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Sample records for controladores fuzzy aplicados

  1. Fuzzy Commitment

    NASA Astrophysics Data System (ADS)

    Juels, Ari

    The purpose of this chapter is to introduce fuzzy commitment, one of the earliest and simplest constructions geared toward cryptography over noisy data. The chapter also explores applications of fuzzy commitment to two problems in data security: (1) secure management of biometrics, with a focus on iriscodes, and (2) use of knowledge-based authentication (i.e., personal questions) for password recovery.

  2. Fuzzy logic

    NASA Technical Reports Server (NTRS)

    Zadeh, Lofti A.

    1988-01-01

    The author presents a condensed exposition of some basic ideas underlying fuzzy logic and describes some representative applications. The discussion covers basic principles; meaning representation and inference; basic rules of inference; and the linguistic variable and its application to fuzzy control.

  3. Fuzzy jets

    NASA Astrophysics Data System (ADS)

    Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; Stansbury, Conrad

    2016-06-01

    Collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets. To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets, are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet tagging variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.

  4. Fuzzy jets

    DOE PAGESBeta

    Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel; Stansbury, Conrad

    2016-06-01

    Here, collimated streams of particles produced in high energy physics experiments are organized using clustering algorithms to form jets . To construct jets, the experimental collaborations based at the Large Hadron Collider (LHC) primarily use agglomerative hierarchical clustering schemes known as sequential recombination. We propose a new class of algorithms for clustering jets that use infrared and collinear safe mixture models. These new algorithms, known as fuzzy jets , are clustered using maximum likelihood techniques and can dynamically determine various properties of jets like their size. We show that the fuzzy jet size adds additional information to conventional jet taggingmore » variables in boosted topologies. Furthermore, we study the impact of pileup and show that with some slight modifications to the algorithm, fuzzy jets can be stable up to high pileup interaction multiplicities.« less

  5. Hybrid fuzzy regression with trapezoidal fuzzy data

    NASA Astrophysics Data System (ADS)

    Razzaghnia, T.; Danesh, S.; Maleki, A.

    2011-12-01

    In this regard, this research deals with a method for hybrid fuzzy least-squares regression. The extension of symmetric triangular fuzzy coefficients to asymmetric trapezoidal fuzzy coefficients is considered as an effective measure for removing unnecessary fuzziness of the linear fuzzy model. First, trapezoidal fuzzy variable is applied to derive a bivariate regression model. In the following, normal equations are formulated to solve the four parts of hybrid regression coefficients. Also the model is extended to multiple regression analysis. Eventually, method is compared with Y-H.O. chang's model.

  6. Fuzzy associative memories

    NASA Technical Reports Server (NTRS)

    Kosko, Bart

    1991-01-01

    Mappings between fuzzy cubes are discussed. This level of abstraction provides a surprising and fruitful alternative to the propositional and predicate-calculas reasoning techniques used in expert systems. It allows one to reason with sets instead of propositions. Discussed here are fuzzy and neural function estimators, neural vs. fuzzy representation of structured knowledge, fuzzy vector-matrix multiplication, and fuzzy associative memory (FAM) system architecture.

  7. Universal Approximation of Mamdani Fuzzy Controllers and Fuzzy Logical Controllers

    NASA Technical Reports Server (NTRS)

    Yuan, Bo; Klir, George J.

    1997-01-01

    In this paper, we first distinguish two types of fuzzy controllers, Mamdani fuzzy controllers and fuzzy logical controllers. Mamdani fuzzy controllers are based on the idea of interpolation while fuzzy logical controllers are based on fuzzy logic in its narrow sense, i.e., fuzzy propositional logic. The two types of fuzzy controllers treat IF-THEN rules differently. In Mamdani fuzzy controllers, rules are treated disjunctively. In fuzzy logic controllers, rules are treated conjunctively. Finally, we provide a unified proof of the property of universal approximation for both types of fuzzy controllers.

  8. Fuzzy logic controller optimization

    DOEpatents

    Sepe, Jr., Raymond B; Miller, John Michael

    2004-03-23

    A method is provided for optimizing a rotating induction machine system fuzzy logic controller. The fuzzy logic controller has at least one input and at least one output. Each input accepts a machine system operating parameter. Each output produces at least one machine system control parameter. The fuzzy logic controller generates each output based on at least one input and on fuzzy logic decision parameters. Optimization begins by obtaining a set of data relating each control parameter to at least one operating parameter for each machine operating region. A model is constructed for each machine operating region based on the machine operating region data obtained. The fuzzy logic controller is simulated with at least one created model in a feedback loop from a fuzzy logic output to a fuzzy logic input. Fuzzy logic decision parameters are optimized based on the simulation.

  9. Fuzzy slope stability method

    SciTech Connect

    Kacewicz, M.

    1987-11-01

    An approach for the description of uncertainty in geology using fuzzy-set theory and an example of slope stability problem is presented. Soil parameters may be described by fuzzy sets. The fuzzy method of slope stability estimation is considered and verified in the case of one of Warsaw's (Poland) slopes.

  10. Fuzzy Logic Engine

    NASA Technical Reports Server (NTRS)

    Howard, Ayanna

    2005-01-01

    The Fuzzy Logic Engine is a software package that enables users to embed fuzzy-logic modules into their application programs. Fuzzy logic is useful as a means of formulating human expert knowledge and translating it into software to solve problems. Fuzzy logic provides flexibility for modeling relationships between input and output information and is distinguished by its robustness with respect to noise and variations in system parameters. In addition, linguistic fuzzy sets and conditional statements allow systems to make decisions based on imprecise and incomplete information. The user of the Fuzzy Logic Engine need not be an expert in fuzzy logic: it suffices to have a basic understanding of how linguistic rules can be applied to the user's problem. The Fuzzy Logic Engine is divided into two modules: (1) a graphical-interface software tool for creating linguistic fuzzy sets and conditional statements and (2) a fuzzy-logic software library for embedding fuzzy processing capability into current application programs. The graphical- interface tool was developed using the Tcl/Tk programming language. The fuzzy-logic software library was written in the C programming language.

  11. Recurrent fuzzy ranking methods

    NASA Astrophysics Data System (ADS)

    Hajjari, Tayebeh

    2012-11-01

    With the increasing development of fuzzy set theory in various scientific fields and the need to compare fuzzy numbers in different areas. Therefore, Ranking of fuzzy numbers plays a very important role in linguistic decision-making, engineering, business and some other fuzzy application systems. Several strategies have been proposed for ranking of fuzzy numbers. Each of these techniques has been shown to produce non-intuitive results in certain case. In this paper, we reviewed some recent ranking methods, which will be useful for the researchers who are interested in this area.

  12. Applications of fuzzy logic

    SciTech Connect

    Zargham, M.R.

    1995-06-01

    Recently, fuzzy logic has been applied to many areas, such as process control, image understanding, robots, expert systems, and decision support systems. This paper will explain the basic concepts of fuzzy logic and its application in different fields. The steps to design a control system will be explained in detail. Fuzzy control is the first successful industrial application of fuzzy logic. A fuzzy controller is able to control systems which previously could only be controlled by skilled operators. In recent years Japan has achieved significant progress in this area and has applied it to variety of products such as cruise control for cars, video cameras, rice cookers, washing machines, etc.

  13. Fuzzy Sets and Mathematical Education.

    ERIC Educational Resources Information Center

    Alsina, C.; Trillas, E.

    1991-01-01

    Presents the concept of "Fuzzy Sets" and gives some ideas for its potential interest in mathematics education. Defines what a Fuzzy Set is, describes why we need to teach fuzziness, gives some examples of fuzzy questions, and offers some examples of activities related to fuzzy sets. (MDH)

  14. Introduction to Fuzzy Set Theory

    NASA Technical Reports Server (NTRS)

    Kosko, Bart

    1990-01-01

    An introduction to fuzzy set theory is described. Topics covered include: neural networks and fuzzy systems; the dynamical systems approach to machine intelligence; intelligent behavior as adaptive model-free estimation; fuzziness versus probability; fuzzy sets; the entropy-subsethood theorem; adaptive fuzzy systems for backing up a truck-and-trailer; product-space clustering with differential competitive learning; and adaptive fuzzy system for target tracking.

  15. Fuzzy mathematical techniques with applications

    SciTech Connect

    Kandel, A.

    1986-01-01

    This text presents the basic concepts of fuzzy set theory within a context of real-world applications. The book is self-contained and can be used as a starting point for people interested in this fast growing field as well as by researchers looking for new application techniques. The section on applications includes: Manipulation of knowledge in expert systems; relational database structures; pattern clustering; analysis of transient behavior in digital systems; modeling of uncertainty and search trees. Contents: Fuzzy sets; Possibility theory and fuzzy quantification; Fuzzy functions; Fuzzy events and fuzzy statistics; Fuzzy relations; Fuzzy logics; Some applications; Bibliography.

  16. Fuzzy and neural control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    Fuzzy logic and neural networks provide new methods for designing control systems. Fuzzy logic controllers do not require a complete analytical model of a dynamic system and can provide knowledge-based heuristic controllers for ill-defined and complex systems. Neural networks can be used for learning control. In this chapter, we discuss hybrid methods using fuzzy logic and neural networks which can start with an approximate control knowledge base and refine it through reinforcement learning.

  17. Some Properties of Fuzzy Soft Proximity Spaces

    PubMed Central

    Demir, İzzettin; Özbakır, Oya Bedre

    2015-01-01

    We study the fuzzy soft proximity spaces in Katsaras's sense. First, we show how a fuzzy soft topology is derived from a fuzzy soft proximity. Also, we define the notion of fuzzy soft δ-neighborhood in the fuzzy soft proximity space which offers an alternative approach to the study of fuzzy soft proximity spaces. Later, we obtain the initial fuzzy soft proximity determined by a family of fuzzy soft proximities. Finally, we investigate relationship between fuzzy soft proximities and proximities. PMID:25793224

  18. Clustering by Fuzzy Neural Gas and Evaluation of Fuzzy Clusters

    PubMed Central

    Geweniger, Tina; Fischer, Lydia; Kaden, Marika; Lange, Mandy; Villmann, Thomas

    2013-01-01

    We consider some modifications of the neural gas algorithm. First, fuzzy assignments as known from fuzzy c-means and neighborhood cooperativeness as known from self-organizing maps and neural gas are combined to obtain a basic Fuzzy Neural Gas. Further, a kernel variant and a simulated annealing approach are derived. Finally, we introduce a fuzzy extension of the ConnIndex to obtain an evaluation measure for clusterings based on fuzzy vector quantization. PMID:24396342

  19. Simplify fuzzy control implementation

    SciTech Connect

    Stoll, K.E.; Ralston, P.A.S.; Ramaganesan, S. )

    1993-07-01

    A controller that uses fuzzy rules provides better response than a conventional linear controller in some applications. The rules are best implemented as a breakpoint function. A level control example illustrates the technique and advantages over proportional-integral (PI) control. In numerous papers on fuzzy controller development, emphasis has been primarily on formal inferencing, membership functions, and steps in building a fuzzy relation, as described by Zadeh. The rationale used in formulating the required set of rules is usually neglected, and the interpretation of the final controller as an input-output algorithm is overlooked. Also, the details of fuzzy mathematics are unfamiliar to many engineers and the implementation appears cumbersome to most. Process description and control instrumentation. This article compares a fuzzy controller designed by specifying a breakpoint function with a traditional PI controller for a level control system on a laboratory scale. In this discussion, only setpoint changes are considered.

  20. Complex intuitionistic fuzzy sets

    NASA Astrophysics Data System (ADS)

    Alkouri, Abdulazeez (Moh'd. Jumah) S.; Salleh, Abdul Razak

    2012-09-01

    This paper presents a new concept of complex intuitionistic fuzzy set (CIFS) which is generalized from the innovative concept of a complex fuzzy set (CFS) by adding the non-membership term to the definition of CFS. The novelty of CIFS lies in its ability for membership and non-membership functions to achieve more range of values. The ranges of values are extended to the unit circle in complex plane for both membership and non-membership functions instead of [0, 1] as in the conventional intuitionistic fuzzy functions. We define basic operations namely complement, union, and intersection on CIFSs. Properties of these operations are derived.

