Linguistic Uncertainty Research Articles

Intelligent control for nonlinear inverted pendulum based on interval type-2 fuzzy PD controller

The interval type-2 fuzzy logic controller (IT2-FLC) is able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of a fuzzy logic system (FLS). This paper proposes an interval type-2 fuzzy PD (IT2F-PD) controller for nonlinear inverted pendulum. The proposed controller uses the Mamdani interval type-2 fuzzy rule based, interval type-2 fuzzy sets (IT2-FSs) with triangular membership function, and the Wu–Mendel uncertainty bound method to approximate the type-reduced set. The proposed controller is able to minimize the effect of the structure uncertainties and the external disturbances for the inverted pendulum. The results of the proposed controller are compared with the type-1 fuzzy PD (T1F-PD) controller in order to investigate the effectiveness and the robustness of the proposed controller. The simulation results show that the performance of the proposed controller is significantly improved compared with the T1F-PD controller. Also, the results show good performance over a wide range of the structure uncertainties and the effect of the external disturbances.

Real-time fuzzy system identification using uncertainty bounds

In this paper, a novel embedded real-time interval type-2 fuzzy neural network (FNN) system identification is presented using intelligent algorithms, back propagation (BP) algorithms. Interval type-2 FNN is introduced to handle uncertainties which arise from the noisy training data, noisy measurements used to activate the fuzzy logic system (FLS) and linguistic uncertainties. In order to overcome the iterative type-reduction overhead, the intelligent algorithms are proposed to learn the parameters of interval type-2 FLS using uncertainty bounds, inner- and outer-bound sets, which provide estimates of the uncertainties contained in the output of an interval type-2 FLS without having to perform the costly computations of type-reduction. Two nonlinear systems, namely, Duffing forced oscillation system and inverted pendulum system, are fully illustrated to be identified and simulation results show that not only similar identification performance to one that use type-reduction can be achieved but also significantly faster real-time identification can be performed.

Ranking biodiversity risk factors using expert groups – Treating linguistic uncertainty and documenting epistemic uncertainty

Sound planning is vital to ensure effective management of biodiversity, particularly where there is a high risk that management goals may not be achieved. This is the case at Toolibin Lake, an internationally recognised wetland, where changed hydrology as a result of agricultural development has detrimentally affected the quality and quantity of water entering the lake. Although management actions have slowed or halted degradation of the lake’s biological assets, goals have not been fully achieved and management is under review. To rank the hydrological risk factors threatening the lake’s biota as a foundation for more detailed planning, a structured elicitation process was used with an expert, cross-disciplinary group. Techniques used were explicitly aimed at minimising and documenting uncertainty. These included calibration questions to assess the accuracy of experts, and tightly specified goals and terms to minimise vagueness, ambiguity and redundancy. Surface water salinity, groundwater salinity and drought were the only factors identified as having a high probability of causing goal failure. Importantly, the majority of risk factors were evaluated as having a low probability of causing goal failure, enabling these factors to be rapidly eliminated from short-term consideration. The experts, acting anonymously when estimating probabilities, varied considerably in the assessment of one risk. This underlines the importance of rigorous processes to identify knowledge gaps and assess the likelihood that proposed management will be successful. The elicitation process provided low cost, rapid assessment of large numbers of risk factors with explicit assessment of uncertainty.

Risk Assessment of Groundwater Contamination: A Multilevel Fuzzy Comprehensive Evaluation Approach Based on DRASTIC Model

Groundwater contamination is a serious threat to water supply. Risk assessment of groundwater contamination is an effective way to protect the safety of groundwater resource. Groundwater is a complex and fuzzy system with many uncertainties, which is impacted by different geological and hydrological factors. In order to deal with the uncertainty in the risk assessment of groundwater contamination, we propose an approach with analysis hierarchy process and fuzzy comprehensive evaluation integrated together. Firstly, the risk factors of groundwater contamination are identified by the sources-pathway-receptor-consequence method, and a corresponding index system of risk assessment based on DRASTIC model is established. Due to the complexity in the process of transitions between the possible pollution risks and the uncertainties of factors, the method of analysis hierarchy process is applied to determine the weights of each factor, and the fuzzy sets theory is adopted to calculate the membership degrees of each factor. Finally, a case study is presented to illustrate and test this methodology. It is concluded that the proposed approach integrates the advantages of both analysis hierarchy process and fuzzy comprehensive evaluation, which provides a more flexible and reliable way to deal with the linguistic uncertainty and mechanism uncertainty in groundwater contamination without losing important information.

