Showing papers on "Fuzzy logic published in 2005"
22 May 2005
TL;DR: In this article, a new type of identity-based encryption called Fuzzy Identity-Based Encryption (IBE) was introduced, where an identity is viewed as set of descriptive attributes, and a private key for an identity can decrypt a ciphertext encrypted with an identity if and only if the identities are close to each other as measured by the set overlap distance metric.
Abstract: We introduce a new type of Identity-Based Encryption (IBE) scheme that we call Fuzzy Identity-Based Encryption. In Fuzzy IBE we view an identity as set of descriptive attributes. A Fuzzy IBE scheme allows for a private key for an identity, ω, to decrypt a ciphertext encrypted with an identity, ω ′, if and only if the identities ω and ω ′ are close to each other as measured by the “set overlap” distance metric. A Fuzzy IBE scheme can be applied to enable encryption using biometric inputs as identities; the error-tolerance property of a Fuzzy IBE scheme is precisely what allows for the use of biometric identities, which inherently will have some noise each time they are sampled. Additionally, we show that Fuzzy-IBE can be used for a type of application that we term “attribute-based encryption”.
In this paper we present two constructions of Fuzzy IBE schemes. Our constructions can be viewed as an Identity-Based Encryption of a message under several attributes that compose a (fuzzy) identity. Our IBE schemes are both error-tolerant and secure against collusion attacks. Additionally, our basic construction does not use random oracles. We prove the security of our schemes under the Selective-ID security model.
3,610 citations
Book•
01 Mar 2005
1,285 citations
TL;DR: generalized concepts of differentiability (of any order n@?N), which solves this shortcoming of fuzzy number differentiability, are introduced and some concrete applications to partial and ordinary fuzzy differential equations with fuzzy input data of the form c@?g(x).
911 citations
01 Jun 2005
TL;DR: Experimental results, provided by the proposed algorithm for a set of standard test functions, outperformed those of the standard differential evolution algorithm for optimization problems with higher dimensionality.
Abstract: The differential evolution algorithm is a floating-point encoded evolutionary algorithm for global optimization over continuous spaces. The algorithm has so far used empirically chosen values for its search parameters that are kept fixed through an optimization process. The objective of this paper is to introduce a new version of the Differential Evolution algorithm with adaptive control parameters – the fuzzy adaptive differential evolution algorithm, which uses fuzzy logic controllers to adapt the search parameters for the mutation operation and crossover operation. The control inputs incorporate the relative objective function values and individuals of the successive generations. The emphasis of this paper is analysis of the dynamics and behavior of the algorithm. Experimental results, provided by the proposed algorithm for a set of standard test functions, outperformed those of the standard differential evolution algorithm for optimization problems with higher dimensionality.
822 citations
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TL;DR: This work designs classifiers based on the well-known fisherface method and demonstrates that the proposed method comes with better performance when compared with other template-based techniques and shows substantial insensitivity to large variation in light direction and facial expression.
679 citations
16 May 2005
TL;DR: A fuzzy logic approach to cluster-head election is proposed based on three descriptors-energy, concentration and centrality and shows that depending upon network configuration, a substantial increase in network lifetime can be accomplished as compared to probabilistically selecting the nodes as cluster-heads using only local information.
Abstract: Wireless sensor networks (WSNs) present a new generation of real-time embedded systems with limited computation, energy and memory resources that are being used in a wide variety of applications where traditional networking infrastructure is practically infeasible. Appropriate cluster-head node election can drastically reduce the energy consumption and enhance the lifetime of the network. In this paper, a fuzzy logic approach to cluster-head election is proposed based on three descriptors-energy, concentration and centrality. Simulation shows that depending upon network configuration, a substantial increase in network lifetime can be accomplished as compared to probabilistically selecting the nodes as cluster-heads using only local information.
552 citations
TL;DR: This fuzzy expert system provides vulnerability estimates that correlate with observed declines more closely than previous methods, and has advantages in flexibility of input data requirements, in the explicit representation of uncertainty, and in the ease of incorporating new knowledge.