  1. Fuzzy blood pressure measurement

    NASA Astrophysics Data System (ADS)

    Cuce, Antonino; Di Guardo, Mario; Sicurella, Gaetano

    1998-10-01

    In this paper, an intelligent system for blood pressure measurement is posed together with a possible implementation using an eight bit fuzzy processor. The system can automatically determine the ideal cuff inflation level eliminating the discomfort and misreading caused by incorrect cuff inflation. Using statistics distribution of the systolic and diastolic blood pressure, in the inflation phase, a fuzzy rule system determine the pressure levels at which checking the presence of heart beat in order to exceed the systolic pressure with the minimum gap. The heart beats, characterized through pressure variations, are recognized by a fuzzy classifier.

  2. Fuzziness in abacus logic

    NASA Astrophysics Data System (ADS)

    Malhas, Othman Qasim

    1993-10-01

    The concept of “abacus logic” has recently been developed by the author (Malhas, n.d.). In this paper the relation of abacus logic to the concept of fuzziness is explored. It is shown that if a certain “regularity” condition is met, concepts from fuzzy set theory arise naturally within abacus logics. In particular it is shown that every abacus logic then has a “pre-Zadeh orthocomplementation”. It is also shown that it is then possible to associate a fuzzy set with every proposition of abacus logic and that the collection of all such sets satisfies natural conditions expected in systems of fuzzy logic. Finally, the relevance to quantum mechanics is discussed.

  3. Fuzzy Risk Analyzer

    Energy Science and Technology Software Center (ESTSC)

    1994-03-04

    FRA is a general purpose code for risk analysis using fuzzy, not numeric, attributes. It allows the user to evaluate the risk associated with a composite system on the basis of the risk estimates of the individual components.

  4. Fuzzy control of magnetic bearings

    NASA Technical Reports Server (NTRS)

    Feeley, J. J.; Niederauer, G. M.; Ahlstrom, D. J.

    1991-01-01

    The use of an adaptive fuzzy control algorithm implemented on a VLSI chip for the control of a magnetic bearing was considered. The architecture of the adaptive fuzzy controller is similar to that of a neural network. The performance of the fuzzy controller is compared to that of a conventional controller by computer simulation.

  5. Component Models for Fuzzy Data

    ERIC Educational Resources Information Center

    Coppi, Renato; Giordani, Paolo; D'Urso, Pierpaolo

    2006-01-01

    The fuzzy perspective in statistical analysis is first illustrated with reference to the "Informational Paradigm" allowing us to deal with different types of uncertainties related to the various informational ingredients (data, model, assumptions). The fuzzy empirical data are then introduced, referring to "J" LR fuzzy variables as observed on "I"…

  6. An approximation of interval type-2 fuzzy controllers using fuzzy ratio switching type-1 fuzzy controllers.

    PubMed

    Tao, C W; Taur, Jinshiuh; Chuang, Chen-Chia; Chang, Chia-Wen; Chang, Yeong-Hwa

    2011-06-01

    In this paper, the interval type-2 fuzzy controllers (FC(IT2)s) are approximated using the fuzzy ratio switching type-1 FCs to avoid the complex type-reduction process required for the interval type-2 FCs. The fuzzy ratio switching type-1 FCs (FC(FRST1)s) are designed to be a fuzzy combination of the possible-leftmost and possible-rightmost type-1 FCs. The fuzzy ratio switching type-1 fuzzy control technique is applied with the sliding control technique to realize the hybrid fuzzy ratio switching type-1 fuzzy sliding controllers (HFSC(FRST1)s) for the double-pendulum-and-cart system. The simulation results and comparisons with other approaches are provided to demonstrate the effectiveness of the proposed HFSC(FRST1)s. PMID:21189244

  7. Solving fuzzy polynomial equation and the dual fuzzy polynomial equation using the ranking method

    NASA Astrophysics Data System (ADS)

    Rahman, Nurhakimah Ab.; Abdullah, Lazim

    2014-06-01

    Fuzzy polynomials with trapezoidal and triangular fuzzy numbers have attracted interest among some researchers. Many studies have been done by researchers to obtain real roots of fuzzy polynomials. As a result, there are many numerical methods involved in obtaining the real roots of fuzzy polynomials. In this study, we will present the solution to the fuzzy polynomial equation and dual fuzzy polynomial equation using the ranking method of fuzzy numbers and subsequently transforming fuzzy polynomials to crisp polynomials. This transformation is performed using the ranking method based on three parameters, namely Value, Ambiguity and Fuzziness. Finally, we illustrate our approach with two numerical examples for fuzzy polynomial equation and dual fuzzy polynomial equation.

  8. A neural fuzzy controller learning by fuzzy error propagation

    NASA Technical Reports Server (NTRS)

    Nauck, Detlef; Kruse, Rudolf

    1992-01-01

    In this paper, we describe a procedure to integrate techniques for the adaptation of membership functions in a linguistic variable based fuzzy control environment by using neural network learning principles. This is an extension to our work. We solve this problem by defining a fuzzy error that is propagated back through the architecture of our fuzzy controller. According to this fuzzy error and the strength of its antecedent each fuzzy rule determines its amount of error. Depending on the current state of the controlled system and the control action derived from the conclusion, each rule tunes the membership functions of its antecedent and its conclusion. By this we get an unsupervised learning technique that enables a fuzzy controller to adapt to a control task by knowing just about the global state and the fuzzy error.

  9. Reconfigurable fuzzy cell

    NASA Technical Reports Server (NTRS)

    Salazar, George A. (Inventor)

    1993-01-01

    This invention relates to a reconfigurable fuzzy cell comprising a digital control programmable gain operation amplifier, an analog-to-digital converter, an electrically erasable PROM, and 8-bit counter and comparator, and supporting logic configured to achieve in real-time fuzzy systems high throughput, grade-of-membership or membership-value conversion of multi-input sensor data. The invention provides a flexible multiplexing-capable configuration, implemented entirely in hardware, for effectuating S-, Z-, and PI-membership functions or combinations thereof, based upon fuzzy logic level-set theory. A membership value table storing 'knowledge data' for each of S-, Z-, and PI-functions is contained within a nonvolatile memory for storing bits of membership and parametric information in a plurality of address spaces. Based upon parametric and control signals, analog sensor data is digitized and converted into grade-of-membership data. In situ learn and recognition modes of operation are also provided.

  10. Fuzzy object modeling

    NASA Astrophysics Data System (ADS)

    Udupa, Jayaram K.; Odhner, Dewey; Falcao, Alexandre X.; Ciesielski, Krzysztof C.; Miranda, Paulo A. V.; Vaideeswaran, Pavithra; Mishra, Shipra; Grevera, George J.; Saboury, Babak; Torigian, Drew A.

    2011-03-01

    To make Quantitative Radiology (QR) a reality in routine clinical practice, computerized automatic anatomy recognition (AAR) becomes essential. As part of this larger goal, we present in this paper a novel fuzzy strategy for building bodywide group-wise anatomic models. They have the potential to handle uncertainties and variability in anatomy naturally and to be integrated with the fuzzy connectedness framework for image segmentation. Our approach is to build a family of models, called the Virtual Quantitative Human, representing normal adult subjects at a chosen resolution of the population variables (gender, age). Models are represented hierarchically, the descendents representing organs contained in parent organs. Based on an index of fuzziness of the models, 32 thorax data sets, and 10 organs defined in them, we found that the hierarchical approach to modeling can effectively handle the non-linear relationships in position, scale, and orientation that exist among organs in different patients.

  11. Fast Fuzzy Arithmetic Operations

    NASA Technical Reports Server (NTRS)

    Hampton, Michael; Kosheleva, Olga

    1997-01-01

    In engineering applications of fuzzy logic, the main goal is not to simulate the way the experts really think, but to come up with a good engineering solution that would (ideally) be better than the expert's control, In such applications, it makes perfect sense to restrict ourselves to simplified approximate expressions for membership functions. If we need to perform arithmetic operations with the resulting fuzzy numbers, then we can use simple and fast algorithms that are known for operations with simple membership functions. In other applications, especially the ones that are related to humanities, simulating experts is one of the main goals. In such applications, we must use membership functions that capture every nuance of the expert's opinion; these functions are therefore complicated, and fuzzy arithmetic operations with the corresponding fuzzy numbers become a computational problem. In this paper, we design a new algorithm for performing such operations. This algorithm is applicable in the case when negative logarithms - log(u(x)) of membership functions u(x) are convex, and reduces computation time from O(n(exp 2))to O(n log(n)) (where n is the number of points x at which we know the membership functions u(x)).

  12. Fuzziness at the horizon

    NASA Astrophysics Data System (ADS)

    Batic, Davide; Nicolini, Piero

    2010-08-01

    We study the stability of the noncommutative Schwarzschild black hole interior by analysing the propagation of a massless scalar field between the two horizons. We show that the spacetime fuzziness triggered by the field higher momenta can cure the classical exponential blue-shift divergence, suppressing the emergence of infinite energy density in a region nearby the Cauchy horizon.

  13. Fuzzy logic in control systems: Fuzzy logic controller. I, II

    NASA Technical Reports Server (NTRS)

    Lee, Chuen Chien

    1990-01-01

    Recent advances in the theory and applications of fuzzy-logic controllers (FLCs) are examined in an analytical review. The fundamental principles of fuzzy sets and fuzzy logic are recalled; the basic FLC components (fuzzification and defuzzification interfaces, knowledge base, and decision-making logic) are described; and the advantages of FLCs for incorporating expert knowledge into a control system are indicated. Particular attention is given to fuzzy implication functions, the interpretation of sentence connectives (and, also), compositional operators, and inference mechanisms. Applications discussed include the FLC-guided automobile developed by Sugeno and Nishida (1985), FLC hardware systems, FLCs for subway trains and ship-loading cranes, fuzzy-logic chips, and fuzzy computers.

  14. Interval-valued fuzzy hypergraph and fuzzy partition.

    PubMed

    Chen, S M

    1997-01-01

    This paper extends the work of H. Lee-Kwang and L.M. Lee (1995) to present the concept of the interval-valued fuzzy hypergraph. In the interval-valued fuzzy hypergraph, the concepts of the dual interval-valued fuzzy hypergraph, the crisp-valued alpha-cut hypergraph, and the interval-valued [alpha(1),alpha(2 )]-cut at beta level hypergraph are developed, where alphain [0, 1], 0fuzzy partition of a system. PMID:18255914

  15. Fuzzy logic particle tracking velocimetry

    NASA Technical Reports Server (NTRS)

    Wernet, Mark P.

    1993-01-01

    Fuzzy logic has proven to be a simple and robust method for process control. Instead of requiring a complex model of the system, a user defined rule base is used to control the process. In this paper the principles of fuzzy logic control are applied to Particle Tracking Velocimetry (PTV). Two frames of digitally recorded, single exposure particle imagery are used as input. The fuzzy processor uses the local particle displacement information to determine the correct particle tracks. Fuzzy PTV is an improvement over traditional PTV techniques which typically require a sequence (greater than 2) of image frames for accurately tracking particles. The fuzzy processor executes in software on a PC without the use of specialized array or fuzzy logic processors. A pair of sample input images with roughly 300 particle images each, results in more than 200 velocity vectors in under 8 seconds of processing time.

  16. Fuzzy learning under and about an unfamiliar fuzzy teacher

    NASA Technical Reports Server (NTRS)

    Dasarathy, Belur V.

    1992-01-01

    This study addresses the problem of optimal parametric learning in unfamiliar fuzzy environments. Prior studies in the domain of unfamiliar environments, which employed either crisp or fuzzy approaches to model the uncertainty or imperfectness of the learning environment, assumed that the training sample labels provided by the unfamiliar teacher were crisp, even if not perfect. Here, the more realistic problem of fuzzy learning under an unfamiliar teacher who provides only fuzzy (instead of crisp) labels, is tackled by expanding the previously defined fuzzy membership concepts to include an additional component representative of the fuzziness of the teacher. The previously studied scenarios, namely, crisp and fuzzy learning under (crisp) unfamiliar teacher, can be looked upon as special cases of this new methodology. As under the earlier studies, the estimated membership functions can then be deployed during the ensuing classification decision phase to judiciously take into account the imperfectness of the learning environment. The study also offers some insight into the properties of several of these fuzzy membership function estimators by examining their behavior under certain specific scenarios.

