Linguistic Hedges Research Articles

CMOS fuzzy logic controller supporting fractional polynomial membership functions

This paper presents a new Fuzzy Logic Controller (FLC) having the ability to support fractional polynomial membership functions. These functions are general forms of triangular and trapezoidal membership functions, and also those functions which are used in linguistic hedge FLC (LHFLC). A two-input, single-output Takagi–Sugeno–Kang (TSK) type 0 FLC is designed in 0.35 μm standard CMOS process. Analog realization of the circuit makes the design programmable and extendable, while having high speed and low power consumption. Also all the control signals and input signals are in voltage form and no need for digital programmability. Voltage mode realization of the circuits leads to a simple use of FLC with other circuits which are in voltage mode like sensors. Simulation results of the controller using HSPICE simulator and BSIM3v3 parameters, and comparing them with ideal results obtained from MATLAB software verify the functionality and performance of the design.

A label semantics approach to linguistic hedges

We introduce a model for the linguistic hedges ‘very’ and ‘quite’ within the label semantics framework, and combined with the prototype and conceptual spaces theories of concepts. The proposed model emerges naturally from the representational framework we use and as such, has a clear semantic grounding. We give generalisations of these hedge models and show that they can be composed with themselves and with other functions, going on to examine their behaviour in the limit of composition.

Neuro-fuzzy feature selection approach based on linguistic hedges for medical diagnosis

The use of soft computing techniques in disease diagnosis is increasing. This is mainly because the effectiveness of classification and prediction systems has been improved to help physicians in diagnosing. One of the core issues in medical data analysis and mining is the curse of dimensionality; particularly, the medical datasets are characterised by relatively few instances and presented in a high-dimensional feature space. Feature selection plays an important role in building classification systems. It can not only reduce the dimension of data, but also lower the computation costs and gain a good classification performance. In this paper, a feature selection method based on linguistic hedges neural-fuzzy classifier (LHNFC) is presented for medical diagnosis. This classifier is used to achieve very fast, simple and efficient diagnosis. The results indicated that use of linguistic hedges in adaptive neural-fuzzy classifier improves the success of the classifier. Applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted, or unimportant features. The results strongly suggest that the proposed method not only helps to reduce the dimensionality of large datasets but also can speed up the computation time of a learning algorithm and simplify the classification tasks.

Fuzzy linguistic hedges for the selection of manufacturing process for prosthetic sockets

In this paper, a comparison is presented between two prime methods of producing prosthetic sockets by using the fuzzy linguistic hedges approach on the qualitative feedback of Indian prosthetic users. Recent trends indicate that the Indian manufacturers have tried to adopt the newer technologies like reverse engineering (RE) approach to achieve the desired goals. However, the satisfaction of the user is of utmost importance for the unique and customized products for rehabilitation. In order to analyze the effectiveness of the manufacturing approaches, user case studies are taken, based on the linguistic feedbacks, and a comparative study is conducted. Thirteen users from four different manufacturing units are taken for study and sockets made by conventional as well as RE are experimented. Fuzzy membership functions are constructed using the linguistic hedges based on the user feedbacks. An analytical hierarchy process (AHP) is applied to arrive at a decision to select the manufacturing process for user satisfaction and manufacturing excellence.

"First Offers Revisited: The Impact of Counteroffers, Integrative Information and Anchor Precision."

The first moments at the bargaining table critically impact final settlements. Making the first offer and doing so ambitiously can give individuals a significant bargaining advantage (see Galinsky, Ku, & Mussweiler, 2009 for a review). Abundant research indicates that this first- offer advantage—senders obtain better individual outcomes than recipients—is highly robust; the effect has been replicated across Eastern and Western cultures and among negotiators varying in power and expertise. In the present symposium, three research project strive to cast novel light on this well-established first offer effect. In the first paper, Wazlawek and Ames examine the important role of counteroffers. Second, Loschelder, Troetschel and Friese investigate the crucial role of compatible and/or integrative information conveyed by senders and establish a first-mover disadvantage. Third, Mason, Ames, Lee and Wiley illustrate the differential effectiveness of numerically precise vs. rounded first offers and how powerful words (i.e., linguistic hedges) further moderate this effect. Finally, Gillian Ku will discuss the three projects in light of their theoretical contributions to the first-offer literature and their practical implications for the first minutes at a bargaining table. Pitfalls of Moving First – When and How First Offers Backfire Presenter: David D Loschelder; Saarland U. Presenter: Roman Trotschel; Leuphana U. Presenter: Malte Friese; Saarland U. Discussant Presenter: Gillian Ku; London Business School Anchors Away – The Sources and Impact of Counteroffers Presenter: Abbie Wazlawek; Columbia Business School Presenter: Daniel Ames; Columbia U. More Than What is Said – Attributions of Knowledge and Intent Shape Negotiated Agreements Presenter: Daniel Ames; Columbia U. Presenter: Malia Mason; Columbia Business School Presenter: Alice J Lee; Columbia Business School Presenter: Elizabeth A. Wiley; Columbia Business School

