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Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on

Date 7-10 May 2000

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  • The Ninth IEEE International Conference on Fuzzy Systems [front matter]

    Page(s): i - xx
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    Freely Available from IEEE
  • Author index

    Page(s): 1079 - 1080
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    Freely Available from IEEE
  • Fuzzy relational products in information processing: classical products and new products

    Page(s): 981 - 984 vol.2
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    This paper summarizes and compares different fuzzy relational products. First, the classical circle product is discussed. Then, another set of classical products, triangle subproduct, triangle superproduct, and square product are revisited for both harsh and soft versions. Afterward, two new fuzzy relational products developed by the authors, namely similarity product and star product are described. These new products have been developed from practical viewpoints of certain applications for which the semantics of the classical products has proven inadequate. The products' definitions and semantics are given, and compared with those of the classical ones where appropriate View full abstract»

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  • Mining approximate dependency to answer null queries on similarity-based fuzzy relational databases

    Page(s): 615 - 620 vol.2
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    Null queries are queries that elicit a null answer from the database. In possibility-based fuzzy relational database model, a theoretical framework utilizing analogical reasoning and fuzzy functional dependency to answer null queries has been proposed by Dutta (1991). However, no searching algorithm is provided to discover the fuzzy functional dependencies among attributes. In this work, we extend the concept of fuzzy functional dependency to approximate dependency on similarity-based fuzzy relational data model. In addition, we proposed a data mining algorithm to discover all the approximate dependencies among attributes. It therefore can automatically obtain approximate answers for null queries and missing data values in an incomplete database. This kind of facility will certainly improve the cooperative nature of databases and enhance the user-friendliness of the database systems View full abstract»

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  • A fuzzy Kohonen classifier

    Page(s): 572 - 576 vol.2
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    Several ways of combining concepts of fuzzy set theory with connectionist methods are known. We focus on the use of fuzzy numbers in neural networks. Our goal is to create a fully fuzzified Kohonen-layer which receives fuzzy numbers as inputs and computes its output employing fuzzy weights. The main problem is the determination of the winning neuron by the exclusive use of special, “monotonic” fuzzy operations, which guarantee a certain “goodness” of the input/output behaviour. A selection-function is introduced for solving this problem. Furthermore, we formulate a fuzzified version of the standard learning rule, that can be applied on the fuzzified Kohonen neurons View full abstract»

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  • Predictive fuzzy PID control for complex processes

    Page(s): 544 - 548 vol.2
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    In this paper, a new structure of predictive fuzzy PID controller is proposed. The new controller is robust and effective in controlling higher order and time-delayed complex processes. The proposed predictive fuzzy PID controller combines the fuzzy PID and generalized predictive control (GPC) ideas together, and is equipped with optimization capability that minimizes a cost function. These features make the new controller more effective than individual fuzzy PID and GPC controllers in handling nonlinear systems. Computer simulations are shown for various types of process stabilization and set point tracking problems View full abstract»

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  • T-S fuzzy model with linear rule consequence and PDC controller: a universal framework for nonlinear control systems

    Page(s): 549 - 554 vol.2
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    We present two results concerning the fuzzy modeling and control of nonlinear systems. The first result is on the approximation of smooth nonlinear dynamical systems using linear Takagi-Sugeno (T-S) fuzzy models. The second result is on the approximation of smooth nonlinear state-feedback controllers using the so-called parallel distributed compensation (PDC) controller. Both results are based on the effectiveness of using linear Takagi-Sugeno systems to approximate nonlinear function, which is also proved in this paper View full abstract»

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  • Analysis and design of fuzzy robust observer for uncertain nonlinear systems

    Page(s): 993 - 996 vol.2
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    This paper addresses the analysis and the design of fuzzy robust observer for a class of uncertain nonlinear systems on the basis of Takagi-Sugeno fuzzy model, which considers both the states unobservable problem and the uncertainty problem of fuzzy model. It is shown that the designed robust observers possess the stability and the state estimation errors converge to zero View full abstract»