  17. On fuzzy ideals of BL-algebras.

    PubMed

    Meng, Biao Long; Xin, Xiao Long

    2014-01-01

    In this paper we investigate further properties of fuzzy ideals of a BL-algebra. The notions of fuzzy prime ideals, fuzzy irreducible ideals, and fuzzy Gödel ideals of a BL-algebra are introduced and their several properties are investigated. We give a procedure to generate a fuzzy ideal by a fuzzy set. We prove that every fuzzy irreducible ideal is a fuzzy prime ideal but a fuzzy prime ideal may not be a fuzzy irreducible ideal and prove that a fuzzy prime ideal ω is a fuzzy irreducible ideal if and only if ω(0) = 1 and |Im(ω)| = 2. We give the Krull-Stone representation theorem of fuzzy ideals in BL-algebras. Furthermore, we prove that the lattice of all fuzzy ideals of a BL-algebra is a complete distributive lattice. Finally, it is proved that every fuzzy Boolean ideal is a fuzzy Gödel ideal, but the converse implication is not true. PMID:24892085

  18. Fuzzy Logic in Medicine and Bioinformatics

    PubMed Central

    Torres, Angela; Nieto, Juan J.

    2006-01-01

    The purpose of this paper is to present a general view of the current applications of fuzzy logic in medicine and bioinformatics. We particularly review the medical literature using fuzzy logic. We then recall the geometrical interpretation of fuzzy sets as points in a fuzzy hypercube and present two concrete illustrations in medicine (drug addictions) and in bioinformatics (comparison of genomes). PMID:16883057

  19. Fuzzy models for pattern recognition

    SciTech Connect

    Bezdek, James C.; Pal, Sankar K.

    1994-01-01

    FUZZY sets were introduced in 1965 by Lotfi Zadeh as a new way to represent vagueness in everyday life. They are a generalization of conventional set theory, one of the basic structures underlying computational mathematics and models. Computational pattern recognition has played a central role in the development of fuzzy models because fuzzy interpretations of data structures are a very natural and intuitively plausible way to formulate and solve various problems. Fuzzy control theory has also provided a wide variety of real, fielded system applications of fuzzy technology. We shall have little more to say about the growth of fuzzy models in control, except to the extent that pattern recognition algorithms and methods described in this book impact control systems. Collected here are many of the seminal papers in the field. There will be, of course, omissions that are neither by intent nor ignorance; we cannot reproduce all of the important papers that have helped in the evolution of fuzzy pattern recognition (there may be as many as five hundred) even in this narrow application domain. We will attempt, in each chapter introduction, to comment on some of the important papers that not been included and we ask both readers and authors to understand that a book such as this simply cannot {open_quotes}contain everything.{close_quotes} Our objective in Chapter 1 is to describe the basic structure of fuzzy sets theory as it applies to the major problems encountered in the design of a pattern recognition system.

  20. Teaching Machines to Think Fuzzy

    ERIC Educational Resources Information Center

    Technology Teacher, 2004

    2004-01-01

    Fuzzy logic programs for computers make them more human. Computers can then think through messy situations and make smart decisions. It makes computers able to control things the way people do. Fuzzy logic has been used to control subway trains, elevators, washing machines, microwave ovens, and cars. Pretty much all the human has to do is push one…

  1. Generalized interval-valued fuzzy variable precision rough sets determined by fuzzy logical operators

    NASA Astrophysics Data System (ADS)

    Qing Hu, Bao

    2015-11-01

    The fuzzy rough set model and interval-valued fuzzy rough set model have been introduced to handle databases with real values and interval values, respectively. Variable precision rough set was advanced by Ziarko to overcome the shortcomings of misclassification and/or perturbation in Pawlak rough sets. By combining fuzzy rough set and variable precision rough set, a variety of fuzzy variable precision rough sets were studied, which cannot only handle numerical data, but are also less sensitive to misclassification. However, fuzzy variable precision rough sets cannot effectively handle interval-valued data-sets. Research into interval-valued fuzzy rough sets for interval-valued fuzzy data-sets has commenced; however, variable precision problems have not been considered in interval-valued fuzzy rough sets and generalized interval-valued fuzzy rough sets based on fuzzy logical operators nor have interval-valued fuzzy sets been considered in variable precision rough sets and fuzzy variable precision rough sets. These current models are incapable of wide application, especially on misclassification and/or perturbation and on interval-valued fuzzy data-sets. In this paper, these models are generalized to a more integrative approach that not only considers interval-valued fuzzy sets, but also variable precision. First, we review generalized interval-valued fuzzy rough sets based on two fuzzy logical operators: interval-valued fuzzy triangular norms and interval-valued fuzzy residual implicators. Second, we propose generalized interval-valued fuzzy variable precision rough sets based on the above two fuzzy logical operators. Finally, we confirm that some existing models, including rough sets, fuzzy variable precision rough sets, interval-valued fuzzy rough sets, generalized fuzzy rough sets and generalized interval-valued fuzzy variable precision rough sets based on fuzzy logical operators, are special cases of the proposed models.

  2. Fuzzy θ-generalized semi-continuous and fuzzy θ-generalized semi-irresolute mappings

    NASA Astrophysics Data System (ADS)

    Wahab, Nurul Adilla Farhana Abdul; Salleh, Zabidin

    2015-10-01

    In this paper, some fuzzy generalized continuity are introduced and studied using the concept of fuzzy θ-generalized semi-closed sets namely fuzzy θ-generalized semi-continuity, fuzzy θ-generalized semi-irresolute and fuzzy θ-generalized semi-closed maps. Fuzzy θ-generalized semi-T1/2 spaces are also introduced and their characterizations are studied. Several interesting results are obtained.

  3. Fuzzy portfolio model with fuzzy-input return rates and fuzzy-output proportions

    NASA Astrophysics Data System (ADS)

    Tsaur, Ruey-Chyn

    2015-02-01

    In the finance market, a short-term investment strategy is usually applied in portfolio selection in order to reduce investment risk; however, the economy is uncertain and the investment period is short. Further, an investor has incomplete information for selecting a portfolio with crisp proportions for each chosen security. In this paper we present a new method of constructing fuzzy portfolio model for the parameters of fuzzy-input return rates and fuzzy-output proportions, based on possibilistic mean-standard deviation models. Furthermore, we consider both excess or shortage of investment in different economic periods by using fuzzy constraint for the sum of the fuzzy proportions, and we also refer to risks of securities investment and vagueness of incomplete information during the period of depression economics for the portfolio selection. Finally, we present a numerical example of a portfolio selection problem to illustrate the proposed model and a sensitivity analysis is realised based on the results.

  4. Fuzzy conceptual rainfall runoff models

    NASA Astrophysics Data System (ADS)

    Özelkan, Ertunga C.; Duckstein, Lucien

    2001-11-01

    A fuzzy conceptual rainfall-runoff (CRR) framework is proposed herein to deal with those parameter uncertainties of conceptual rainfall-runoff models, that are related to data and/or model structure: with every element of the rainfall-runoff model assumed to be possibly uncertain, taken here as being fuzzy. First, the conceptual rainfall-runoff system is fuzzified and then different operational modes are formulated using fuzzy rules; second, the parameter identification aspect is examined using fuzzy regression techniques. In particular, bi-objective and tri-objective fuzzy regression models are applied in the case of linear conceptual rainfall-runoff models so that the decision maker may be able to trade off prediction vagueness (uncertainty) and the embedding outliers. For the non-linear models, a fuzzy least squares regression framework is applied to derive the model parameters. The methodology is illustrated using: (1) a linear conceptual rainfall-runoff model; (2) an experimental two-parameter model; and (3) a simplified version of the Sacramento soil moisture accounting model of the US National Weather Services river forecast system (SAC-SMA) known as the six-parameter model. It is shown that the fuzzy logic framework enables the decision maker to gain insight about the model sensitivity and the uncertainty stemming from the elements of the CRR model.

  5. Fuzzy expert systems using CLIPS

    NASA Technical Reports Server (NTRS)

    Le, Thach C.

    1994-01-01

    This paper describes a CLIPS-based fuzzy expert system development environment called FCLIPS and illustrates its application to the simulated cart-pole balancing problem. FCLIPS is a straightforward extension of CLIPS without any alteration to the CLIPS internal structures. It makes use of the object-oriented and module features in CLIPS version 6.0 for the implementation of fuzzy logic concepts. Systems of varying degrees of mixed Boolean and fuzzy rules can be implemented in CLIPS. Design and implementation issues of FCLIPS will also be discussed.

  6. Entanglement entropy on fuzzy spaces

    SciTech Connect

    Dou, Djamel; Ydri, Badis

    2006-08-15

    We study the entanglement entropy of a scalar field in 2+1 spacetime where space is modeled by a fuzzy sphere and a fuzzy disc. In both models we evaluate numerically the resulting entropies and find that they are proportional to the number of boundary degrees of freedom. In the Moyal plane limit of the fuzzy disc the entanglement entropy per unite area (length) diverges if the ignored region is of infinite size. The divergence is (interpreted) of IR-UV mixing origin. In general we expect the entanglement entropy per unite area to be finite on a noncommutative space if the ignored region is of finite size.

  7. Fuzzy resource optimization for safeguards

    SciTech Connect

    Zardecki, A.; Markin, J.T.

    1991-01-01

    Authorization, enforcement, and verification -- three key functions of safeguards systems -- form the basis of a hierarchical description of the system risk. When formulated in terms of linguistic rather than numeric attributes, the risk can be computed through an algorithm based on the notion of fuzzy sets. Similarly, this formulation allows one to analyze the optimal resource allocation by maximizing the overall detection probability, regarded as a linguistic variable. After summarizing the necessary elements of the fuzzy sets theory, we outline the basic algorithm. This is followed by a sample computation of the fuzzy optimization. 10 refs., 1 tab.

  8. Evaluation of fuzzy inference systems using fuzzy least squares

    NASA Technical Reports Server (NTRS)

    Barone, Joseph M.

    1992-01-01

    Efforts to develop evaluation methods for fuzzy inference systems which are not based on crisp, quantitative data or processes (i.e., where the phenomenon the system is built to describe or control is inherently fuzzy) are just beginning. This paper suggests that the method of fuzzy least squares can be used to perform such evaluations. Regressing the desired outputs onto the inferred outputs can provide both global and local measures of success. The global measures have some value in an absolute sense, but they are particularly useful when competing solutions (e.g., different numbers of rules, different fuzzy input partitions) are being compared. The local measure described here can be used to identify specific areas of poor fit where special measures (e.g., the use of emphatic or suppressive rules) can be applied. Several examples are discussed which illustrate the applicability of the method as an evaluation tool.

  9. Normal forms of fuzzy middle and fuzzy contradictions.

    PubMed

    Turksen, I B; Kandel, A; Zhang, Y Q

    1999-01-01

    The expressions of "excluded middle" and "crisp contradiction" are reexamined starting with their original linguistic expressions which are first restated in propositional and then predicate forms. It is shown that, in order to generalize the truth tables and hence the normal forms, the membership assignments in predicate expressions must be separated from their truth qualification. In two-valued logic, there is no need to separate them from each other due to reductionist Aristotalean dichotomy. Whereas, in infinite (fuzzy) valued set and logic, the separation of membership assignments from their truth qualification forms the bases of a new reconstruction of the truth tables. The results obtained from these extended truth tables are reducible to their Boolean equivalents under the axioms of Boolean theory. Whereas, in fuzzy set and logic theory, we obtain a richer and more complex interpretations of the "fuzzy middle" and "fuzzy contradiction." PMID:18252295

  10. Fuzzy Thinking in Non-Fuzzy Situations: Understanding Students' Perspective.

    ERIC Educational Resources Information Center

    Zazkis, Rina

    1995-01-01

    In mathematics a true statement is always true, but some false statements are more false than others. Fuzzy logic provides a way of handling degrees of set membership and has implications for helping students appreciate logical thinking. (MKR)

  11. Fuzzy Content-Based Retrieval in Image Databases.

    ERIC Educational Resources Information Center

    Wu, Jian Kang; Narasimhalu, A. Desai

    1998-01-01

    Proposes a fuzzy-image database model and a concept of fuzzy space; describes fuzzy-query processing in fuzzy space and fuzzy indexing on complete fuzzy vectors; and uses an example image database, the computer-aided facial-image inference and retrieval system (CAFIIR), for explanation throughout. (Author/LRW)

  12. A Fuzzy Query Mechanism for Human Resource Websites

    NASA Astrophysics Data System (ADS)

    Lai, Lien-Fu; Wu, Chao-Chin; Huang, Liang-Tsung; Kuo, Jung-Chih

    Users' preferences often contain imprecision and uncertainty that are difficult for traditional human resource websites to deal with. In this paper, we apply the fuzzy logic theory to develop a fuzzy query mechanism for human resource websites. First, a storing mechanism is proposed to store fuzzy data into conventional database management systems without modifying DBMS models. Second, a fuzzy query language is proposed for users to make fuzzy queries on fuzzy databases. User's fuzzy requirement can be expressed by a fuzzy query which consists of a set of fuzzy conditions. Third, each fuzzy condition associates with a fuzzy importance to differentiate between fuzzy conditions according to their degrees of importance. Fourth, the fuzzy weighted average is utilized to aggregate all fuzzy conditions based on their degrees of importance and degrees of matching. Through the mutual compensation of all fuzzy conditions, the ordering of query results can be obtained according to user's preference.