449 citations
TL;DR: It is argued that recurrent fuzzy relationships, which were simply ignored in previous studies, should be considered in forecasting and recommended that different weights be assigned to various fuzzy relationships.
Abstract: This study proposes weighted models to tackle two issues in fuzzy time series forecasting, namely, recurrence and weighting. It is argued that recurrent fuzzy relationships, which were simply ignored in previous studies, should be considered in forecasting. It is also recommended that different weights be assigned to various fuzzy relationships. In previous studies, these fuzzy relationships were treated as if they were equally important, which might not have properly reflected the importance of each individual fuzzy relationship in forecasting. The weighted models are compared with the local regression models in which weight functions also play an important role. Both models are different by nature, but certain theoretical backgrounds in local regression models are adopted. By using the Taiwan stock index as the forecasting target, the empirical results show that the weighted model outperforms one of the conventional fuzzy time series models.
444 citations
12 Sep 2005
TL;DR: A heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control that is transparent to, and easily "tweaked" by, the prosthetist/clinician is presented.
Abstract: This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and easily "tweaked" by, the prosthetist/clinician. Other algorithms in current literature assume a longer period of unperceivable delay, while the system we present has an update rate of 45.7 ms with little postprocessing time, making it suitable for real-time application. Five subjects were investigated (three with intact limbs, one with a unilateral transradial amputation, and one with a unilateral transradial limb-deficiency from birth). Four subjects were used for system offline analysis, and the remaining intact-limbed subject was used for system real-time analysis. We discriminated between four EMG patterns for subjects with intact limbs, and between three patterns for limb-deficient subjects. Overall classification rates ranged from 94% to 99%. The fuzzy algorithm also demonstrated success in real-time classification, both during steady state motions and motion state transitioning. This functionality allows for seamless control of multiple degrees-of-freedom in a multifunctional prosthesis.
423 citations
TL;DR: State-of-the-art techniques for identifying fuzzy models and designing model-based controllers are reviewed in this article and attention is paid to the role of fuzzy systems in higher levels of the control hierarchy.
397 citations
TL;DR: In this article, the fuzzy multi-attribute AD approach is also developed and it is compared by one of fuzzy AHP methods in the literature, and the selection process has been accomplished by aiding a software that includes crisp AD and fuzzy AD.
01 Oct 2005
TL;DR: The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization and an experimental website is constructed to test the approach.
Abstract: In this paper, a fuzzy ontology and its application to news summarization are presented. The fuzzy ontology with fuzzy concepts is an extension of the domain ontology with crisp concepts. It is more suitable to describe the domain knowledge than domain ontology for solving the uncertainty reasoning problems. First, the domain ontology with various events of news is predefined by domain experts. The document preprocessing mechanism will generate the meaningful terms based on the news corpus and the Chinese news dictionary defined by the domain expert. Then, the meaningful terms will be classified according to the events of the news by the term classifier. The fuzzy inference mechanism will generate the membership degrees for each fuzzy concept of the fuzzy ontology. Every fuzzy concept has a set of membership degrees associated with various events of the domain ontology. In addition, a news agent based on the fuzzy ontology is also developed for news summarization. The news agent contains five modules, including a retrieval agent, a document preprocessing mechanism, a sentence path extractor, a sentence generator, and a sentence filter to perform news summarization. Furthermore, we construct an experimental website to test the proposed approach. The experimental results show that the news agent based on the fuzzy ontology can effectively operate for news summarization.
TL;DR: Failure of oil and gas transmission pipelines was analyzed by fault tree analysis and the proposed method, which combined expert elicitation with fuzzy set theories to evaluate probability of the events, is effective to treat fuzzy events of FTA.