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  • Incorporating fuzzy logic to reinforcement learning [mobile robot navigation]

    Page(s): 847 - 852 vol.2
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    Proposes a sensor-based navigation method that utilizes fuzzy logic in reinforcement learning algorithms for navigation of a mobile robot in uncertain environments. The sonar readings are codified in distance notions by fuzzy sets and a modification in the R-learning algorithm by incorporating fuzzy logic is proposed. Fuzzy logic is used for weighting the immediate reward value, that is a variable present in most reinforcement learning algorithms. The effectiveness of the modified algorithm, R'-learning, is verified in several tests and compared to the performance of the R-learning algorithm View full abstract»

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  • Local and global identification and interpretation of parameters in Takagi-Sugeno fuzzy models

    Page(s): 835 - 840 vol.2
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    Addresses the interpretation of parameters in Takagi-Sugeno (TS) fuzzy models. The analysis is presented for the dynamic gain and steady-state representation, but it holds for parameters related to the dynamics as well. The TS model interpolates between local linear models. The overall gain obtained by interpolating the gains of the local models can be interpreted as the local dynamic gain of the entire fuzzy model. This locally interpreted gain is not identical to the dynamic gain obtained by linearization of the fuzzy model at the considered equilibrium. We analyze the origin of this difference with regard to the applied identification method. In order to keep the analysis simple and transparent, a fuzzy model of a Hammerstein system is studied. The results show that fuzzy models obtained by local identification (weighted least squares for each rule) typically yields a poor steady-state representation and the model can only be locally interpreted. On the contrary, a fuzzy model obtained by global identification (one least-square solution for the entire model) can result in a qualitatively bad local interpretation of the gain even though approximates the real process well. Therefore, this model can only be used for prediction or local linearization through Taylor expansion. It is shown that the difference between the globally and locally interpreted gain can be reduced by using a priori knowledge in global identification. The steady-state representation of fuzzy models obtained by local identification can be improved by using inference based on the smoothed maximum operator (instead of the weighted mean) View full abstract»

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  • Model free online adaptive feedback control with FuNe I AFC neuro-fuzzy system

    Page(s): 997 - 1000 vol.2
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    FuNe I AFC fuzzy system is useful in mapping into a neural network that utilises the advantages of both neural and fuzzy systems. FuNe I architecture, previously used in classification applications, had been modified for feedback control adding a feedback at its output. The simulation results show that adaptive control of a real plant can be achieved without any prior knowledge of plant using this technique. Authors are currently developing optimization algorithms for the proposed architecture View full abstract»

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  • Using the transformed data to construct an extension-based fuzzy inference model

    Page(s): 823 - 828 vol.2
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    Adjusting the membership functions to satisfy one pattern may deteriorate the inference outcomes of the others. This incompatible issue can be retarded by the extension theory. A novel extension-based fuzzy modeling method, which differs from the traditional fuzzy inference, is proposed. Instead of directly applying the given data to building the fuzzy model, the given data are transformed to another domain by a sigmoidal function to obtain a better fuzzy model. We also define the extended correlation functions to relate the data with the fuzzy sets. During the refining process, the extended fuzzy model, which considers the positive and negative sets simultaneously, is adjusted by the gradient descent method. Simulation results from both single-input-single-output and double-input-single-output systems verified that better results than the conventional methods can be obtained View full abstract»

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  • A fuzzy-based heuristic measure evaluating quality of a concept partition: application to SAINTETIQ, a database summarization system

    Page(s): 957 - 960 vol.2
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    We present an original fuzzy-based heuristic measure, the partition quality, highly integrated to a general database summarization framework. Our system SAINTETIQ is based on an incremental conceptual clustering algorithm, building a concept hierarchy from database observations. Concepts of the hierarchy are summaries of a part of the database, at different levels of abstraction. The algorithm roughly consists in applying learning operators on the hierarchy as new observations arrive into the system. The decision function named partition quality that we introduce, reflects contrast and typicity of a concept partition and allows the system to select the best operator to apply during the construction of the concept hierarchy View full abstract»