  13. Fuzzy logic components for iterative deconvolution systems

    NASA Astrophysics Data System (ADS)

    Northan, Brian M.

    2013-02-01

    Deconvolution systems rely heavily on expert knowledge and would benefit from approaches that capture this expert knowledge. Fuzzy logic is an approach that is used to capture expert knowledge rules and produce outputs that range in degree. This paper describes a fuzzy-deconvolution-system that integrates traditional Richardson-Lucy deconvolution with fuzzy components. The system is intended for restoration of 3D widefield images taken under conditions of refractive index mismatch. The system uses a fuzzy rule set for calculating sample refractive index, a fuzzy median filter for inter-iteration noise reduction, and a fuzzy rule set for stopping criteria.

  14. Dynamical tachyons on fuzzy spheres

    NASA Astrophysics Data System (ADS)

    Berenstein, David; Trancanelli, Diego

    2011-05-01

    We study the spectrum of off-diagonal fluctuations between displaced fuzzy spheres in the Berenstein-Maldacena-Nastase plane wave matrix model. The displacement is along the plane of the fuzzy spheres. We find that when two fuzzy spheres intersect at angles, classical tachyons develop and that the spectrum of these modes can be computed analytically. These tachyons can be related to the familiar Nielsen-Olesen instabilities in Yang-Mills theory on a constant magnetic background. Many features of the problem become more apparent when we compare with maximally supersymmetric Yang-Mills theory on a sphere, of which this system is a truncation. We also set up a simple oscillatory trajectory on the displacement between the fuzzy spheres and study the dynamics of the modes as they become tachyonic for part of the oscillations. We speculate on their role regarding the possible thermalization of the system.

  15. Knowledge representation in fuzzy logic

    NASA Technical Reports Server (NTRS)

    Zadeh, Lotfi A.

    1989-01-01

    The author presents a summary of the basic concepts and techniques underlying the application of fuzzy logic to knowledge representation. He then describes a number of examples relating to its use as a computational system for dealing with uncertainty and imprecision in the context of knowledge, meaning, and inference. It is noted that one of the basic aims of fuzzy logic is to provide a computational framework for knowledge representation and inference in an environment of uncertainty and imprecision. In such environments, fuzzy logic is effective when the solutions need not be precise and/or it is acceptable for a conclusion to have a dispositional rather than categorical validity. The importance of fuzzy logic derives from the fact that there are many real-world applications which fit these conditions, especially in the realm of knowledge-based systems for decision-making and control.

  16. Dynamical tachyons on fuzzy spheres

    SciTech Connect

    Berenstein, David; Trancanelli, Diego

    2011-05-15

    We study the spectrum of off-diagonal fluctuations between displaced fuzzy spheres in the Berenstein-Maldacena-Nastase plane wave matrix model. The displacement is along the plane of the fuzzy spheres. We find that when two fuzzy spheres intersect at angles, classical tachyons develop and that the spectrum of these modes can be computed analytically. These tachyons can be related to the familiar Nielsen-Olesen instabilities in Yang-Mills theory on a constant magnetic background. Many features of the problem become more apparent when we compare with maximally supersymmetric Yang-Mills theory on a sphere, of which this system is a truncation. We also set up a simple oscillatory trajectory on the displacement between the fuzzy spheres and study the dynamics of the modes as they become tachyonic for part of the oscillations. We speculate on their role regarding the possible thermalization of the system.

  17. Current projects in Fuzzy Control

    NASA Technical Reports Server (NTRS)

    Sugeno, Michio

    1990-01-01

    Viewgraphs on current projects in fuzzy control are presented. Three projects on helicopter flight control are discussed. The projects are (1) radio control by oral instructions; (2) automatic autorotation entry in engine failure; and (3) unmanned helicopter for sea rescue.

  18. Fuzzy Logic Particle Tracking

    NASA Technical Reports Server (NTRS)

    2005-01-01

    A new all-electronic Particle Image Velocimetry technique that can efficiently map high speed gas flows has been developed in-house at the NASA Lewis Research Center. Particle Image Velocimetry is an optical technique for measuring the instantaneous two component velocity field across a planar region of a seeded flow field. A pulsed laser light sheet is used to illuminate the seed particles entrained in the flow field at two instances in time. One or more charged coupled device (CCD) cameras can be used to record the instantaneous positions of particles. Using the time between light sheet pulses and determining either the individual particle displacements or the average displacement of particles over a small subregion of the recorded image enables the calculation of the fluid velocity. Fuzzy logic minimizes the required operator intervention in identifying particles and computing velocity. Using two cameras that have the same view of the illumination plane yields two single exposure image frames. Two competing techniques that yield unambiguous velocity vector direction information have been widely used for reducing the single-exposure, multiple image frame data: (1) cross-correlation and (2) particle tracking. Correlation techniques yield averaged velocity estimates over subregions of the flow, whereas particle tracking techniques give individual particle velocity estimates. For the correlation technique, the correlation peak corresponding to the average displacement of particles across the subregion must be identified. Noise on the images and particle dropout result in misidentification of the true correlation peak. The subsequent velocity vector maps contain spurious vectors where the displacement peaks have been improperly identified. Typically these spurious vectors are replaced by a weighted average of the neighboring vectors, thereby decreasing the independence of the measurements. In this work, fuzzy logic techniques are used to determine the true

  19. A Novel Method for Discovering Fuzzy Sequential Patterns Using the Simple Fuzzy Partition Method.

    ERIC Educational Resources Information Center

    Chen, Ruey-Shun; Hu, Yi-Chung

    2003-01-01

    Discusses sequential patterns, data mining, knowledge acquisition, and fuzzy sequential patterns described by natural language. Proposes a fuzzy data mining technique to discover fuzzy sequential patterns by using the simple partition method which allows the linguistic interpretation of each fuzzy set to be easily obtained. (Author/LRW)

  20. Lorentzian fuzzy spheres

    NASA Astrophysics Data System (ADS)

    Chaney, A.; Lu, Lei; Stern, A.

    2015-09-01

    We show that fuzzy spheres are solutions of Lorentzian Ishibashi-Kawai-Kitazawa-Tsuchiya-type matrix models. The solutions serve as toy models of closed noncommutative cosmologies where big bang/crunch singularities appear only after taking the commutative limit. The commutative limit of these solutions corresponds to a sphere embedded in Minkowski space. This "sphere" has several novel features. The induced metric does not agree with the standard metric on the sphere, and, moreover, it does not have a fixed signature. The curvature computed from the induced metric is not constant, has singularities at fixed latitudes (not corresponding to the poles) and is negative. Perturbations are made about the solutions, and are shown to yield a scalar field theory on the sphere in the commutative limit. The scalar field can become tachyonic for a range of the parameters of the theory.

  1. Fuzzy tuning B-spline curve

    NASA Astrophysics Data System (ADS)

    Fatah, Abd.; Rozaimi

    2015-12-01

    In this paper, we will discuss about the construction of fuzzy tuning B-spline curve based on fuzzy set theory. The concept of fuzzy tuning in designing this B-spline curve is based on the uncertain knots values which has to be defined first and then the result will be blended together with B-spline function which exists in users presumption in deciding the best knots value of tuning. Therefore, fuzzy set theory especially fuzzy number concepts are used to define the uncertain knots values and then it will be become fuzzy knots values. The Result by using different values of fuzzy knots for constructing a fuzzy tuning of B-spline curves will be illustrated.

  2. The Lattices of Group Fuzzy Congruences and Normal Fuzzy Subsemigroups on E-Inversive Semigroups

    PubMed Central

    Wang, Shoufeng

    2014-01-01

    The aim of this paper is to investigate the lattices of group fuzzy congruences and normal fuzzy subsemigroups on E-inversive semigroups. We prove that group fuzzy congruences and normal fuzzy subsemigroups determined each other in E-inversive semigroups. Moreover, we show that the set of group t-fuzzy congruences and the set of normal subsemigroups with tip t in a given E-inversive semigroup form two mutually isomorphic modular lattices for every t∈ [0,1]. PMID:24892045

  3. Consistent linguistic fuzzy preference relations method with ranking fuzzy numbers

    NASA Astrophysics Data System (ADS)

    Ridzuan, Siti Amnah Mohd; Mohamad, Daud; Kamis, Nor Hanimah

    2014-12-01

    Multi-Criteria Decision Making (MCDM) methods have been developed to help decision makers in selecting the best criteria or alternatives from the options given. One of the well known methods in MCDM is the Consistent Fuzzy Preference Relation (CFPR) method, essentially utilizes a pairwise comparison approach. This method was later improved to cater subjectivity in the data by using fuzzy set, known as the Consistent Linguistic Fuzzy Preference Relations (CLFPR). The CLFPR method uses the additive transitivity property in the evaluation of pairwise comparison matrices. However, the calculation involved is lengthy and cumbersome. To overcome this problem, a method of defuzzification was introduced by researchers. Nevertheless, the defuzzification process has a major setback where some information may lose due to the simplification process. In this paper, we propose a method of CLFPR that preserves the fuzzy numbers form throughout the process. In obtaining the desired ordering result, a method of ranking fuzzy numbers is utilized in the procedure. This improved procedure for CLFPR is implemented to a case study to verify its effectiveness. This method is useful for solving decision making problems and can be applied to many areas of applications.

  4. Fuzzy logic and neural networks

    SciTech Connect

    Loos, J.R.

    1994-11-01

    Combine fuzzy logic`s fuzzy sets, fuzzy operators, fuzzy inference, and fuzzy rules - like defuzzification - with neural networks and you can arrive at very unfuzzy real-time control. Fuzzy logic, cursed with a very whimsical title, simply means multivalued logic, which includes not only the conventional two-valued (true/false) crisp logic, but also the logic of three or more values. This means one can assign logic values of true, false, and somewhere in between. This is where fuzziness comes in. Multi-valued logic avoids the black-and-white, all-or-nothing assignment of true or false to an assertion. Instead, it permits the assignment of shades of gray. When assigning a value of true or false to an assertion, the numbers typically used are {open_quotes}1{close_quotes} or {open_quotes}0{close_quotes}. This is the case for programmed systems. If {open_quotes}0{close_quotes} means {open_quotes}false{close_quotes} and {open_quotes}1{close_quotes} means {open_quotes}true,{close_quotes} then {open_quotes}shades of gray{close_quotes} are any numbers between 0 and 1. Therefore, {open_quotes}nearly true{close_quotes} may be represented by 0.8 or 0.9, {open_quotes}nearly false{close_quotes} may be represented by 0.1 or 0.2, and {close_quotes}your guess is as good as mine{close_quotes} may be represented by 0.5. The flexibility available to one is limitless. One can associate any meaning, such as {open_quotes}nearly true{close_quotes}, to any value of any granularity, such as 0.9999. 2 figs.

  5. Fuzzy image processing in sun sensor

    NASA Technical Reports Server (NTRS)

    Mobasser, S.; Liebe, C. C.; Howard, A.