Linguistic variable, hedges and several distances on complex fuzzy sets

Complex fuzzy set CFS is a fuzzy set characterized by complex-valued membership function consisting of amplitude and phase terms, whose range of membership function extended to the unit circle in the complex plane. We often use words to describe variables in our daily life. Hence, the definition of linguistic variables and linguistic hedges on CFS are introduced. The most commonly used hedges are obtained. This paper also illustrates priority needs and advantages to introduce several distance measures in the case of complex fuzzy sets. Solutions to some problems in different fields are suggested through complex fuzzy distance measure and several distance measures between two complex fuzzy sets are introduced. The boundary for each distance of complex fuzzy sets is obtained.

Opinion Mining from Online User Reviews Using Fuzzy Linguistic Hedges

Nowadays, there are several websites that allow customers to buy and post reviews of purchased products, which results in incremental accumulation of a lot of reviews written in natural language. Moreover, conversance with E-commerce and social media has raised the level of sophistication of online shoppers and it is common practice for them to compare competing brands of products before making a purchase. Prevailing factors such as availability of online reviews and raised end-user expectations have motivated the development of opinion mining systems that can automatically classify and summarize users’ reviews. This paper proposes an opinion mining system that can be used for both binary and fine-grained sentiment classifications of user reviews. Feature-based sentiment classification is a multistep process that involves preprocessing to remove noise, extraction of features and corresponding descriptors, and tagging their polarity. The proposed technique extends the feature-based classification approach to incorporate the effect of various linguistic hedges by using fuzzy functions to emulate the effect of modifiers, concentrators, and dilators. Empirical studies indicate that the proposed system can perform reliable sentiment classification at various levels of granularity with high average accuracy of 89% for binary classification and 86% for fine-grained classification.

A Fully Programmable Analog CMOS Rational-Powered Membership Function Generator with Continuously Adjustable High Precision Parameters

This paper presents a novel structure for implementing rational-powered membership functions (RPMFs), which are the extended forms of triangular/trapezoidal membership functions and those functions which are generated by applying linguistic hedges. The hardware realization of an RPMF consists of a triangular membership function generator circuit followed by a rational-powered generator module (RPGM). A novel fully programmable compact triangular/trapezoidal/s-shaped/z-shaped membership function generator with the ability to continuously change parameters is presented which is compatible with the proposed RPGM. A new method is introduced to implement the RPGM based on the approximation of the function "x a " by the functions square and square-rooter which are simply implemented in a current-mode analog approach based on the translinear principle, which leads to a design that is simple, and has high accuracy and less hardware usage, with a resulting lower chip area and lower power consumption. The designed circuit was simulated by an HSPICE simulator with level 49 parameters (BSIM3v3), and the simulation results show that the maximum power consumption of the RPGM is 800 μW, while the maximum RMS error is 1.25 %. Finally, layouts of the circuits prepared using Cadence software are presented.

English

  A new prediction model for the early warning of apple scab is proposed in this study. The method is based on artificial intelligence and time series prediction. The infection period of apple scab was evaluated as the time series prediction model instead of summation of wetness duration. Also, the relations of different measurements with apple scab infection time were analyzed. The important hours of duration were determined with the feature selection methods, such as Pearson’s correlation coefficients (PCC), Fisher’s linear discriminant analysis (FLDA) and an adaptive neuro-fuzzy classifier with linguistic hedges (ANFC_LH). The experimental dataset with selected features was classified by ANFC_LH, and predicted by an adaptive neural network (ANN) model. The proposed ANN model successfully predicts the apple scab infection time with 2 to 5% error rates compared to the traditional weather station predictions. The results show that the last 24-hour period is important to determine the apple scab infection at any time.   Key words: Apple scab (Venturia inaequalis), early warning, time series prediction, feature selection, artificial intelligence.