Abstract: Failure of oil and gas transmission pipelines was analyzed by fault tree analysis in this paper. According to failure modes of pipeline: leakage and rupture, a fault tree of the pipeline was constructed. Fifty-five minimal cut sets of the fault tree had been achieved by qualitative analysis, while the failure probability of top event and the important analyses of basic events were evaluated by quantitative analysis. In conventional fault tree analysis, probabilities of the basic events were treated as precise values, which could not reflect real situation of system because of ambiguity and imprecision of some basic events. In order to overcome this disadvantage, a new method was proposed which combined expert elicitation with fuzzy set theories to evaluate probability of the events. As an example, failure probability of pipeline installation was assessed by using the proposed method, achieving its fuzzy failure probability of 6.4603×10 −3 . The method given in this article is effective to treat fuzzy events of FTA.
21 Aug 2005
TL;DR: This paper enhances the density-based clustering algorithm DBSCAN so that it can work directly on these fuzzy distance functions, and proposes to express the similarity between two fuzzy objects by distance probability functions.
Abstract: In many different application areas, e.g. sensor databases, location based services or face recognition systems, distances between odjects have to be computed based on vague and uncertain data. Commonly, the distances between these uncertain object descriptions are expressed by one numerical distance value. Based on such single-valued distance functions standard data mining algorithms can work without any changes. In this paper, we propose to express the similarity between two fuzzy objects by distance probability functions. These fuzzy distance functions assign a probability value to each possible distance value. By integrating these fuzzy distance functions directly into data mining algorithms, the full information provided by these functions is exploited. In order to demonstrate the benefits of this general approach, we enhance the density-based clustering algorithm DBSCAN so that it can work directly on these fuzzy distance functions. In a detailed experimental evaluation based on artificial and real-world data sets, we show the characteristics and benefits of our new approach.
15 Oct 2005
TL;DR: In this paper, the grade of membership model is proposed as a straightforward way of performing fuzzy cluster analysis, which has a long history of usage in other contexts and is illustrated by an example involving gene expression data.
Abstract: Usually in cluster analysis, an object is a member of one and only one cluster, a property described as ‘crisp’ membership. Fuzzy cluster analysis allows an object to have partial membership in more than one cluster. Selecting a good membership function is important to the success of the methods. The Grade of Membership model – which has a long history of usage in other contexts – is proposed as a straightforward way of performing fuzzy cluster analysis. The Grade of Membership model is illustrated by an example involving gene expression data.
Keywords:
Fuzzy cluster;
grade of membership;
membership function
TL;DR: A two-stage logarithmic goal programming (TLGP) method is proposed to generate weights from interval comparison matrices, which can be either consistent or inconsistent, and is applicable to fuzzy comparison matrix when they are transformed into interval comparison Matrices using @a-level sets and the extension principle.
01 Jan 2005
TL;DR: The universality of the normal cloud model is proved, which is more superior and easier, and can fit the fuzziness and gentleness of human cognitive processing and be more applicable and universal in the representation of uncertain notions.
Abstract: The distribution function is an important tool in the study of the stochastic variances. The normal distribution is very popular in the nature and our society. The idea of membership functions is the foundation of the fuzzy sets theory. While the fuzzy theory is widely used, the completely certain membership function that has no any fuzziness at all has been the bottleneck of the applications of this theory.Cloud models are the effective tools in transforming between qualitative concepts and their quantitative expressions. It can represent the fuzziness and randomness and their relations of uncertain concepts. Also cloud models can show the concept granularity in multi-scale spaces by the digital characteristic Entropy (En). The normal cloud model not only broadens the form conditions of the normal distribution but also makes the normal membership function be the expectation of the random membership degree. In this paper, the universality of the normal cloud model is proved, which is more superior and easier, and can fit the fuzziness and gentleness of human cognitive processing.It would be more applicable and universal in the representation of uncertain notions.
TL;DR: A new thresholding technique is introduced which processes thresholds as type II fuzzy sets and a new measure of ultrafuzziness is also introduced and experimental results using laser cladding images are provided.
