy language based on the triple bottom lines of sustainability

Advanced product optimization design methods can greatly improve the market competitiveness of enterprises in manufacturing industry and promote the sustainable development of economy, society and environment. Quality characteristic is the carrier of product quality and closely related to sustainable product design. To manage reasonably the environmental, economic and social goals in actual product design, this paper proposes a quality characteristic extraction method for complex products with multi-granular fuzzy language based on the triple bottom lines of sustainability. Satisfaction evaluation of a customer group expressed in multi-granular fuzzy language is given full consideration with the adoption of dominant granular structures. Interval rough number (IRN) is employed for product quality function development (QFD) to fully retain the calculation simplicity of rough sets and expression flexibility of interval numbers in the correlation transformation between customer requirements and quality characteristics. A multi-objective optimization model for the quality characteristic extraction of complex products is established according to the triple bottom lines of sustainability, and an improved fruit fly optimization algorithm with the segmented variable step size is designed to solve it efficiently. Lastly, the rationality and effectiveness of the proposed method are demonstrated by the case study of a computer numerical control (CNC) machine tool.

Interval‐valued Pythagorean fuzzy linguistic KPCA model based on TOPSIS and its application for emergency group decision making

This paper investigates the emergency group decision-making problem based on interval-valued Pythagorean fuzzy language sets (IVPFLSs). The frequent occurrence of emergency events can bring huge economic damage to human beings. To reduce the loss, it is very important to make reasonable emergency decisions effectively and timely. In the emergency decision making (EDM), these problems are few studied, such as high dimension problem, data nonlinearity, and correlation. For EDM problems, the advantage of IVPFLSs is that it can reasonably express the evaluation information given by decision makers (DMs) through both qualitative and quantitative aspects. However, if the dimension and nonlinear relationship of the decision data keep growing, and the traditional decision-making methods will fail. The distance measure between decision data is necessary to calculate in the process of dimensionality reduction, and the current research does not propose the definition of IVPFLSs distance measure. On the basis of this, this paper first uses the attributes and DMs as variables to define the standard Euclidean distance measure between IVPFLSs. For nonlinear features, we construct the interval-valued Pythagorean fuzzy language kernel principal component analysis (IVPFL-KPCA) model to reduce the dimensionality. What is more, we also obtain the reasonable weight vectors of the attribute and DMs from cumulative contribution rate. For low-dimensional decision data, according to the technique for order performance by similarity to ideal solution method, the best emergency plan is selected for the information variables after dimensionality reduction. In sum, the IVPFL-KPCA model not only avoids multicollinearity and nonlinear separability between decision data, but also obtains reasonable weights. It further improves the efficiency of decision-making operations and reduces the difficulty of the algorithm. Finally, an example of earthquake emergency plan is shown to demonstrate the feasibility and practicability of the proposed method. Besides, we also compare it with the existing methods, which proves the effectiveness of the method.

Research on Pragmatic Value of English Fuzzy Language Based on Needs Analysis

In the process of college English teaching, students often lack interest and initiative in learning. At this time, teachers can try to introduce the concepts and contents of fuzzy language and enrich the means and methods of language learning, so as to improve students' learning enthusiasm. The pragmatic value of fuzzy language and fuzzy language in college English needs teachers' attention. According to students' actual learning situation, we should choose the most suitable fuzzy language teaching method to further improve the efficiency of college English teaching. This paper mainly explores the pragmatic value of fuzzy language in college English teaching from the perspective of needs analysis, and puts forward corresponding suggestions. Fuzzy language is uncertain and relative, and also has strong pragmatic value. Therefore, university teachers should attach importance to the pragmatic value of fuzzy language in teaching and deepen students' understanding of fuzzy language.

Synthesizing fuzzy tree automata

Fuzzy tree automata are mathematical devices for modeling and analyzing vaguely defined tree structures. The behavior of a fuzzy tree automaton generates a fuzzy tree language by mapping a set of regular trees on a ranked alphabet to fuzzy membership values. It calculates the membership grade of trees using a set of rules that process their structural characteristics. This paper deals with constructing fuzzy tree automata models that their behavior satisfies a set of given logical propositions (called properties) on the structure of trees. Our goal is uncertainty modeling by synthesizing fuzzy tree automata whose behavior is described by fuzzy linguistic variables. In this regard, we first provide several patterns and heuristic tricks and techniques for constructing fuzzy tree automata that satisfy simple properties. Then, we develop a method for modeling complex propositional formulas based on the conversion of a logical formula into a computation tree, as well as a step-by-step combination of models.

