A linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) with learning algorithm

In this study, the linguistic information feed-back-based dynamical fuzzy system (LIFBDFS) proposed earlier by the authors is first introduced. The principles of α-level sets and backpropagation through time approach are also briefly discussed. We next employ these two methods to derive an explicit learning algorithm for the feedback parameters of the LIFBDFS. With this training algorithm, our LIFBDFS indeed becomes a potential candidate in solving real-time modeling and prediction problems.     

基于模糊语言的信息系统事前评价模型

给出了信息系统事前评价的指标体系.针对信息系统事前评价指标的不确定性,提出使用语言值对指标进行评价,并给出了一种基于模糊语言的信息系统事前评价模型.由决策者给出语言判断矩阵,使用EOWA算子进行集结,并求得各方案的综合属性值,对方案进行择优,为信息化建设提供决策支持.     

A neural fuzzy system with linguistic teaching signals

A neural fuzzy system learning with linguistic teaching signals is proposed. This system is able to process and learn numerical information as well as linguistic information. It can be used either as an adaptive fuzzy expert system or as an adaptive fuzzy controller. First, we propose a five-layered neural network for the connectionist realization of a fuzzy inference system. The connectionist structure can house fuzzy logic rules and membership functions for fuzzy inference. We use α-level sets of fuzzy numbers to represent linguistic information. The inputs, outputs, and weights of the proposed network can be fuzzy numbers of any shape. Furthermore, they can be hybrid of fuzzy numbers and numerical numbers through the use of fuzzy singletons. Based on interval arithmetics, two kinds of learning schemes are developed for the proposed system: fuzzy supervised learning and fuzzy reinforcement learning. Simulation results are presented to illustrate the performance and applicability of the proposed system     

Intuitionistic fuzzy linguistic clustering algorithm based on a new correlation coefficient for intuitionistic fuzzy linguistic information

Pattern Analysis and Applications - For observations to be classified, when scoring rules are imprecise or the cost of their computation is too high, the clustering method under linguistic...     

Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries

As in the Web, the growing of information is the main problem of the academic digital libraries. Thus, similar tools could be applied in university digital libraries to facilitate the information access by the students and teachers. In [46] we presented a fuzzy linguistic recommender system to advice research resources in university digital libraries. The problem of this system is that the user profiles are provided directly by the own users and the process for acquiring user preferences is quite difficult because it requires too much user effort. In this paper we present a new fuzzy linguistic recommender system that facilitates the acquisition of the user preferences to characterize the user profiles. We allow users to provide their preferences by means of incomplete fuzzy linguistic preference relation. We include tools to manage incomplete information when the users express their preferences, and, in such a way, we show that the acquisition of the user profiles is improved.     

A context-aware reminder system for elders based on fuzzy linguistic approach

To date, several reminder systems for elders have been developed, but when and how to prompt the reminding message has not been fully explored yet. This paper presents a context-aware system that provides reminding messages for elders based on fuzzy linguistic model in order to properly deliver a reminder in an appropriate time and way. By separating the user activity contexts and contexts utilized to trigger a reminder, it is much easier for the elderly or their care givers to schedule and maintain reminders. We first adopt a fuzzy linguistic model to determine the prompting level based on the interrupt degree of user current activity and the urgent level of to-be-prompt reminder. An adaptive mapping strategy is then presented to transfer the prompting level into machine-readable parameters. Finally, the proposed approach is verified through our system prototype and experiments.     

Understanding user commands by evaluating fuzzy linguistic information based on visual attention

This article proposes a method for understanding user commands based on visual attention. Normally, fuzzy linguistic terms such as “very little” are commonly included in voice commands. Therefore, a robot’s capacity to understand such information is vital for effective human-robot interaction. However, the quantitative meaning of such information strongly depends on the spatial arrangement of the surrounding environment. Therefore, a visual attention system (VAS) is introduced to evaluate fuzzy linguistic information based on the environmental conditions. It is assumed that the corresponding distance value for a particular fuzzy linguistic command depends on the spatial arrangement of the surrounding objects. Therefore, a fuzzy-logic-based voice command evaluation system (VCES) is proposed to assess the uncertain information in user commands based on the average distance to the surrounding objects. A situation of object manipulation to rearrange the user’s working space is simulated to illustrate the system. This is demonstrated with a PA-10 robot manipulator.     