Risk prediction of medical equipment based on fuzzy processing of measuring information

Modern methods for determining and prognosis of the risk of medical equipment take into account the only stochastic uncertainty in measuring information on safety index characteristics, which limit the reliability of obtained evaluation of risks possibilities. It is shown that in the existence of linguistic uncertainty, the increase of risk evaluation accuracy and safety of medical equipment can be achieved through the development of methods for the determination and prognosis of risk on the basis of inaccurate processing of measuring information. We propose a method for prediction the risk in incubators for newborns based on inaccurate processing of air temperature measuring data in the children's compartment. The presented results of theoretical and experimental investigations confirm the hypothesis of the possibility of increasing the reliability of the risk evaluation using inaccurate processing of measuring data on medical equipment safety.

Core concepts of spatial prioritisation in systematic conservation planning

Systematic conservation planning (SCP) is a field of conservation biology concerned with delivering on-the-ground actions that achieve conservation goals. It describes a set of operational models that cover both design and implementation of conservation, with a strong focus on mobilising the collective action typically required to implement conservation. SCP, as it was originally described, was composed of six different stages: collection of data, identification of conservation goals, evaluation of the existing protected area network, design of expansions, implementation of conservation action, and long-term maintenance of biodiversity in the network. Since then, the operational model has been expanded into several different variants. Conservation actions applied inside SCP include establishment and expansion of reserve networks and allocation of habitat restoration and management.Within the broader context of SCP, there is a fundamental biogeographic-economic analysis frequently called spatial conservation prioritisation or conservation assessment, which is used for identifying where important areas for biodiversity are and how conservation goals might be achieved efficiently. Here, we review the usage and meaning of the 12 biogeographic-economic core concepts of SCP: adequacy, complementarity, comprehensiveness, effectiveness, efficiency, flexibility, irreplaceability, replacement cost, representation, representativeness, threat, and vulnerability. Some of the concepts have clear definitions whereas others may have alternative and possibly conflicting definitions. With a comprehensive literature review literature, we elucidate the historical backgrounds of these concepts, the first definitions and usages, alternative later definitions, key applications, and prior reviews. This review reduces linguistic uncertainty in the application of SCP. Since SCP is a global activity with a multitude of different stakeholders involved, it is vital that those involved can speak the same language. Through these concepts, this review serves as a source of information about the historical development of SCP. It provides a comprehensive review for anyone wishing to understand the key concepts of spatial prioritisation within SCP.

Treatment of uncertainty in conservation under climate change

AbstractClimate change is an important threat to biodiversity globally, but there are major uncertainties associated with its magnitude and ecological consequences. Here, we investigate how three major classes of uncertainty, linguistic uncertainty, epistemic uncertainty (uncertainty about facts), and human decision uncertainty, have been accounted for in scientific literature about climate change. Some sources of uncertainty are poorly characterized and epistemic uncertainty is much more commonly treated than linguistic or human decision uncertainty. Furthermore, we show that linguistic and human decision uncertainties are relatively better treated in the literature on sociopolitics or economics than in natural sciences, which often overlook communication between stakeholders and socioeconomic consequences. As uncertainty can significantly influence implementation of conservation, we discuss uncertainties associated with some commonly proposed conservation adaptation actions to mitigate climate change. There may be major differences between strategies, with implications on how they should be viewed in conservation planning. We conclude that evaluating conservation strategies in terms of different types of uncertainty will facilitate communication between disciplines and stakeholders. While accounting for uncertainties in a quantitative manner is difficult and data demanding, even qualitative appreciation about the uncertainties inherent in conservation strategies can facilitate and improve decision making.

Expressing Uncertainty in Computer-Mediated Discourse: Language as a Marker of Intellectual Work

Learning and dialogue may naturally engender feelings and expressions of uncertainty for a variety of reasons and purposes. Yet, little research has examined how patterns of linguistic uncertainty are enacted and changed over time as students reciprocally influence one another and the dialogical system they are creating. This study describes the occurrence of uncertainty expressions of graduate students collaborating in computer-mediated discussions to negotiate and construct understandings of new concepts from course readings. We report on how often uncertainty was expressed in online synchronous and asynchronous discussions and the characteristics of its expression. We also explore the antecedents and consequences of such expression. Findings indicate that students expressed uncertainty often and in many ways and that such expression seemed to be integrated in a dynamic system of influences on the conversation. We conclude that the ability to deal with and express uncertainty appropriately is an important skill in online academic contexts that may be related to the intellectual work of students in the process of appropriating the discourse of their academic disciplines as they make meaning of scholarly texts.