TL;DR: In this article, an approach using fuzzy logic is proposed to evaluate the criticality or risk associated with item failure modes, which makes use of well-defined rule base and fuzzy logic operations to determine criticality/riskiness level of the failure.
Abstract: Purpose – To permit the system safety and reliability analysts to evaluate the criticality or risk associated with item failure modes.Design/methodology/approach – The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (Sf), severity (S) and detectability (Sd) of an item failure mode. Because of the subjective and qualitative nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic is proposed. In the proposed approach, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and are evaluated in fuzzy inference engine, which makes use of well‐defined rule base and fuzzy logic operations to determine the criticality/riskiness level of the failure. The fuzzy conclusion is then defuzzified to get risk priority number. The higher the value of RPN, the greater will be the risk and lower the value of RPN, and the lesser will be the ris...
Book•
01 Jul 2005
TL;DR: Part I: Introduction Computational Intelligence: An Introduction Traditional Problem Definition Part II: Basic Intelligent Computational Technologies Neural Networks Approach Fuzzy Logic Approach Evolutionary Computation Part III: Hybrid Computational technologies Neuro-fuzzy Approach Transparent FuzzY/Neuro-fBuzzy Modeling Evolving Neural and Fuzzed Systems Adaptive Genetic Algorithms Part IV: Recent Developments
Abstract: Part I: Introduction Computational Intelligence: An Introduction Traditional Problem Definition Part II: Basic Intelligent Computational Technologies Neural Networks Approach Fuzzy Logic Approach Evolutionary Computation Part III: Hybrid Computational Technologies Neuro-fuzzy Approach Transparent Fuzzy/Neuro-fuzzy Modeling Evolving Neural and Fuzzy Systems Adaptive Genetic Algorithms Part IV: Recent Developments The State of the Art and Development Trends
TL;DR: The proposed fuzzy multiple attribute decision-making method is a generalised model, which can be applied to great variety of practical problems encountered in the naval architecture from propulsion/manoeuvring system selection to warship requirements definition.
01 Jan 2005
TL;DR: The results show that the proposed system has outperformed the other approaches, while operating online in a life-long mode to realize the ambient intelligence vision.
Abstract: We describe a novel life-long learning approach for intelligent agents that are embedded in intelligent environments. The agents aim to realize the vision of ambient intelligence in intelligent inhabited environments (IIE) by providing ubiquitous computing intelligence in the environment supporting the activities of the user. An unsupervised, data-driven, fuzzy technique is proposed for extracting fuzzy membership functions and rules that represent the user's particularized behaviors in the environment. The user's learned behaviors can then be adapted online in a life-long mode to satisfy the different user and system objectives. We have performed unique experiments in which the intelligent agent has learned and adapted to the user's behavior, during a stay of five consecutive days in the intelligent dormitory (iDorm), which is a real ubiquitous computing environment test bed. Both offline and online experimental results are presented comparing the performance of our technique with other approaches. The results show that our proposed system has outperformed the other approaches, while operating online in a life-long mode to realize the ambient intelligence vision.
TL;DR: This study presents an interval-parameter fuzzy two-stage stochastic programming (IFTSP) method for the planning of water-resources-management systems under uncertainty and demonstrates how the method efficiently produces stable solutions together with different risk levels of violating pre-established allocation criteria.
TL;DR: A method of maximum power point tracking using adaptive fuzzy logic control for grid-connected photovoltaic systems and can deliver more power than the conventional fuzzy logic controller.
TL;DR: The purpose is the design of a full-order fuzzy dynamic output feedback controller which ensures the robust asymptotic stability of the closed-loop system and guarantees an H/sub /spl infin// norm bound constraint on disturbance attenuation for all admissible uncertainties.