    2003-01-01

    This paper will describe how the fuzzy image processing is implemented in the instrument. Comparison of the Fuzzy image processing and a more conventional image processing algorithm is provided and shows that the Fuzzy image processing yields better accuracy then conventional image processing.

  6. Forecasting Enrollments with Fuzzy Time Series.

    ERIC Educational Resources Information Center

    Song, Qiang; Chissom, Brad S.

    The concept of fuzzy time series is introduced and used to forecast the enrollment of a university. Fuzzy time series, an aspect of fuzzy set theory, forecasts enrollment using a first-order time-invariant model. To evaluate the model, the conventional linear regression technique is applied and the predicted values obtained are compared to the…

  7. Imprecise (fuzzy) information in geostatistics

    SciTech Connect

    Bardossy, A.; Bogardi, I.; Kelly, W.E.

    1988-05-01

    A methodology based on fuzzy set theory for the utilization of imprecise data in geostatistics is presented. A common problem preventing a broader use of geostatistics has been the insufficient amount of accurate measurement data. In certain cases, additional but uncertain (soft) information is available and can be encoded as subjective probabilities, and then the soft kriging method can be applied (Journal, 1986). In other cases, a fuzzy encoding of soft information may be more realistic and simplify the numerical calculations. Imprecise (fuzzy) spatial information on the possible variogram is integrated into a single variogram which is used in a fuzzy kriging procedure. The overall uncertainty of prediction is represented by the estimation variance and the calculated membership function for each kriged point. The methodology is applied to the permeability prediction of a soil liner for hazardous waste containment. The available number of hard measurement data (20) was not enough for a classical geostatistical analysis. An additional 20 soft data made it possible to prepare kriged contour maps using the fuzzy geostatistical procedure.

  8. Fuzzy-algebra uncertainty assessment

    SciTech Connect

    Cooper, J.A.; Cooper, D.K.

    1994-12-01

    A significant number of analytical problems (for example, abnormal-environment safety analysis) depend on data that are partly or mostly subjective. Since fuzzy algebra depends on subjective operands, we have been investigating its applicability to these forms of assessment, particularly for portraying uncertainty in the results of PRA (probabilistic risk analysis) and in risk-analysis-aided decision-making. Since analysis results can be a major contributor to a safety-measure decision process, risk management depends on relating uncertainty to only known (not assumed) information. The uncertainties due to abnormal environments are even more challenging than those in normal-environment safety assessments; and therefore require an even more judicious approach. Fuzzy algebra matches these requirements well. One of the most useful aspects of this work is that we have shown the potential for significant differences (especially in perceived margin relative to a decision threshold) between fuzzy assessment and probabilistic assessment based on subtle factors inherent in the choice of probability distribution models. We have also shown the relation of fuzzy algebra assessment to ``bounds`` analysis, as well as a description of how analyses can migrate from bounds analysis to fuzzy-algebra analysis, and to probabilistic analysis as information about the process to be analyzed is obtained. Instructive examples are used to illustrate the points.

  9. Fuzzy logic of Aristotelian forms

    SciTech Connect

    Perlovsky, L.I.

    1996-12-31

    Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.

  10. Effect of noise on chaotic fuzzy mappings

    SciTech Connect

    Zardecki, A.

    1996-03-01

    Chaotic mappings in the space of fuzzy sets induced by mappings of the underlying reference set are investigated. Different fuzzification schemes are considered and their impact on the resultant iterated fuzzy set, under a quadratic mapping, is studied numerically. The fuzzy set mapping is described in terms of the mapping of level cuts, resulting from the resolution theorem for fuzzy sets. In the two-dimensional case, a generalized notion, given as a fuzzy set, of the Hausdorff dimension is formulated. An example, based on the Henon Mapping, is provided.

  11. Construction of fuzzy S{sup 4}

    SciTech Connect

    Abe, Yasuhiro

    2004-12-15

    We construct a fuzzy S{sup 4}, utilizing the fact that CP{sup 3} is an S{sup 2} bundle over S{sup 4}. We find that the fuzzy S{sup 4} can be described by a block-diagonal form whose embedding square matrix represents a fuzzy CP{sup 3}. We discuss some pending issues on fuzzy S{sup 4}, i.e., precise matrix-function correspondence, associativity of the algebra, and, etc. Similarly, we also obtain a fuzzy S{sup 8}, using the fact that CP{sup 7} is a CP{sup 3} bundle over S{sup 8}.

  12. Parallel Fuzzy Segmentation of Multiple Objects.

    PubMed

    Garduño, Edgar; Herman, Gabor T

    2008-01-01

    The usefulness of fuzzy segmentation algorithms based on fuzzy connectedness principles has been established in numerous publications. New technologies are capable of producing larger-and-larger datasets and this causes the sequential implementations of fuzzy segmentation algorithms to be time-consuming. We have adapted a sequential fuzzy segmentation algorithm to multi-processor machines. We demonstrate the efficacy of such a distributed fuzzy segmentation algorithm by testing it with large datasets (of the order of 50 million points/voxels/items): a speed-up factor of approximately five over the sequential implementation seems to be the norm. PMID:19444333

  13. Fuzzy reliability analysis of structures by using the method of fuzzy optimization

    SciTech Connect

    Hu, Y.; Chen, B.

    1996-12-31

    There are two kinds of uncertainties in safety assessment of engineering structures. One is of the nature of randomness, and the other fuzziness. Fuzzy uncertainties exist in defining certain structural performances, conditions, parameters, and their interrelationships. The theory of fuzzy sets should be employed to cope with the fuzzy uncertainties. In this paper, a general definition for structural failure considering the fuzzy uncertainties is introduced firstly. Failure of the structure is modelled by a fuzzy event, and described by the membership function. The limit state surface is then replaced by a fuzzy limit state zone, in which every point represents a state belonging to the failure with a certain degree of membership. Then a fuzzy optimization problem for solving the reliability index is formulated. In classical structural reliability theory, the reliability index is defined by the minimum distance from the limit state surface to the origin of the standard normal variable space. It can be evaluated by solving an optimization problem. When the fuzzy uncertainties are taken into consideration, the crisp limit state surface becomes a fuzzy limit state zone. In this case, a corresponding fuzzy optimization problem can be formulated. Fuzzy reliability index can be obtained by solving the fuzzy optimization problem by an iterative procedure with some criteria base on the fuzzy decision-making. Numerical examples are given in the paper.

  14. Fuzzy sensitivity analysis for reliability assessment of building structures

    NASA Astrophysics Data System (ADS)

    Kala, Zdeněk

    2016-06-01

    The mathematical concept of fuzzy sensitivity analysis, which studies the effects of the fuzziness of input fuzzy numbers on the fuzziness of the output fuzzy number, is described in the article. The output fuzzy number is evaluated using Zadeh's general extension principle. The contribution of stochastic and fuzzy uncertainty in reliability analysis tasks of building structures is discussed. The algorithm of fuzzy sensitivity analysis is an alternative to stochastic sensitivity analysis in tasks in which input and output variables are considered as fuzzy numbers.

  15. Discovering fuzzy spatial association rules

    NASA Astrophysics Data System (ADS)

    Kacar, Esen; Cicekli, Nihan K.

    2002-03-01

    Discovering interesting, implicit knowledge and general relationships in geographic information databases is very important to understand and use these spatial data. One of the methods for discovering this implicit knowledge is mining spatial association rules. A spatial association rule is a rule indicating certain association relationships among a set of spatial and possibly non-spatial predicates. In the mining process, data is organized in a hierarchical manner. However, in real-world applications it may not be possible to construct a crisp structure for this data, instead some fuzzy structures should be used. Fuzziness, i.e. partial belonging of an item to more than one sub-item in the hierarchy, could be applied to the data itself, and also to the hierarchy of spatial relations. This paper shows that, strong association rules can be mined from large spatial databases using fuzzy concept and spatial relation hierarchies.

  16. Fuzzy lattice neurocomputing (FLN) models.

    PubMed

    Kaburlasos, V G; Petridis, V

    2000-12-01

    In this work it is shown how fuzzy lattice neurocomputing (FLN) emerges as a connectionist paradigm in the framework of fuzzy lattices (FL-framework) whose advantages include the capacity to deal rigorously with: disparate types of data such as numeric and linguistic data, intervals of values, 'missing' and 'don't care' data. A novel notation for the FL-framework is introduced here in order to simplify mathematical expressions without losing content. Two concrete FLN models are presented, namely 'sigma-FLN' for competitive clustering, and 'FLN with tightest fits (FLNtf)' for supervised clustering. Learning by the sigma-FLN, is rapid as it requires a single pass through the data, whereas learning by the FLNtf, is incremental, data order independent, polynomial theta(n3), and it guarantees maximization of the degree of inclusion of an input in a learned class as explained in the text. Convenient geometric interpretations are provided. The sigma-FLN is presented here as fuzzy-ART's extension in the FL-framework such that sigma-FLN widens fuzzy-ART's domain of application to (mathematical) lattices by augmenting the scope of both of fuzzy-ART's choice (Weber) and match functions, and by enhancing fuzzy-ART's complement coding technique. The FLNtf neural model is applied to four benchmark data sets of various sizes for pattern recognition and rule extraction. The benchmark data sets in question involve jointly numeric and nominal data with 'missing' and/or 'don't care' attribute values, whereas the lattices involved include the unit-hypercube, a probability space, and a Boolean algebra. The potential of the FL-framework in computing is also delineated. PMID:11156192

  17. Learning and Tuning of Fuzzy Rules

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1997-01-01

    In this chapter, we review some of the current techniques for learning and tuning fuzzy rules. For clarity, we refer to the process of generating rules from data as the learning problem and distinguish it from tuning an already existing set of fuzzy rules. For learning, we touch on unsupervised learning techniques such as fuzzy c-means, fuzzy decision tree systems, fuzzy genetic algorithms, and linear fuzzy rules generation methods. For tuning, we discuss Jang's ANFIS architecture, Berenji-Khedkar's GARIC architecture and its extensions in GARIC-Q. We show that the hybrid techniques capable of learning and tuning fuzzy rules, such as CART-ANFIS, RNN-FLCS, and GARIC-RB, are desirable in development of a number of future intelligent systems.

  18. Expert systems and fuzzy systems

    SciTech Connect

    Negoita, C.

    1985-01-01

    This book examines the design of the expert computer system and how fuzzy systems can be used to deal with imprecise information. As the author explores the effects of semantic systems on decision support systems, he asserts that the utilization of fuzzy set theory can help an expert system draw from its knowledge base more efficiently and therefore make more accurate and reliable decisions. The book includes realistic status reports in approximate reasoning and knowledge representation that are supported by a ''theory of categories'' mathematical approach. The differences between symbolic and semantic manipulation are outline, and detailed information is given on the actual theory of knowledge-based systems.

  19. The semantics of fuzzy logic

    NASA Technical Reports Server (NTRS)

    Ruspini, Enrique H.

    1991-01-01

    Summarized here are the results of recent research on the conceptual foundations of fuzzy logic. The focus is primarily on the principle characteristics of a model that quantifies resemblance between possible worlds by means of a similarity function that assigns a number between 0 and 1 to every pair of possible worlds. Introduction of such a function permits one to interpret the major constructs and methods of fuzzy logic: conditional and unconditional possibility and necessity distributions and the generalized modus ponens of Zadeh on the basis of related metric relationships between subsets of possible worlds.

  20. Fuzzy simulation in concurrent engineering

    NASA Technical Reports Server (NTRS)

    Kraslawski, A.; Nystrom, L.

    1992-01-01

    Concurrent engineering is becoming a very important practice in manufacturing. A problem in concurrent engineering is the uncertainty associated with the values of the input variables and operating conditions. The problem discussed in this paper concerns the simulation of processes where the raw materials and the operational parameters possess fuzzy characteristics. The processing of fuzzy input information is performed by the vertex method and the commercial simulation packages POLYMATH and GEMS. The examples are presented to illustrate the usefulness of the method in the simulation of chemical engineering processes.