A model for selecting an ERP system based on linguistic information processing

An enterprise resource planning (ERP) is an enterprise-wide application software package that integrates all necessary business functions into a single system with a common database. In order to implement an ERP project successfully in an organization, it is necessary to select a suitable ERP system. This paper presents a new model, which is based on linguistic information processing, for dealing with such a problem. In the study, a similarity degree based algorithm is proposed to aggregate the objective information about ERP systems from some external professional organizations, which may be expressed by different linguistic term sets. The consistency and inconsistency indices are defined by considering the subject information obtained from internal interviews with ERP vendors, and then a linear programming model is established for selecting the most suitable ERP system. Finally, a numerical example is given to demonstrate the application of the proposed method.     

A new computational approach to stability analysis and synthesis of linguistic fuzzy control system

In this paper, stability analysis and synthesis of the type-II (Singleton-type) linguistic fuzzy control system are addressed. First, stability theorems are given for both continuous-time case and discrete-time case and they are recast in the formulation of bilinear matrix inequalities (BMIs). Continuous and discrete iterative linear matrix inequality methods are presented to obtain the feasible solution for the stability conditions represented as the BMI. The interesting feature of this paper is that the suggested method can be applied not only to the stability analysis but also to the controller synthesis. When it is applied to the synthesis of the type-II fuzzy logic controller, the resulting one can theoretically guarantee the stability of the closed-loop system. Finally, the effectiveness of the suggested method is illustrated via computer simulation.     

A class of type-2 fuzzy neural networks for nonlinear dynamical system identification

This paper presents the ability of the interval type-2 Takagi–Sugeno–Kang fuzzy neural networks (IT2-TSK-FNN) for nonlinear dynamical system identification. The proposed IT2-TSK-FNN has seven layers. The first two layers consist of type-2 fuzzy neurons with uncertainty in the mean of Gaussian membership functions. Third layer is rule layer. Type-reduction is done in fourth layer. In the fifth, sixth, and seventh layers, consequent left–right firing points, two end points, and output are evaluated, respectively. In this paper, gradient descent with adaptive learning rate backpropagation is used in learning phase. IT2-TSK-FNN is used for the identification of three nonlinear systems, and then results are compared with adaptive-network-based fuzzy inference system (ANFIS).     

FILIP: A fuzzy intelligent information system with learning capabilities

FILIP (fuzzy intelligent learning information processing) system is designed with the goal to model human information processing. The issues addressed are uncertain knowledge representation and approximate reasoning based on fuzzy set theory, and knowledge acquisition by “being told” or by “learning from examples”. Concepts that can be “learned” by the system can be imprecise (fuzzy), or the knowledge can be incomplete. In the latter case, FILIP uses the concept of similarity to extrapolate the knowledge to cases that were not covered by examples provided by the user. Concepts are stored in the Knowledge Base and employed in intelligent query processing, based on flexible inference that supports approximate matches between the data in the database and the query.

The architecture of FILIP is discussed, the learning algorithm is described, and examples of the system's performance in the knowledge acquisition and querying modes, together with its explanatory capabilities are shown.     

Double-hierarchy hesitant fuzzy linguistic information-based framework for green supplier selection with partial weight information

Neural Computing and Applications - Green supplier selection (GSS) is a crucial issue in green supply chain management. CAPS indicate that industries spend yearly USD 25 million per procurement,...     