Abstract: This work investigates the problem of robust output feedback H/sub /spl infin// control for a class of uncertain discrete-time fuzzy systems with time delays. The state-space Takagi-Sugeno fuzzy model with time delays and norm-bounded parameter uncertainties is adopted. The purpose is the design of a full-order fuzzy dynamic output feedback controller which ensures the robust asymptotic stability of the closed-loop system and guarantees an H/sub /spl infin// norm bound constraint on disturbance attenuation for all admissible uncertainties. In terms of linear matrix inequalities (LMIs), a sufficient condition for the solvability of this problem is presented. Explicit expressions of a desired output feedback controller are proposed when the given LMIs are feasible. The effectiveness and the applicability of the proposed design approach are demonstrated by applying this to the problem of robust H/sub /spl infin// control for a class of uncertain nonlinear discrete delay systems.
TL;DR: Stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied and Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages.
Abstract: The ordinary Takagi-Sugeno (TS) fuzzy models have provided an approach to represent complex nonlinear systems to a set of linear sub-models by using fuzzy sets and fuzzy reasoning. In this paper, stochastic fuzzy Hopfield neural networks with time-varying delays (SFVDHNNs) are studied. The model of SFVDHNN is first established as a modified TS fuzzy model in which the consequent parts are composed of a set of stochastic Hopfield neural networks with time-varying delays. Secondly, the global exponential stability in the mean square for SFVDHNN is studied by using the Lyapunov-Krasovskii approach. Stability criterion is derived in terms of linear matrix inequalities (LMIs), which can be effectively solved by some standard numerical packages.
01 Apr 2005
TL;DR: A hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems is proposed and shows that the hybrid algorithm has higher search ability.
Abstract: We propose a hybrid algorithm of two fuzzy genetics-based machine learning approaches (i.e., Michigan and Pittsburgh) for designing fuzzy rule-based classification systems. First, we examine the search ability of each approach to efficiently find fuzzy rule-based systems with high classification accuracy. It is clearly demonstrated that each approach has its own advantages and disadvantages. Next, we combine these two approaches into a single hybrid algorithm. Our hybrid algorithm is based on the Pittsburgh approach where a set of fuzzy rules is handled as an individual. Genetic operations for generating new fuzzy rules in the Michigan approach are utilized as a kind of heuristic mutation for partially modifying each rule set. Then, we compare our hybrid algorithm with the Michigan and Pittsburgh approaches. Experimental results show that our hybrid algorithm has higher search ability. The necessity of a heuristic specification method of antecedent fuzzy sets is also demonstrated by computational experiments on high-dimensional problems. Finally, we examine the generalization ability of fuzzy rule-based classification systems designed by our hybrid algorithm.
TL;DR: Simulations show that the SOFNN has the capability to encode fuzzy rules in the resulting network, based on new adding and pruning techniques and a recursive learning algorithm.
TL;DR: An extension of the classic Yager's approach to involve more sophisticated criteria of goodness, search methods, etc, and shows how fuzzy queries are related to linguistic summaries, which makes it possible to implement such linguistic data summaries.
01 Jun 2005
TL;DR: The aim is to design a mode-independent fuzzy controller such that the closed-loop Markovian jump fuzzy system (MJFS) is robustly stochastically stable and derived for the uncertain MJFS in terms of linear matrix inequalities (LMIs).
Abstract: This paper is concerned with the robust-stabilization problem of uncertain Markovian jump nonlinear systems (MJNSs) without mode observations via a fuzzy-control approach. The Takagi and Sugeno (T-S) fuzzy model is employed to represent a nonlinear system with norm-bounded parameter uncertainties and Markovian jump parameters. The aim is to design a mode-independent fuzzy controller such that the closed-loop Markovian jump fuzzy system (MJFS) is robustly stochastically stable. Based on a stochastic Lyapunov function, a robust-stabilization condition using a mode-independent fuzzy controller is derived for the uncertain MJFS in terms of linear matrix inequalities (LMIs). A new improved LMI formulation is used to alleviate the interrelation between the stochastic Lyapunov matrix and the system matrices containing controller variables in the derivation process. Finally, a simulation example is presented to illustrate the effectiveness of the proposed design method.