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  • SVD based reduction for subdivided rule bases

    Page(s): 712 - 716 vol.2
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    This paper is motivated by the fact that though fuzzy and B-spline techniques are popular engineering tools, their utilisation is being restricted by their exponential complexity property. As a result SVD based reduction techniques have emerged. These methods apply singular value decomposition to the characteristic matrix of the rule base. The maximum size of the rule base taken into consideration is limited by size of operation memory available for singular value decomposition. The method proposed in this paper is capable of applying singular value decomposition step by step to the partitions of the rule base. Therefore, using the proposed extension, there is no limit, theoretically, for the size of the rule bases View full abstract»

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  • Motion planning of an autonomous mobile robot by integrating GAs and fuzzy logic control

    Page(s): 933 - 936 vol.2
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    The aim of the paper is to determine optimal motion planning for an autonomous mobile robot (AMR) moving in an environment with obstacles. We propose two motion planning methods, one is the linear vertex decision mechanism (LVDM) and the other is the fuzzy logic decision mechanism (FLDM). Computer simulations are explored to compare the performance of these two mechanisms. Furthermore, we apply genetic algorithms (GAs) to the LVDM to select the optimal weighting factor and we also adopt GAs in the FLDM such that the best membership function and/or the number of fuzzy control rules can be obtained. Computer simulations of the evolved LVDM and FLDM with GAs are also provided. All the simulations demonstrate that the proposed schemes can indeed guide the AMR even if the environment is filled with obstacles View full abstract»

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  • Fuzzy based sliding manifolds for identification of a class of nonlinear systems

    Page(s): 841 - 846 vol.2
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    A variable-structure based fuzzy-logic identifier (VSFI) is introduced to model a class of black-box nonlinear systems. The proposed identifier adopts a serial-parallel structure and, unlike most fuzzy identifiers, does not require measurements of all the system's states. Based on output measurements, the system states are estimated using a high gain observer. It is shown that the proposed VSFI is stable provided that the system identified is stable. Furthermore, we show that the estimator state-errors converge exponentially to an arbitrarily small ball. Simulation results illustrates that the identification scheme proposed might serve as a potential candidate for nonlinear system identification View full abstract»

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  • Fuzzy types: softening structures

    Page(s): 774 - 779 vol.2
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    The requirements of complex applications in the world of object oriented databases have motivated the study of the addition of vagueness to the existing models, giving rise to different approaches. The presence of vagueness can be considered in the type associated to a class, parallel and independently from a fuzzy view of the set of objects that belong to the class. This paper offers a new perspective for representing the type associated to a class, tackling the problem of vagueness in the database schema and defining the concept of fuzzy type. Two different components of these types are defined: the structural component and the behavior component. An adaptation of the mechanism of instantiation and inheritance is presented, considering adequate union operators View full abstract»

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  • Servicebots-a scalable architecture for autonomous service robots

    Page(s): 1013 - 1016 vol.2
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    The proposed architecture is designed for a group of service robots operating in structured environments. Although each robot is an autonomous systems, the group of service robots is coordinated to ensure a reliable service delivery. The relationship between the autonomy of behavior of each robot and the coordination of the team is the backbone of the proposed architecture. We show how aspects related to high availability scalability and reliability can be insured while keeping a decision autonomy of the robots. We conclude our paper by comparing our architecture with the SAPHIRA reference architecture View full abstract»

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  • Thau-Luenberger observers for TS fuzzy systems

    Page(s): 671 - 676 vol.2
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    Deals with local fuzzy Thau-Luenberger observers for nonlinear plants. A nonlinear plant is approximated by a Takagi-Sugeno (TS) fuzzy multiple model with affine subsystems for which local linear fuzzy observers are constructed. Analysis of stability conditions for the complete observer is made and a design procedure based on linear matrix inequalities is devised. A simulation example is also given for an open loop model View full abstract»

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  • Fuzzy advantage learning