A 2-tuple fuzzy linguistic representation model for computing withwords

The fuzzy linguistic approach has been applied successfully to many problems. However, there is a limitation of this approach imposed by its information representation model and the computation methods used when fusion processes are performed on linguistic values. This limitation is the loss of information; this loss of information implies a lack of precision in the final results from the fusion of linguistic information. In this paper, we present tools for overcoming this limitation. The linguistic information is expressed by means of 2-tuples, which are composed of a linguistic term and a numeric value assessed in (-0.5, 0.5). This model allows a continuous representation of the linguistic information on its domain, therefore, it can represent any counting of information obtained in a aggregation process. We then develop a computational technique for computing with words without any loss of information. Finally, different classical aggregation operators are extended to deal with the 2-tuple linguistic model     

一种信息系统安全风险的灰色模糊综合评估方法

针对信息系统安全风险因素的灰色性和模糊性,以及信息安全风险评估过程中存在的主观性,将灰色统计评估法、模糊综合评判法和层次分析法结合,建立一种信息系统安全风险的灰色模糊综合评估模型.通过灰色统计法建立模糊隶属度矩阵,层次分析法确定风险因素权重,以此来评估量化系统风险,该方法能较好地量化评估信息系统安全风险.     

A similarity measure for fuzzy rulebases based on linguistic gradients

Fuzzy rulebases are an approximate representation of some interesting system. As such, they could potentially be used for indexing and searching candidate solutions in case-based reasoning (CBR) systems in a variety of application areas. However, there is currently no method for directly and efficiently determining the similarity between two rulebases, which is a necessary element of their use in any CBR system. We propose a method for measuring similarity between two linguistic fuzzy rulebases, based on the granular computing technique of linguistic gradients. The proposed similarity measure is based on comparing the linguistic structure of two rulebases, using the linguistic gradient operator to reveal that structure. Our algorithm operates at the level of linguistic rulebases, rather than a defuzzified reasoning surface, and thus belongs to the Computing-with-Words paradigm. In a validation experiment, we compare our new method with the root-mean-square difference between reasoning surfaces for 603 pairs of fuzzy rulebases drawn from the literature. A Spearman correlation analysis shows that our new linguistic method is consistent with the numerical RMS results, while being theoretically and empirically faster than computing an accurate RMS difference.     

Extension of the VIKOR method for group decision making with extended hesitant fuzzy linguistic information

Group decision-making approaches are very important due to the complexity and uncertainty of many real-world decision-making problems. Some of the decision-making problems are defined in qualitative frameworks. Extended hesitant fuzzy linguistic term set (EHFLTS) is proposed as a new and powerful tool for elicitation of hesitant qualitative information in group decision-making process. In this paper, we first introduced the comparison laws and a family of distance and similarity measures for extended hesitant fuzzy linguistic terms (EHFLTs) and EHFLTSs, respectively. Next, we developed the extended hesitant fuzzy linguistic (EHFL)-VIKOR method as a qualitative multi-attributes group decision-making approach based on the EHFLTS distance measures to deal with the qualitative hesitancy in group decision making. Finally, we presented an application example about selection of suitable telecommunications service provider of small- and medium-sized enterprises to verify applicability and validation of proposed method in the process of qualitative group decision making.

     

A new type-2 fuzzy set of linguistic variables for the fuzzy analytic hierarchy process

The fuzzy analytic hierarchy process (FAHP) has been used to solve various multi-criteria decision-making problems where trapezoidal type-1 fuzzy sets are utilized in defining decision-makers’ linguistic judgment. Previous theories have suggested that interval type-2 fuzzy sets (IT2 FS) can offer an alternative that can handle vagueness and uncertainty. This paper proposes a new FAHP characterized by IT2 FS for linguistic variables. Differently from the typical FAHP, which directly utilizes trapezoidal type-1 fuzzy numbers, this method introduces IT2 FS to enhance judgment in the fuzzy decision-making environment. This new model includes linguistic variables in IT2 FS and a rank value method for normalizing upper and lower memberships of IT2 FS. The proposed model is illustrated by a numerical example of work safety evaluation. Comparable results are also presented to check the feasibility of the proposed method. It is shown that the ranking order of the proposed method is consistent with the other two methods despite difference in weight priorities.