FUZZY ATTRIBUTE IMPLICATIONS AND THEIR EXPRESSIVE POWER

We deal with the expressive power of if-then rules called fuzzy attribute implications (FAIs) which can be seen as formulas A ⇒ B where both A and B are conjunctions of subformulas containing propositional variables and constants for truth degrees and whose interpretation is parameterized by linguistic hedges. The formulas admit the following model-theoretical property: a system of fuzzy sets [Formula: see text] is a fuzzy closure system satisfying an additional condition of being closed under a*-shifts (so-called L*-closure system) if and only if [Formula: see text] is a system of models of a set of FAIs. In this paper, we point out the importance of constants of truth degrees in the antecedents of formulas by showing that simpler formulas are not sufficient to describe all L*-closure systems. As a practical consequence, the simpler formulas cannot be used as an alternative description of concept lattices with linguistic hedges.

SOME OPERATIONS OVER ATANASSOV'S INTUITIONISTIC FUZZY SETS BASED ON EINSTEIN T-NORM AND T-CONORM

We define some operations over Atanassov's intuitionistic fuzzy sets (AIFSs), such as Einstein intersection, Einstein product, Einstein scalar multiplication and Einstein exponentiation, etc., and then define new concentration and dilation of AIFSs. These definitions will be useful while dealing with various linguistic hedges like “very”, “more or less”, “highly”, “very very” etc., involved in the problems under intuitionistic fuzzy environment. We also prove some propositions and present some examples in this context.

Stochastic MIMO channel modelling using FMLP-based inference engine

A fuzzy-neural FN estimator of stochastic multi-input-multi-output MIMO wireless channels shows dependence on the membership and inference rule generation norms adopted. These two factors decide the ability of the fuzzy-estimator to capture the subtle variations in the input signal patterns and provide corresponding responses with optimum performance. They also determine the precision and processing speed of the FN-estimator while tackling stochastic behaviour of the MIMO channels. The membership and inference rule generation norms must not only capture the subtle variations but also should contribute towards lower bit error rate BER, reduced design and time complexity. Here, we propose the design of a fuzzy multilayer perceptron FMLP-based inference engine design for a FN-based MIMO modelling using multiple membership and inference rule generation norms. Experimental results derived show that a set formulated with seven linguistic hedges and six inference states helps in designing a FN MIMO estimator which can be an important element in the design of adaptive receivers for high data rate wireless communication.

Superior neuro-fuzzy classification systems

Although adaptive neuro-fuzzy inference system (ANFIS) has very fast convergence time, it is not suitable for classification problems because its outputs are not integer. In order to overcome this problem, this paper provides four adaptive neuro-fuzzy classifiers; adaptive neuro-fuzzy classifier with linguistic hedges (ANFCLH), linguistic hedges neuro-fuzzy classifier with selected features (LHNFCSF), conjugate gradient neuro-fuzzy classifier (SCGNFC) and speeding up scaled conjugate gradient neuro-fuzzy classifier (SSCGNFC). These classifiers are used to achieve very fast, simple and efficient breast cancer diagnosis. Both SCGNFC and SSCGNFC systems are optimized by scaled conjugate gradient algorithms. In these two systems, k-means algorithm is used to initialize the fuzzy rules. Also, Gaussian membership function is only used for fuzzy set descriptions, because of its simple derivative expressions. The other two systems are based on linguistic hedges (LH) tuned by scaled conjugate gradient. The classifiers performances are analyzed and compared by applying them to breast cancer diagnosis. The results indicated that SCGNFC, SSCGNFC and ANFCLH achieved the same accuracy of 97.6608 % in the training phase while LHNFCSF performed better than other methods in the training phase by achieving an accuracy of 100 %. In the testing phase, the overall accuracies of LHNFCSF achieved 97.8038 %, which is superior also to other methods. Applying LHNFCSF not only reduces the dimensions of the problem, but also improves classification performance by discarding redundant, noise-corrupted or unimportant features. Also, the k-means clustering algorithm was used to determine the membership functions of each feature. LHNFCSF achieved mean RMSE values of 0.0439 in the training phase after feature selection and gives the best testing recognition rates of 98.8304 and 98.0469 during training and testing phases, respectively using two clusters for each class. The results strongly suggest that ANFCLH can aid in the diagnosis of breast cancer and can be very helpful to the physicians for their final decision on their patients.

Basic properties of fuzziness degrees for linguistic hedge.

Basic properties of fuzziness degrees for linguistic hedge.