  1. Universal fuzzy models and universal fuzzy controllers for discrete-time nonlinear systems.

    PubMed

    Gao, Qing; Feng, Gang; Dong, Daoyi; Liu, Lu

    2015-05-01

    This paper investigates the problems of universal fuzzy model and universal fuzzy controller for discrete-time nonaffine nonlinear systems (NNSs). It is shown that a kind of generalized T-S fuzzy model is the universal fuzzy model for discrete-time NNSs satisfying a sufficient condition. The results on universal fuzzy controllers are presented for two classes of discrete-time stabilizable NNSs. Constructive procedures are provided to construct the model reference fuzzy controllers. The simulation example of an inverted pendulum is presented to illustrate the effectiveness and advantages of the proposed method. These results significantly extend the approach for potential applications in solving complex engineering problems. PMID:25137736

  2. Fuzzy set applications in engineering optimization: Multilevel fuzzy optimization

    NASA Technical Reports Server (NTRS)

    Diaz, Alejandro R.

    1989-01-01

    A formulation for multilevel optimization with fuzzy objective functions is presented. With few exceptions, formulations for fuzzy optimization have dealt with a one-level problem in which the objective is the membership function of a fuzzy set formed by the fuzzy intersection of other sets. In the problem examined here, the goal set G is defined in a more general way, using an aggregation operator H that allows arbitrary combinations of set operations (union, intersection, addition) on the individual sets Gi. This is a straightforward extension of the standard form, but one that makes possible the modeling of interesting evaluation strategies. A second, more important departure from the standard form will be the construction of a multilevel problem analogous to the design decomposition problem in optimization. This arrangement facilitates the simulation of a system design process in which different components of the system are designed by different teams, and different levels of design detail become relevant at different time stages in the process: global design features early, local features later in the process.

  3. A fuzzy control design case: The fuzzy PLL

    NASA Technical Reports Server (NTRS)

    Teodorescu, H. N.; Bogdan, I.

    1992-01-01

    The aim of this paper is to present a typical fuzzy control design case. The analyzed controlled systems are the phase-locked loops (PLL's)--classic systems realized in both analogic and digital technology. The crisp PLL devices are well known.

  4. Fuzzy coordinator in control problems

    NASA Technical Reports Server (NTRS)

    Rueda, A.; Pedrycz, W.

    1992-01-01

    In this paper a hierarchical control structure using a fuzzy system for coordination of the control actions is studied. The architecture involves two levels of control: a coordination level and an execution level. Numerical experiments will be utilized to illustrate the behavior of the controller when it is applied to a nonlinear plant.

  5. Soft computing and fuzzy logic

    SciTech Connect

    Zadeh, L.A.

    1994-12-31

    Soft computing is a collection of methodologies that aim to exploit the tolerance for imprecision and uncertainty to achieve tractability, robustness, and low solution cost. Its principal constituents are fuzzy logic, neuro-computing, and probabilistic reasoning. Soft computing is likely to play an increasingly important role in many application areas, including software engineering. The role model for soft computing is the human mind.

  6. Fuzzy control system for a mobile robot

    SciTech Connect

    Hai Quan Dai; Dalton, G.R.; Tulenko, J. )

    1992-01-01

    Since the first fuzzy logic control system was proposed by Mamdani, many studies have been carried out on industrial process and real-time controls. The key problem for the application of fuzzy logic control is to find a suitable set of fuzzy control rules. Three common modes of deriving fuzzy control rules are often distinguished and mentioned: (1) expert experience and knowledge; (2) modeling operator control actions; and (3) modeling a process. In cases where an operator's skill is important, it is very useful to derive fuzzy control rules by modeling an operator's control actions. It is possible to model an operator's control behaviors in terms of fuzzy implications using the input-output data concerned with his/her control actions. The authors use the model obtained in this way as the basis for a fuzzy controller. The authors use a finite number of fuzzy or approximate control rules. To control a robot in a cluttered reactor environment, it is desirable to combine all the methods. In this paper, the authors describe a general algorithm for a mobile robot control system with fuzzy logic reasoning. They discuss the way that knowledge of fuzziness will be represented in this control system. They also describe a simulation program interface to the K2A Cybermation mobile robot to be used to demonstrate the control system.

  7. Fuzzy tree automata and syntactic pattern recognition.

    PubMed

    Lee, E T

    1982-04-01

    An approach of representing patterns by trees and processing these trees by fuzzy tree automata is described. Fuzzy tree automata are defined and investigated. The results include that the class of fuzzy root-to-frontier recognizable ¿-trees is closed under intersection, union, and complementation. Thus, the class of fuzzy root-to-frontier recognizable ¿-trees forms a Boolean algebra. Fuzzy tree automata are applied to processing fuzzy tree representation of patterns based on syntactic pattern recognition. The grade of acceptance is defined and investigated. Quantitative measures of ``approximate isosceles triangle,'' ``approximate elongated isosceles triangle,'' ``approximate rectangle,'' and ``approximate cross'' are defined and used in the illustrative examples of this approach. By using these quantitative measures, a house, a house with high roof, and a church are also presented as illustrative examples. In addition, three fuzzy tree automata are constructed which have the capability of processing the fuzzy tree representations of ``fuzzy houses,'' ``houses with high roofs,'' and ``fuzzy churches,'' respectively. The results may have useful applications in pattern recognition, image processing, artificial intelligence, pattern database design and processing, image science, and pictorial information systems. PMID:21869062

  8. Learning fuzzy logic control system

    NASA Technical Reports Server (NTRS)

    Lung, Leung Kam

    1994-01-01

    The performance of the Learning Fuzzy Logic Control System (LFLCS), developed in this thesis, has been evaluated. The Learning Fuzzy Logic Controller (LFLC) learns to control the motor by learning the set of teaching values that are generated by a classical PI controller. It is assumed that the classical PI controller is tuned to minimize the error of a position control system of the D.C. motor. The Learning Fuzzy Logic Controller developed in this thesis is a multi-input single-output network. Training of the Learning Fuzzy Logic Controller is implemented off-line. Upon completion of the training process (using Supervised Learning, and Unsupervised Learning), the LFLC replaces the classical PI controller. In this thesis, a closed loop position control system of a D.C. motor using the LFLC is implemented. The primary focus is on the learning capabilities of the Learning Fuzzy Logic Controller. The learning includes symbolic representation of the Input Linguistic Nodes set and Output Linguistic Notes set. In addition, we investigate the knowledge-based representation for the network. As part of the design process, we implement a digital computer simulation of the LFLCS. The computer simulation program is written in 'C' computer language, and it is implemented in DOS platform. The LFLCS, designed in this thesis, has been developed on a IBM compatible 486-DX2 66 computer. First, the performance of the Learning Fuzzy Logic Controller is evaluated by comparing the angular shaft position of the D.C. motor controlled by a conventional PI controller and that controlled by the LFLC. Second, the symbolic representation of the LFLC and the knowledge-based representation for the network are investigated by observing the parameters of the Fuzzy Logic membership functions and the links at each layer of the LFLC. While there are some limitations of application with this approach, the result of the simulation shows that the LFLC is able to control the angular shaft position of the

  9. Combinational reasoning of quantitative fuzzy topological relations for simple fuzzy regions.

    PubMed

    Liu, Bo; Li, Dajun; Xia, Yuanping; Ruan, Jian; Xu, Lili; Wu, Huanyi

    2015-01-01

    In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models. PMID:25775452

  10. Combinational Reasoning of Quantitative Fuzzy Topological Relations for Simple Fuzzy Regions

    PubMed Central

    Liu, Bo; Li, Dajun; Xia, Yuanping; Ruan, Jian; Xu, Lili; Wu, Huanyi

    2015-01-01

    In recent years, formalization and reasoning of topological relations have become a hot topic as a means to generate knowledge about the relations between spatial objects at the conceptual and geometrical levels. These mechanisms have been widely used in spatial data query, spatial data mining, evaluation of equivalence and similarity in a spatial scene, as well as for consistency assessment of the topological relations of multi-resolution spatial databases. The concept of computational fuzzy topological space is applied to simple fuzzy regions to efficiently and more accurately solve fuzzy topological relations. Thus, extending the existing research and improving upon the previous work, this paper presents a new method to describe fuzzy topological relations between simple spatial regions in Geographic Information Sciences (GIS) and Artificial Intelligence (AI). Firstly, we propose a new definition for simple fuzzy line segments and simple fuzzy regions based on the computational fuzzy topology. And then, based on the new definitions, we also propose a new combinational reasoning method to compute the topological relations between simple fuzzy regions, moreover, this study has discovered that there are (1) 23 different topological relations between a simple crisp region and a simple fuzzy region; (2) 152 different topological relations between two simple fuzzy regions. In the end, we have discussed some examples to demonstrate the validity of the new method, through comparisons with existing fuzzy models, we showed that the proposed method can compute more than the existing models, as it is more expressive than the existing fuzzy models. PMID:25775452

  11. Analysis of inventory difference using fuzzy controllers

    SciTech Connect

    Zardecki, A.

    1994-08-01

    The principal objectives of an accounting system for safeguarding nuclear materials are as follows: (a) to provide assurance that all material quantities are present in the correct amount; (b) to provide timely detection of material loss; and (c) to estimate the amount of any loss and its location. In fuzzy control, expert knowledge is encoded in the form of fuzzy rules, which describe recommended actions for different classes of situations represented by fuzzy sets. The concept of a fuzzy controller is applied to the forecasting problem in a time series, specifically, to forecasting and detecting anomalies in inventory differences. This paper reviews the basic notion underlying the fuzzy control systems and provides examples of application. The well-known material-unaccounted-for diffusion plant data of Jaech are analyzed using both feedforward neural networks and fuzzy controllers. By forming a deference between the forecasted and observed signals, an efficient method to detect small signals in background noise is implemented.

  12. (Fuzzy) Ideals of BN-Algebras

    PubMed Central

    Walendziak, Andrzej

    2015-01-01

    The notions of an ideal and a fuzzy ideal in BN-algebras are introduced. The properties and characterizations of them are investigated. The concepts of normal ideals and normal congruences of a BN-algebra are also studied, the properties of them are displayed, and a one-to-one correspondence between them is presented. Conditions for a fuzzy set to be a fuzzy ideal are given. The relationships between ideals and fuzzy ideals of a BN-algebra are established. The homomorphic properties of fuzzy ideals of a BN-algebra are provided. Finally, characterizations of Noetherian BN-algebras and Artinian BN-algebras via fuzzy ideals are obtained. PMID:26125050

  13. Predictive fuzzy controller for robotic motion control

    SciTech Connect

    Huang, S.J.; Hu, C.F.

    1995-12-31

    A system output prediction strategy incorporated with a fuzzy controller is proposed to manipulate the robotic motion control. Usually, the current position and velocity errors are used to operate the fuzzy logic controller for picking out a corresponding rule. When the system has fast planning speed or time varying behavior, the required tracking accuracy is difficult to achieve by adjusting the fuzzy rules. In order to improve the position control accuracy and system robustness for the industrial application, the current position error in the fuzzy rules look-up table is substituted by the predictive position error of the next step by using the grey predictive algorithm. This idea is implemented on a five degrees of freedom robot. The experimental results show that this fuzzy controller has effectively improve the system performance and achieved the facilitation of fuzzy controller implementation.

  14. Fuzzy multiple linear regression: A computational approach

    NASA Technical Reports Server (NTRS)

    Juang, C. H.; Huang, X. H.; Fleming, J. W.

    1992-01-01

    This paper presents a new computational approach for performing fuzzy regression. In contrast to Bardossy's approach, the new approach, while dealing with fuzzy variables, closely follows the conventional regression technique. In this approach, treatment of fuzzy input is more 'computational' than 'symbolic.' The following sections first outline the formulation of the new approach, then deal with the implementation and computational scheme, and this is followed by examples to illustrate the new procedure.

  15. Refining fuzzy logic controllers with machine learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1994-01-01

    In this paper, we describe the GARIC (Generalized Approximate Reasoning-Based Intelligent Control) architecture, which learns from its past performance and modifies the labels in the fuzzy rules to improve performance. It uses fuzzy reinforcement learning which is a hybrid method of fuzzy logic and reinforcement learning. This technology can simplify and automate the application of fuzzy logic control to a variety of systems. GARIC has been applied in simulation studies of the Space Shuttle rendezvous and docking experiments. It has the potential of being applied in other aerospace systems as well as in consumer products such as appliances, cameras, and cars.