General Type-2 Fuzzy C-Means Algorithm for Uncertain Fuzzy Clustering

Pattern recognition in real-world data is subject to various sources of uncertainty that should be appropriately managed. The focus of this paper is the management of uncertainty associated with parameters of fuzzy clustering algorithms. Type-2 fuzzy sets (T2 FSs) have received increased research interest over the past decade, primarily due to their potential to model various uncertainties. However, because of the computational intensity of the processing of general T2 fuzzy sets (GT2 FSs), only their constrained version, i.e., the interval T2 (IT2) FSs, were typically used. Fortunately, the recently introduced concepts of α-planes and zSlices allow for efficient representation and computation with GT2 FSs. Following this recent development, this paper presents a novel approach for uncertain fuzzy clustering using the general type-2 fuzzy C-means (GT2 FCM) algorithm. The proposed method builds on top of the previously published IT2 FCM algorithm, which is extended via the α- planes representation theorem. The fuzzifier parameter of the FCM algorithm can be expressed using linguistic terms such as “small” or “high,” which are modeled as T1 FSs. This linguistic fuzzifier value is then used to construct the GT2 FCM cluster membership functions. The linguistic uncertainty is transformed into uncertain fuzzy positions of the extracted clusters. The GT2 FCM algorithm was found to balance the performance of T1 FCM algorithms in various uncertain pattern recognition tasks and to provide increased robustness in situations where noisy or insufficient training data are present.

Fuzzy-Based System for Determining the Severity Level of Knee Osteoarthritis

the task of medical diagnosis, unlike other diagnostic processes is more complex because a lot of vagueness, linguistic uncertainty, subjectivity, measurement imprecision, natural diversity are all prominently present in medical diagnosis. Osteoarthritis (OA) of the knee is a major public health issue causing chronic disability and reduction in quality of life; it is reported to be associated with a significant decline in function and causes a higher rate of disability than any other chronic condition. Osteoarthritis (OA) exacts a cost in terms of pain, limited mobility, and decreased function among a wide range of individuals. With improvement in science and technology, intelligent computing has been used to assist in enhancing qualitative services. This paper reports the development of a fuzzy-based system to determine the level of severity of knee osteoarthritis, given some input conditions. The system was implemented and simulated using MATLAB Fuzzy Logic Toolbox. The results are entrusting and promising based on the flexibility and case of adaptability.

Universals in language contact and translation

Translational communication, in relevance theoretic terms, is interpretive language use, depending on a pre-existing text. Types of translation that cannot be regarded as interpretive still share the latter feature. Ordinary bilingual communication is descriptive language use, i.e. it represents independent text production. Both types of communication involve the use of two languages, and this fact may account for similar phenomena appearing in both types of communication.The present study surveys parallels between proposed translation universals and similar features of bilingual communication, which we may tentatively call language contact universals. The present paper hypothesizes that both kinds of universals (or general features) are likely to be manifestations of universals of constrained communication. The main constraint on bilingual communication is the need to manage two languages. Linguistic uncertainty resulting from the parallel activation of two languages affects both bilingual and translational communication. In the latter, an additional constraint is the fact that it is interpretive language use. Both types of bilingual communication give rise to special language varieties (translated language and contact language varieties). Further research to confirm these hypotheses is called for.

Uncertainty in invasive alien species listing

Lists of invasive alien species (IAS) are essential for preventing, controlling, and reporting on the state of biological invasions. However, these lists suffer from a range of errors, with serious consequences for their use in science, policy, and management. Here we (1) collated and classified errors in IAS listing using a taxonomy of uncertainty; and (2) estimated the size of these errors using data from a completed listing exercise, with the purpose of better understanding, communicating, and dealing with them. Ten errors were identified. Most result from a lack of knowledge or measurement error (epistemic uncertainty), although two were a result of context dependence and vagueness (linguistic uncertainty). Estimates of the size of the effects of these errors were substantial in a number of cases and unknown in others. Most errors, and those with the largest estimated effect, result in underestimates of IAS numbers. However, there are a number of errors where the size and direction of the effect remains poorly understood. The effect of differences in opinion between specialists is potentially large, particularly for data-poor taxa and regions, and does not have a clearly directional or consistent effect on the size and composition of IAS lists. Five tactics emerged as important for reducing uncertainty in IAS lists, and while uncertainty will never be removed entirely, these approaches will significantly improve the transparency, repeatability, and comparability of IAS lists. Understanding the errors and uncertainties that occur during the process of listing invasive species, as well as the potential size and nature of their effects on IAS lists, is key to improving the value of these lists for governments, management agencies, and conservationists. Such understanding is increasingly important given positive trends in biological invasion and the associated risks to biodiversity and biosecurity.