    Page(s): 865 - 870 vol.2
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    Based on advantage learning and fuzzy inference systems (FISs), fuzzy advantage learning (FAL) is presented. In FAL, a FIS is applied to introduce strong generalization ability and to generate continuous actions for advantage learning. This approach is illustrated and compared to other reinforcement learning methods. The simulation study on inverted pendulum control shows its effectiveness with faster learning speed View full abstract»

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  • Collective choice rules under linguistic preferences: an example of the computing with words/perceptions paradigm

    Page(s): 786 - 791 vol.2
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    Originally, most of the popular group decision making rules were conceived for classical (crisp) preference relations (orderings), and then extended to the case of traditional fuzzy preference relations. We propose to further extend them to linguistic preference relations. A Linguistic OWA operators guided aggregation of preferences is employed. The approach proposed is an example of the use of the new paradigm of computing with words/perceptions View full abstract»

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  • A proposal for a model for dealing with value-based data dependencies to improve the rule discovery process

    Page(s): 1025 - 1028 vol.2
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    The discovery of conjunctive “if-then” classification rules may be intractable when enumerating all possible conjunctions of terms. Various algorithms, notably C4.5 and CART, adopt a univariate strategy which reduces the process to a one-at-a-time best variable type of approach. While computationally feasible, such an approach may lead to unexplored portions of the database which may contain valuable nuggets. On the other hand, an exhaustive evaluation of all possible conjunctions may be intractable even for relatively small datasets. We propose a general approach to reduce the size of the search space of conjunctive “if-then” rule discovery algorithms by exploiting value-based data dependencies existing among the independent variables View full abstract»

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  • Fuzzy PERT in series-parallel graphs

    Page(s): 717 - 722 vol.2
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    This paper deals with the fuzzy project scheduling approach, where fuzzy intervals model uncertain durations of tasks. While it is easy to compute fuzzy earliest starting dates of tasks in the critical path method, the problem of determining latest starting dates and slack times is much more tricky and has never been solved in a fully satisfactory manner in the past. Here, we propose a rigorous treatment of this problem in series-parallel graphs, in the framework of possibility theory. The main difficulty lies in the fact that the variation of latest starting dates and slack times, as a function of task durations, is not straightforward to predict for general graph topologies. However, it is easier in the case of series-parallel graphs. The case of interval-valued durations is first addressed, and then extended to fuzzy intervals View full abstract»

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  • Functional dependencies in extended possibility-based fuzzy relational databases

    Page(s): 929 - 932 vol.2
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    Based on the fuzzy relational databases where fuzziness of data appears in attribute values in the forms of possibility attributions as well as proximity relations in attribute domain elements, called extended possibility-based fuzzy relational databases, a notion of semantic equivalence of fuzzy data is introduced in this paper. Following this notion, we focus on the issues of fuzzy functional dependencies. A set of sound inference rules, which are similar to Armstrong's axioms (1974) for classical cases, for fuzzy functional dependencies, are proposed View full abstract»

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  • Basic algebra of BK products of relations in t-norm fuzzy logics

    Page(s): 599 - 604 vol.2
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    Relational computing structures make it possible to perform knowledge representation as well as all the computations in intelligent systems in a unified way. When crisp computations are replaced by fuzzy relational computations, it is possible to improve significantly handling of indeterminacy and incompleteness of information. Fuzzy computational structures in which cuts commute with closures over relational properties, provide in addition the means for significant data compression, resulting in significant speedup of computations. BK-relational products provide axiomatics necessary for constructive computational procedures that rigorously satisfy the above formulated requirements. This paper provides the mathematical overview of concepts needed for fuzzy relational computations and the rigorous mathematical proofs of inequalities relating triangle BK-products to standard t-norm based compositions of relations. The proofs of correctness of these crucial computational relationships is performed in basic logic of Hajek (1996, 1998), which is the fuzzy predicate logic axiomatization of t-norm based residuated fuzzy logics. The paper concludes with a survey of the use of BK-products in scientific, medical, engineering and business applications, information retrieval, automated reasoning, etc View full abstract»

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