  16. Fuzzy logic control for camera tracking system

    NASA Technical Reports Server (NTRS)

    Lea, Robert N.; Fritz, R. H.; Giarratano, J.; Jani, Yashvant

    1992-01-01

    A concept utilizing fuzzy theory has been developed for a camera tracking system to provide support for proximity operations and traffic management around the Space Station Freedom. Fuzzy sets and fuzzy logic based reasoning are used in a control system which utilizes images from a camera and generates required pan and tilt commands to track and maintain a moving target in the camera's field of view. This control system can be implemented on a fuzzy chip to provide an intelligent sensor for autonomous operations. Capabilities of the control system can be expanded to include approach, handover to other sensors, caution and warning messages.

  17. Fuzzy control of small servo motors

    NASA Technical Reports Server (NTRS)

    Maor, Ron; Jani, Yashvant

    1993-01-01

    To explore the benefits of fuzzy logic and understand the differences between the classical control methods and fuzzy control methods, the Togai InfraLogic applications engineering staff developed and implemented a motor control system for small servo motors. The motor assembly for testing the fuzzy and conventional controllers consist of servo motor RA13M and an encoder with a range of 4096 counts. An interface card was designed and fabricated to interface the motor assembly and encoder to an IBM PC. The fuzzy logic based motor controller was developed using the TILShell and Fuzzy C Development System on an IBM PC. A Proportional-Derivative (PD) type conventional controller was also developed and implemented in the IBM PC to compare the performance with the fuzzy controller. Test cases were defined to include step inputs of 90 and 180 degrees rotation, sine and square wave profiles in 5 to 20 hertz frequency range, as well as ramp inputs. In this paper we describe our approach to develop a fuzzy as well as PH controller, provide details of hardware set-up and test cases, and discuss the performance results. In comparison, the fuzzy logic based controller handles the non-linearities of the motor assembly very well and provides excellent control over a broad range of parameters. Fuzzy technology, as indicated by our results, possesses inherent adaptive features.

  18. Fuzzy logic control and optimization system

    DOEpatents

    Lou, Xinsheng

    2012-04-17

    A control system (300) for optimizing a power plant includes a chemical loop having an input for receiving an input signal (369) and an output for outputting an output signal (367), and a hierarchical fuzzy control system (400) operably connected to the chemical loop. The hierarchical fuzzy control system (400) includes a plurality of fuzzy controllers (330). The hierarchical fuzzy control system (400) receives the output signal (367), optimizes the input signal (369) based on the received output signal (367), and outputs an optimized input signal (369) to the input of the chemical loop to control a process of the chemical loop in an optimized manner.

  19. A fuzzy classifier system for process control

    NASA Technical Reports Server (NTRS)

    Karr, C. L.; Phillips, J. C.

    1994-01-01

    A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

  20. Fuzzy set classifier for waste classification tracking

    SciTech Connect

    Gavel, D.T.

    1992-11-04

    We have developed an expert system based on fuzzy logic theory to fuse the data from multiple sensors and make classification decisions for objects in a waste reprocessing stream. Fuzzy set theory has been applied in decision and control applications with some success, particularly by the Japanese. We have found that the fuzzy logic system is rather easy to design and train, a feature that can cut development costs considerably. With proper training, the classification accuracy is quite high. We performed several tests sorting radioactive test samples using a gamma spectrometer to compare fuzzy logic to more conventional sorting schemes.

  1. Indirect Adaptive Fuzzy Power System Stabilizer

    NASA Astrophysics Data System (ADS)

    Saoudi, Kamel; Bouchama, Ziad; Harmas, Mohamed Naguib; Zehar, Khaled

    2008-06-01

    A power system stabilizer based on adaptive fuzzy technique is presented. The design of a fuzzy logic power system stabilizer (FLPSS) requires the collection of fuzzy IF-THEN rules which are used to initialize an adaptive fuzzy power system AFPSS. The rule-base can be then tuned on-line so that the stabilizer can adapt to the different operating conditions occurring in the power system. The adaptation laws are developed based on a Lyapunov synthesis approach. Assessing the validity of this technique simulation of a power system is conducted and results are discussed.

  2. A proposal of fuzzy connective with learning function and its application to fuzzy retrieval system

    NASA Technical Reports Server (NTRS)

    Hayashi, Isao; Naito, Eiichi; Ozawa, Jun; Wakami, Noboru

    1993-01-01

    A new fuzzy connective and a structure of network constructed by fuzzy connectives are proposed to overcome a drawback of conventional fuzzy retrieval systems. This network represents a retrieval query and the fuzzy connectives in networks have a learning function to adjust its parameters by data from a database and outputs of a user. The fuzzy retrieval systems employing this network are also constructed. Users can retrieve results even with a query whose attributes do not exist in a database schema and can get satisfactory results for variety of thinkings by learning function.

  3. Consistency of crisp and fuzzy pairwise comparison matrix using fuzzy preference programming

    NASA Astrophysics Data System (ADS)

    Aminuddin, Adam Shariff Adli; Nawawi, Mohd Kamal Mohd

    2014-12-01

    In this paper, the consistency of crisp pairwise comparison matrix is compared with the fuzzy pairwise comparison matrix of Analytic Network Process (ANP). The fuzzy input in the form of triangular membership function is converted into crisp value using Fuzzy Preference Programming (FPP) method which is implemented using MATLAB. The consistency ratio (CR) for both of the crisp and fuzzy pairwise comparison matrix is calculated using SuperDecisions. Main finding shows that the involvement of fuzzy elements into the decision maker's judgment can reduce the inconsistency of the pairwise comparison matrix compared with the crisp judgment.

  4. Fuzzy fractals, chaos, and noise

    SciTech Connect

    Zardecki, A.

    1997-05-01

    To distinguish between chaotic and noisy processes, the authors analyze one- and two-dimensional chaotic mappings, supplemented by the additive noise terms. The predictive power of a fuzzy rule-based system allows one to distinguish ergodic and chaotic time series: in an ergodic series the likelihood of finding large numbers is small compared to the likelihood of finding them in a chaotic series. In the case of two dimensions, they consider the fractal fuzzy sets whose {alpha}-cuts are fractals, arising in the context of a quadratic mapping in the extended complex plane. In an example provided by the Julia set, the concept of Hausdorff dimension enables one to decide in favor of chaotic or noisy evolution.

  5. Collaborating Fuzzy Reinforcement Learning Agents

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1997-01-01

    Earlier, we introduced GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Relearning and at the local level, each agent learns and operates based on ANTARCTIC, a technique for fuzzy reinforcement learning. In this paper, we show that it is possible for these agents to compete in order to affect the selected control policy but at the same time, they can collaborate while investigating the state space. In this model, the evaluator or the critic learns by observing all the agents behaviors but the control policy changes only based on the behavior of the winning agent also known as the super agent.

  6. Weighted Fuzzy Interpolative Reasoning Based on the Slopes of Fuzzy Sets and Particle Swarm Optimization Techniques.

    PubMed

    Chen, Shyi-Ming; Hsin, Wen-Chyuan

    2015-07-01

    In this paper, we propose a new weighted fuzzy interpolative reasoning method for sparse fuzzy rule-based systems based on the slopes of fuzzy sets. We also propose a particle swarm optimization (PSO)-based weights-learning algorithm to automatically learn the optimal weights of the antecedent variables of fuzzy rules for weighted fuzzy interpolative reasoning. We apply the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm to deal with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems. The experimental results show that the proposed weighted fuzzy interpolative reasoning method using the proposed PSO-based weights-learning algorithm outperforms the existing methods for dealing with the computer activity prediction problem, the multivariate regression problems, and the time series prediction problems. PMID:25204003

  7. A Combination of Extended Fuzzy AHP and Fuzzy GRA for Government E-Tendering in Hybrid Fuzzy Environment

    PubMed Central

    Wang, Yan; Xi, Chengyu; Zhang, Shuai; Yu, Dejian; Zhang, Wenyu; Li, Yong

    2014-01-01

    The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach. PMID:25057506

  8. A combination of extended fuzzy AHP and fuzzy GRA for government E-tendering in hybrid fuzzy environment.

    PubMed

    Wang, Yan; Xi, Chengyu; Zhang, Shuai; Yu, Dejian; Zhang, Wenyu; Li, Yong

    2014-01-01

    The recent government tendering process being conducted in an electronic way is becoming an inevitable affair for numerous governmental agencies to further exploit the superiorities of conventional tendering. Thus, developing an effective web-based bid evaluation methodology so as to realize an efficient and effective government E-tendering (GeT) system is imperative. This paper firstly investigates the potentiality of employing fuzzy analytic hierarchy process (AHP) along with fuzzy gray relational analysis (GRA) for optimal selection of candidate tenderers in GeT process with consideration of a hybrid fuzzy environment with incomplete weight information. We proposed a novel hybrid fuzzy AHP-GRA (HFAHP-GRA) method that combines an extended fuzzy AHP with a modified fuzzy GRA. The extended fuzzy AHP which combines typical AHP with interval AHP is proposed to obtain the exact weight information, and the modified fuzzy GRA is applied to aggregate different types of evaluation information so as to identify the optimal candidate tenderers. Finally, a prototype system is built and validated with an illustrative example for GeT to confirm the feasibility of our approach. PMID:25057506

  9. Fuzzy logic based robotic controller

    NASA Technical Reports Server (NTRS)

    Attia, F.; Upadhyaya, M.

    1994-01-01

    Existing Proportional-Integral-Derivative (PID) robotic controllers rely on an inverse kinematic model to convert user-specified cartesian trajectory coordinates to joint variables. These joints experience friction, stiction, and gear backlash effects. Due to lack of proper linearization of these effects, modern control theory based on state space methods cannot provide adequate control for robotic systems. In the presence of loads, the dynamic behavior of robotic systems is complex and nonlinear, especially where mathematical modeling is evaluated for real-time operators. Fuzzy Logic Control is a fast emerging alternative to conventional control systems in situations where it may not be feasible to formulate an analytical model of the complex system. Fuzzy logic techniques track a user-defined trajectory without having the host computer to explicitly solve the nonlinear inverse kinematic equations. The goal is to provide a rule-based approach, which is closer to human reasoning. The approach used expresses end-point error, location of manipulator joints, and proximity to obstacles as fuzzy variables. The resulting decisions are based upon linguistic and non-numerical information. This paper presents a solution to the conventional robot controller which is independent of computationally intensive kinematic equations. Computer simulation results of this approach as obtained from software implementation are also discussed.

  10. Fuzzy Control of Robotic Arm

    NASA Astrophysics Data System (ADS)

    Lin, Kyaw Kyaw; Soe, Aung Kyaw; Thu, Theint Theint

    2008-10-01

    This research work investigates a Self-Tuning Proportional Derivative (PD) type Fuzzy Logic Controller (STPDFLC) for a two link robot system. The proposed scheme adjusts on-line the output Scaling Factor (SF) by fuzzy rules according to the current trend of the robot. The rule base for tuning the output scaling factor is defined on the error (e) and change in error (de). The scheme is also based on the fact that the controller always tries to manipulate the process input. The rules are in the familiar if-then format. All membership functions for controller inputs (e and de) and controller output (UN) are defined on the common interval [-1,1]; whereas the membership functions for the gain updating factor (α) is defined on [0,1]. There are various methods to calculate the crisp output of the system. Center of Gravity (COG) method is used in this application due to better results it gives. Performances of the proposed STPDFLC are compared with those of their corresponding PD-type conventional Fuzzy Logic Controller (PDFLC). The proposed scheme shows a remarkably improved performance over its conventional counterpart especially under parameters variation (payload). The two-link results of analysis are simulated. These simulation results are illustrated by using MATLAB® programming.

  11. The Fuzzy Model for Diagnosis of Animal Disease

    NASA Astrophysics Data System (ADS)

    Jianhua, Xiao; Luyi, Shi; Yu, Zhang; Li, Gao; Honggang, Fan; Haikun, Ma; Hongbin, Wang

    The knowledge of animal disease diagnosis was fuzzy; the fuzzy model can imitate the character of clinical diagnosis for veterinary. The fuzzy model of disease, the methods for class the disease group of differential diagnosis and the fuzzy diagnosis model were discussed in this paper.