Adaptive fuzzy H ∞ tracking design of SISO uncertain nonlinear fractional order time-delay systems

In this paper, a fuzzy logic controller equipped with training algorithms is developed such that the H∞ tracking performance should be satisfied for a model-free nonlinear fractional order time delay system which is infinite dimensional in nature and time delay is a source of instability. In order to deal with the linguistic uncertainties caused from delay terms, the adaptive time delay fuzzy logic system is constructed to approximate the unknown time delay system functions. By incorporating Lyapunov stability criterion with H∞ tracking design technique, the free parameters of the adaptive fuzzy controller can be tuned on line by output feedback control law and adaptive law. Moreover, the tracking error and external disturbance can be attenuated to arbitrary desired level. The numerical results show the effectiveness of the proposed adaptive H∞ tracking scheme.

Adaptive Backstepping Fuzzy Control Based on Type‐2 Fuzzy System

A novel indirect adaptive backstepping control approach based on type‐2 fuzzy system is developed for a class of nonlinear systems. This approach adopts type‐2 fuzzy system instead of type‐1 fuzzy system to approximate the unknown functions. With type‐reduction, the type‐2 fuzzy system is replaced by the average of two type‐1 fuzzy systems. Ultimately, the adaptive laws, by means of backstepping design technique, will be developed to adjust the parameters to attenuate the approximation error and external disturbance. According to stability theorem, it is proved that the proposed Type‐2 Adaptive Backstepping Fuzzy Control (T2ABFC) approach can guarantee global stability of closed‐loop system and ensure all the signals bounded. Compared with existing Type‐1 Adaptive Backstepping Fuzzy Control (T1ABFC), as the advantages of handling numerical and linguistic uncertainties, T2ABFC has the potential to produce better performances in many respects, such as stability and resistance to disturbances. Finally, a biological simulation example is provided to illustrate the feasibility of control scheme proposed in this paper.

Interval Type-2 Fuzzy Logic Control of Mobile Robots

Navigation of autonomous mobile robots in dynamic and unknown environments needs to take into account different kinds of uncertainties. Type-1 fuzzy logic research has been largely used in the control of mobile robots. However, type-1 fuzzy control presents limitations in handling those uncertainties as it uses precise fuzzy sets. Indeed type-1 fuzzy sets cannot deal with linguistic and numerical uncertainties associated with either the mechanical aspect of robots, or with dynamic changing environment or with knowledge used in the phase of conception of a fuzzy system. Recently many researchers have applied type-2 fuzzy logic to improve performance. As control using type-2 fuzzy sets represents a new generation of fuzzy controllers in mobile robotic issue, it is interesting to present the performances that can offer type-2 fuzzy sets by regards to type-1 fuzzy sets. The paper presented deep and new comparisons between the two sides of fuzzy logic and demonstrated the great interest in controlling mobile robot using type-2 fuzzy logic. We deal with the design of new controllers for mobile robots using type-2 fuzzy logic in the navigation process in unknown and dynamic environments. The dynamicity of the environment is depicted by the presence of other dynamic robots. The performances of the proposed controllers are represented by both simulations and experimental results, and discussed over graphical paths and numerical analysis.

Adaptive Non-singleton Type-2 Fuzzy Logic Systems: A Way Forward for Handling Numerical Uncertainties in Real World Applications