Research on Credit Portfolio Evaluation of Metaverse Listed Companies Based on the Fuzzy Borda Method

To objectively evaluate the credit level of Metaverse listed companies, this paper constructs a credit evaluation index system for Metaverse listed companies from five aspects: profitability, solvency, growth ability, operational ability, and the ability to introduce technological innovation, and select 12 listed companies in 2021. Based on the relevant financial data of Metaverse listed companies, based on the multi-criteria decision-making problem Fermat's fuzzy TOPSIS method and the hesitant fuzzy language PROMETHEE multi-attribute decision-making method, the credit levels of 12 Metaverse listed companies were calculated respectively, and then the fuzzy Borda method was used to compare the two. The evaluation results of a single method are combined to obtain a combined evaluation result. Compared with the single method, the combined evaluation results have higher confidence. The final results show that GoerTek, Longli Technology, Changxin Technology, and Xinguodu 4 listed companies in the Metaverse are ranked high and have good credit levels. From the perspective of banks, they can be given priority when choosing to issue loans.

On Minimal Fuzzy Realization in Category Theoretic Setting

This paper aims to study the minimal fuzzy realization for a fuzzy language with membership values in a complete residuated lattice by using category theory. Specifically, we introduce the concept of a category [Formula: see text], whose object-class is complete transition residuated lattices corresponding to deterministic [Formula: see text]-semiautomata. We give the categorical characterization of reachability and observability maps for a given deterministic fuzzy automaton. In another direction, we demonstrate that the category [Formula: see text] is a subcategory of the categories [Formula: see text] of [Formula: see text]-coalgebras and [Formula: see text] of [Formula: see text]-dialgebras. Also, we discuss the concept of bisimulation between [Formula: see text]-coalgebras. Next, we introduce a general theory of minimal fuzzy realization for a given fuzzy language in a category theory setting. Strikingly, we demonstrate that all minimal fuzzy realization for a given fuzzy language is one of a kind up to isomorphism.

Decision-Making Method of Qualitative and Quantitative Comprehensive Evaluation of Talents Based on Probability Hesitation Fuzzy Language

A scientific, reasonable, and novel talent evaluation index system is the foundation of talent training and selection. Based on the novel “Man-Machine-Environment System Engineering” (hereinafter referred to as MMESE) theory, this paper proposes a novel talent evaluation index system that considers the ontological attributes and the external environment of the object comprehensively for talent evaluation, which could help the evaluator obtain more accurate evaluation results. Since the comprehensive evaluation of MMESE talents is a complex decision-making problem that is both qualitative and quantitative, a corresponding decision-making method that integrates qualitative and quantitative approaches is proposed here based on probabilistic language entropy and the possibility of superior order relationships. First, the weights of quantitative and qualitative attributes are calculated based on entropy theory and probabilistic fuzzy language. Second, the standard weight vectors of qualitative and quantitative attributes are obtained by adjusting the weight integration coefficients, and the change intervals of the pros and cons between the objects to be evaluated are calculated. Third, the pros and cons of the objects to be evaluated are compared to obtain the possibility degree matrix that describes the priority relationships among the objects, and a ranking vector is derived from the possibility degree matrix to reflect the rankings of the objects’ pros and cons. Finally, this system and the decision-making methods have been verified as scientific and effective.

Enterprise Performance Management following Big Data Analysis Technology under Multisource Information Fusion

With the development of the Internet, big data collection, analysis, and processing are flourishing. The study aims to explore the performance management of power enterprises based on multisource information fusion and big data. First, the application of big data to enterprise management is analyzed. Second, the multisource information fusion method is introduced, and the multisource information fusion model is implemented. Finally, the fuzzy language algorithm is used to evaluate the performance management of power enterprises. The results show that the proposed multisource information fusion algorithm has high efficiency in evaluating enterprise performance management. The evaluation result is closer to the actual value than other algorithms, and the maximum acceleration ratio can reach 7, indicating that the algorithm is suitable for processing big data. The performance evaluation shows that enterprises pay most attention to the quality of their products; the weight reached 0.414; and the index weight difference is large. This study promotes the reform of the performance management mode and improves the management efficiency of enterprises through the proposed enterprise performance management strategy. It provides a great reference for the application of big data and information fusion technology.

Application of integrated factor evaluation–analytic hierarchy process–T-S fuzzy fault tree analysis in reliability allocation of industrial robot systems

Aiming at the defects (only two kinds of state, i.e., normal or fault) of existed reliability allocation methods without considering the intermediate degradation process, a methodology named integrated factor evaluation–analytic hierarchy process–T-S fuzzy fault tree analysis (IFE-AHP-T-S fuzzy FTA) is proposed to allocate the reliability index of industrial robot systems (IRSs) which have multiple fault states. Firstly, the reliability model of IRSs is established and the allocation principle of reliability index is discussed. Secondly, two-layer IFE model is established considering the degradation of mechanical structure and multi-state fault of IRSs to evaluate the technical merit of different subsystems. The hesitant fuzzy language set is adopted to reduce the subjectivity of expert evaluation process, which have the ability of dealing with uncertain information. Then, the AHP is proposed to allocate different weights for influence factors, and the T-S fuzzy FTA is presented to calculate fault probability and mean time between failures (MTBF) in the process of weight allocation of reliability index for IRSs and six subsystems. Finally, multi-state reliability index allocation of IRSs is completed. This investigation has a significance for reducing the fault probability and unsafe factors, and provides a theoretical basis for the whole life cycle design of IRSs.