Formal concept analysis and linguistic hedges

This paper presents an application of linguistic hedges to formal concept analysis of data with fuzzy attributes. Formal concept analysis aims at extraction of particular (bi-)clusters, called formal concepts, from data. The clusters link collections of objects (extents) and attributes (intents), and have a clear interpretation due to a simple verbal description of the concept-forming operators. We insert linguistic hedges such as ‘very’ or ‘extremely’ in the description of the operators. In this way, linguistic hedges become parameters for formal concept analysis that control the number of clusters extracted from data. Namely, as we show theoretically as well as experimentally, stronger hedges result in a smaller number of clusters. The new concept-forming operators form Galois-like connections. We study their properties and axiomatize them. Then, we show that a concept lattice with hedges, i.e. the set of all formal concepts of the new operators is indeed a complete lattice which is isomorphic to a particular ordinary concept lattice. We describe the isomorphism and its inverse. These mappings serve as translation procedures. As a consequence, we obtain a theorem characterizing the structure of concept lattices with hedges which generalizes the well-known main theorem of ordinary concept lattices. The isomorphism and its inverse enable us to compute a concept lattice with hedges using algorithms for ordinary concept lattices. We demonstrate by experiments that when selecting various hedges from the strongest to weaker hedges, the reduction in size of the corresponding concept lattices is smooth. From a broader perspective, we argue that linguistic hedges represent mathematically and computationally a feasible way to parameterize methods for knowledge extraction from data that enable one to emphasize or to suppress extracted patterns while keeping their interpretation.

On terminological issues regarding linguistic hedges

The aim of this short note is to discuss and correct wrong interpretation of ‘very false’ used by Halaš and Botur (2009). We argue that ‘very false’ is antitone unary connective closely related to negation and has nothing to do with monotone linguistic hedges like ‘more or less’ or ‘slightly true’ which are, in fact, used in Halaš and Botur (2009).

Neuro-fuzzy control of underwater vehicle-manipulator systems

This paper presents an intelligent controller for underwater vehicle-manipulator systems (UVMS) based on the neuro-fuzzy approach. The controller is composed of fuzzy PD control with membership function tuning by linguistic hedge. A neural network compensator approximates the dynamics of the UVMS in decentralized form. The new controller has the advantages of simplicity of implementation due to decentralized design, precision, and robustness to payload variations and hydrodynamic disturbances. It has significantly low energy consumption compared to both the conventional PD and conventional fuzzy control methods. The effectiveness of the proposed controller is illustrated by results of simulations for a six degrees of freedom autonomous underwater vehicle with a three degrees of freedom on-board manipulator.

Application of hedge algebra-based fuzzy controller to active control of a structure against earthquake

SUMMARY Active control problems of seismic-excited civil structures have attracted considerable attention in recent years. In this study, a design procedure of the hedge algebra-based fuzzy controller (HAFC) is investigated for a class of high-rise building systems with active tendons, which are subjected to seismic activity. In the HAFC, semantics of linguistic terms is obtained through semantically quantifying mappings (SQMs) without using any fuzzy set. Hence, the design of a HAFC leads to determining parameters of SQMs, which are fuzziness measures of primary terms and linguistic hedges occurring in a fuzzy model. To prove the validity of the HAFC in the aforementioned problem, an earthquake-excited 10-floor building with two actuators is numerically simulated. Copyright © 2011 John Wiley & Sons, Ltd.

Novel Model for the Computation of Linguistic Hedges in Database Queries

Most query languages are designed to retrieve information from databases containing precise and certain data using precisely specified commands. Due to the advancements in various kinds of data repositories in the recent years, there is a steep increase in complex queries. Most of the complex Queries are uncertain and vague. The existing Structured Query Language exhibits its inefficiency in handling these complex Queries. This paper proposes a model to handle the complexities by using fuzzy set theory. In this model, the Fuzzy Query with linguistic hedges is converted into Crisp Query, by deploying an application layer over the Structured Query Language.

Bivalent and other solutions of fuzzy relational equations via linguistic hedges

We show that the well-known results regarding solutions of fuzzy relational equations and their systems can easily be generalized to obtain criteria regarding constrained solutions such as solutions which are crisp relations. When the constraint is empty, constrained solutions are ordinary solutions. The generalization is obtained by employing intensifying and relaxing linguistic hedges, conceived in this paper as certain unary functions on the scale of truth degrees. One aim of the paper is to highlight the problem of constrained solutions and to demonstrate that this problem naturally appears when identifying unknown relations. The other is to emphasize the role of linguistic hedges as constraints.