Real world environments are characterized by high levels of linguistic and numerical uncertainties. A Fuzzy Logic System (FLS) is recognized as an adequate methodology to handle the uncertainties and imprecision available in real world environments and applications. Since the invention of fuzzy logic, it has been applied with great success to numerous real world applications such as washing machines, food processors, battery chargers, electrical vehicles, and several other domestic and industrial appliances. The first generation of FLSs were type-1 FLSs in which type-1 fuzzy sets were employed. Later, it was found that using type-2 FLSs can enable the handling of higher levels of uncertainties. Recent works have shown that interval type-2 FLSs can outperform type-1 FLSs in the applications which encompass high uncertainty levels. However, the majority of interval type-2 FLSs handle the linguistic and input numerical uncertainties using singleton interval type-2 FLSs that mix the numerical and linguistic uncertainties to be handled only by the linguistic labels type-2 fuzzy sets. This ignores the fact that if input numerical uncertainties were present, they should affect the incoming inputs to the FLS. Even in the papers that employed non-singleton type-2 FLSs, the input signals were assumed to have a predefined shape (mostly Gaussian or triangular) which might not reflect the real uncertainty distribution which can vary with the associated measurement. In this paper, we will present a new approach which is based on an adaptive non-singleton interval type-2 FLS where the numerical uncertainties will be modeled and handled by non-singleton type-2 fuzzy inputs and the linguistic uncertainties will be handled by interval type-2 fuzzy sets to represent the antecedents’ linguistic labels. The non-singleton type-2 fuzzy inputs are dynamic and they are automatically generated from data and they do not assume a specific shape about the distribution associated with the given sensor. We will present several real world experiments using a real world robot which will show how the proposed type-2 non-singleton type-2 FLS will produce a superior performance to its singleton type-1 and type-2 counterparts when encountering high levels of uncertainties.

Direct adaptive interval type-2 fuzzy logic controller for the multivariable anaesthesia system

Direct adaptive fuzzy controller is a class of adaptive fuzzy controllers which use fuzzy logic system (FLS) as controller. Interval type-2 fuzzy sets are able to model and minimize the numerical and linguistic uncertainties associated with the inputs and outputs of fuzzy logic controller (FLC). In this paper, a direct adaptive interval type-2 FLC is proposed for controlling the multivariable anaesthesia system to overcome the uncertainty problem that introduced by large inter and intra-individual variability of the patient’s parameters. Simulation results show good performance over a wide range of inter-individual variability of parameters. Also, results show better performance for the proposed controller under the effect of intra-individual variability of parameters than interval type-2 FLC. So, adaptive interval type-2 FLC affords some improvements in performance over the interval type-2 FLC.

Linguistic Objectivity in Norm Sentences: Alternatives in Literal Meaning

AbstractAssuming that legal science, specifically with regard to interpretation, has to provide the tools to reduce the uncertainty of legal solutions arising from the use of natural languages by legal orders, it becomes a central matter to identify, in this limited domain, the spectrum of semantic variation (and its boundaries) that language brings to the definition of a norm expressed by a norm sentence. It is in this framework that the present paper, analyzing norm sentences as a specific kind of speech act, examines the relation of the legal order to natural language rules, the limits of linguistic uncertainty, and alternatives of meaning as distinct possibilities of norms covered by the text (that are, because of this, still within what literal meaning is). Considering interpretation just as the linguistic decoding process, the reverse of the process of creating norm sentences, the paper also argues that subjects not connected with the relation between norm sentences and norms (mainly, normative defeasibility) are analytically distinct and should be removed from the field of language.

Interval type-2 fuzzy modeling of wind power generation in Genco's generation scheduling

Wind power is a promising source of electric power generation since it has tremendous environmental and social benefits. The generation scheduling (GS) problem encounters several uncertainties in terms of the system's parameters such as load, reserve and available wind power generation. The modeling of those uncertainties is an important issue in power system scheduling. A fuzzy based modeling approach can be used to develop the generation schedule under an uncertain environment. In this paper, the type-2 fuzzy membership function (MF) is implemented to model the linguistic uncertainty of type-1 MF of available wind power generation which stems from opinions of different experts. The proposed approach is applied to two test systems (six and twenty-six conventional generating units both with two wind farms) and the results of generation scheduling using both fuzzy modeling type-1, and type-2 are presented. These results demonstrate that the advantage of using the type-2 fuzzy to model the linguistic uncertainty of the type-1 MF. This paper demonstrates how unit scheduling in an uncertain environment of type-1 fuzzy MF modeling can be performed just by using a single type-2 fuzzy MF when all type-1 MF were in the footprint of uncertainty (FOU) of type-2 MF.

EVALUATION OF SALESPERSON BY FUZZY TOPSIS ALGORITHM WITH TRIANGULAR FUZZY NUMBERS

Salesperson selection process is a multi-criteria decision making problem for businessess. election of a salesperson who has all nessesary criterias for doing a job, is very effective for succsess. Most of the choices are qualitative for selection decisions. In this study, salesperson candidates evaluated with Fuzzy TOPSIS (Technique For Order Performance By Similarity To Ideal Solution) Algorithm. Triangular fuzzy numbers are used in a fuzzy environment where linguistic uncertainty exists. A firm’s salesperson process is analyzed which has 17 sale stores. The most convenient salesperson selected between eight candidate salesperson, towards the criterias determinated by decision makers.