  12. Image segmentation using trainable fuzzy set classifiers

    NASA Astrophysics Data System (ADS)

    Schalkoff, Robert J.; Carver, Albrecht E.; Gurbuz, Sabri

    1999-07-01

    A general image analysis and segmentation method using fuzzy set classification and learning is described. The method uses a learned fuzzy representation of pixel region characteristics, based upon the conjunction and disjunction of extracted and derived fuzzy color and texture features. Both positive and negative exemplars of some visually apparent characteristic which forms the basis of the inspection, input by a human operator, are used together with a clustering algorithm to construct positive similarity membership functions and negative similarity membership functions. Using these composite fuzzified images, P and N, are produced using fuzzy union. Classification is accomplished via image defuzzification, whereby linguistic meaning is assigned to each pixel in the fuzzy set using a fuzzy inference operation. The technique permits: (1) strict color and texture discrimination, (2) machine learning of color and texture characteristics of regions, (3) and judicious labeling of each pixel based upon leaned fuzzy representation and fuzzy classification. This approach appears ideal for applications involving visual inspection and allows the development of image-based inspection systems which may be trained and used by relatively unskilled workers. We show three different examples involving the visual inspection of mixed waste drums, lumber and woven fabric.

  13. Modeling Research Project Risks with Fuzzy Maps

    ERIC Educational Resources Information Center

    Bodea, Constanta Nicoleta; Dascalu, Mariana Iuliana

    2009-01-01

    The authors propose a risks evaluation model for research projects. The model is based on fuzzy inference. The knowledge base for fuzzy process is built with a causal and cognitive map of risks. The map was especially developed for research projects, taken into account their typical lifecycle. The model was applied to an e-testing research…

  14. Fuzzy logic mode switching in helicopters

    NASA Technical Reports Server (NTRS)

    Sherman, Porter D.; Warburton, Frank W.

    1993-01-01

    The application of fuzzy logic to a wide range of control problems has been gaining momentum internationally, fueled by a concentrated Japanese effort. Advanced Research & Development within the Engineering Department at Sikorsky Aircraft undertook a fuzzy logic research effort designed to evaluate how effective fuzzy logic control might be in relation to helicopter operations. The mode switching module in the advanced flight control portion of Sikorsky's motion based simulator was identified as a good candidate problem because it was simple to understand and contained imprecise (fuzzy) decision criteria. The purpose of the switching module is to aid a helicopter pilot in entering and leaving coordinated turns while in flight. The criteria that determine the transitions between modes are imprecise and depend on the varied ranges of three flight conditions (i.e., simulated parameters): Commanded Rate, Duration, and Roll Attitude. The parameters were given fuzzy ranges and used as input variables to a fuzzy rulebase containing the knowledge of mode switching. The fuzzy control program was integrated into a real time interactive helicopter simulation tool. Optimization of the heading hold and turn coordination was accomplished by interactive pilot simulation testing of the handling quality performance of the helicopter dynamic model. The fuzzy logic code satisfied all the requirements of this candidate control problem.

  15. Approximation abilities of neuro-fuzzy networks

    NASA Astrophysics Data System (ADS)

    Mrówczyńska, Maria

    2010-01-01

    The paper presents the operation of two neuro-fuzzy systems of an adaptive type, intended for solving problems of the approximation of multi-variable functions in the domain of real numbers. Neuro-fuzzy systems being a combination of the methodology of artificial neural networks and fuzzy sets operate on the basis of a set of fuzzy rules "if-then", generated by means of the self-organization of data grouping and the estimation of relations between fuzzy experiment results. The article includes a description of neuro-fuzzy systems by Takaga-Sugeno-Kang (TSK) and Wang-Mendel (WM), and in order to complement the problem in question, a hierarchical structural self-organizing method of teaching a fuzzy network. A multi-layer structure of the systems is a structure analogous to the structure of "classic" neural networks. In its final part the article presents selected areas of application of neuro-fuzzy systems in the field of geodesy and surveying engineering. Numerical examples showing how the systems work concerned: the approximation of functions of several variables to be used as algorithms in the Geographic Information Systems (the approximation of a terrain model), the transformation of coordinates, and the prediction of a time series. The accuracy characteristics of the results obtained have been taken into consideration.

  16. A Fuzzy Model of Document Retrieval Systems

    ERIC Educational Resources Information Center

    Tahani, Valiollah

    1976-01-01

    This paper is concerned with the organization and retrieval of records in document retrieval systems which admit of imprecision in the form of fuzziness in document characterization and retrieval rules. A mathematical model for such systems, based on the theory of fuzzy sets, is introduced. (Author)

  17. Inducing Fuzzy Models for Student Classification

    ERIC Educational Resources Information Center

    Nykanen, Ossi

    2006-01-01

    We report an approach for implementing predictive fuzzy systems that manage capturing both the imprecision of the empirically induced classifications and the imprecision of the intuitive linguistic expressions via the extensive use of fuzzy sets. From end-users' point of view, the approach enables encapsulating the technical details of the…

  18. Homeopathic drug selection using Intuitionistic fuzzy sets.

    PubMed

    Kharal, Athar

    2009-01-01

    Using intuitionistic fuzzy set theory, Sanchez's approach to medical diagnosis has been applied to the problem of selection of single remedy from homeopathic repertorization. Two types of Intuitionistic Fuzzy Relations (IFRs) and three types of selection indices are discussed. I also propose a new repertory exploiting the benefits of soft-intelligence. PMID:19135957

  19. Transportation Optimization with Fuzzy Trapezoidal Numbers Based on Possibility Theory

    PubMed Central

    He, Dayi; Li, Ran; Huang, Qi; Lei, Ping

    2014-01-01

    In this paper, a parametric method is introduced to solve fuzzy transportation problem. Considering that parameters of transportation problem have uncertainties, this paper develops a generalized fuzzy transportation problem with fuzzy supply, demand and cost. For simplicity, these parameters are assumed to be fuzzy trapezoidal numbers. Based on possibility theory and consistent with decision-makers' subjectiveness and practical requirements, the fuzzy transportation problem is transformed to a crisp linear transportation problem by defuzzifying fuzzy constraints and objectives with application of fractile and modality approach. Finally, a numerical example is provided to exemplify the application of fuzzy transportation programming and to verify the validity of the proposed methods. PMID:25137239

  20. Use of fuzzy logic in lignite inventory estimation

    SciTech Connect

    Tutmez, B.; Dag, A.

    2007-07-01

    Seam thickness is one of the most important parameters for reserve estimation of a lignite deposit. This paper addresses a case study on fuzzy estimation of lignite seam thickness from spatial coordinates. From the relationships between input (Cartesian coordinates) and output (thickness) parameters, fuzzy clustering and a fuzzy rule-based inference system were designed. Data-driven fuzzy model parameters were derived from numerical values directly. In addition, estimations of the fuzzy model were compared with kriging estimations. It was concluded that the performance ofthe fuzzy model was more satisfactory. The results indicated that the fuzzy modeling approach is very reliable for the estimation of lignite reserves.

  1. Fuzzy complexes: Specific binding without complete folding.

    PubMed

    Sharma, Rashmi; Raduly, Zsolt; Miskei, Marton; Fuxreiter, Monika

    2015-09-14

    Specific molecular recognition is assumed to require a well-defined set of contacts and devoid of conformational and interaction ambiguities. Growing experimental evidence demonstrates however, that structural multiplicity or dynamic disorder can be retained in protein complexes, termed as fuzziness. Fuzzy regions establish alternative contacts between specific partners usually via transient interactions. Nature often tailors the dynamic properties of these segments via post-translational modifications or alternative splicing to fine-tune affinity. Most experimentally characterized fuzzy complexes are involved in regulation of gene-expression, signal transduction and cell-cycle regulation. Fuzziness is also characteristic to viral protein complexes, cytoskeleton structure, and surprisingly in a few metabolic enzymes. A plausible role of fuzzy complexes in increasing half-life of intrinsically disordered proteins is also discussed. PMID:26226339

  2. Pattern classification using fuzzy relational calculus.

    PubMed

    Ray, K S; Dinda, T K

    2003-01-01

    Our aim is to design a pattern classifier using fuzzy relational calculus (FRC) which was initially proposed by Pedrycz (1990). In the course of doing so, we first consider a particular interpretation of the multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. Subsequently, we introduce the notion of a fuzzy pattern vector to represent a population of training patterns in the pattern space and to denote the antecedent part of the said particular interpretation of the MFI. We introduce a new approach to the computation of the derivative of the fuzzy max-function and min-function using the concept of a generalized function. During the construction of the classifier based on FRC, we use fuzzy linguistic statements (or fuzzy membership function to represent the linguistic statement) to represent the values of features (e.g., feature F/sub 1/ is small and F/sub 2/ is big) for a population of patterns. Note that the construction of the classifier essentially depends on the estimate of a fuzzy relation /spl Rfr/ between the input (fuzzy set) and output (fuzzy set) of the classifier. Once the classifier is constructed, the nonfuzzy features of a pattern can be classified. At the time of classification of the nonfuzzy features of the testpatterns, we use the concept of fuzzy masking to fuzzify the nonfuzzy feature values of the testpatterns. The performance of the proposed scheme is tested on synthetic data. Finally, we use the proposed scheme for the vowel classification problem of an Indian language. PMID:18238152

  3. Chaotic neurochips for fuzzy computing

    NASA Astrophysics Data System (ADS)

    Szu, Harold H.; Zadeh, Lotfi A.; Hsu, Charles C.; DeWitte, Joseph T., Jr.; Moon, Gyu; Gobovic, Desa; Zaghloul, Mona E.

    1994-03-01

    A massive chaotic neural network (CNN) is demonstrated with a fixed-point Hebbian synaptic weight dynamic: an instantaneous input, and a piecewise negative logic output. The variable slope of the output versus the input becomes a software control of the collective chaos hardware. Two applications are given. The mean synaptic weight field plays an important role for fast pattern recognition capability in examples of both the habituation and the novelty detections. Another novel usage of CNN is to be a bridge between neural learning and learnable fuzzy logic.

  4. Energy partitioning for ``fuzzy'' atoms

    NASA Astrophysics Data System (ADS)

    Salvador, P.; Mayer, I.

    2004-03-01

    The total energy of a molecule is presented as a sum of one- and two-atomic energy components in terms of "fuzzy" atoms, i.e., such divisions of the three-dimensional physical space into atomic regions in which the regions assigned to the individual atoms have no sharp boundaries but exhibit a continuous transition from one to another. By proper definitions the energy components are on the chemical energy scale. The method is realized by using Becke's integration scheme and weight function permitting very effective numerical integrations.

  5. Fuzzy-Kohonen-clustering neural network trained by genetic algorithm and fuzzy competition learning

    NASA Astrophysics Data System (ADS)

    Xie, Weixing; Li, Wenhua; Gao, Xinbo

    1995-08-01

    Kohonen networks are well known for clustering analysis. Classical Kohonen networks for hard c-means clustering (trained by winner-take-all learning) have some severe drawbacks. Fuzzy Kohonen networks (FKCNN) for fuzzy c-means clustering are trained by fuzzy competition learning, and can get better clustering results than the classical Kohonen networks. However, both winner-take-all and fuzzy competition learning algorithms are in essence local search techniques that search for the optimum by using a hill-climbing technique. Thus, they often fail in the search for the global optimum. In this paper we combine genetic algorithms (GAs) with fuzzy competition learning to train the FKCNN. Our experimental results show that the proposed GA/FC learning algorithm has much higher probabilities of finding the global optimal solutions than either the winner-take-all or the fuzzy competition learning.

  6. Fuzzy probabilistic design of water distribution networks

    NASA Astrophysics Data System (ADS)

    Fu, Guangtao; Kapelan, Zoran

    2011-05-01

    The primary aim of this paper is to present a fuzzy probabilistic approach for optimal design and rehabilitation of water distribution systems, combining aleatoric and epistemic uncertainties in a unified framework. The randomness and imprecision in future water consumption are characterized using fuzzy random variables whose realizations are not real but fuzzy numbers, and the nodal head requirements are represented by fuzzy sets, reflecting the imprecision in customers' requirements. The optimal design problem is formulated as a two-objective optimization problem, with minimization of total design cost and maximization of system performance as objectives. The system performance is measured by the fuzzy random reliability, defined as the probability that the fuzzy head requirements are satisfied across all network nodes. The satisfactory degree is represented by necessity measure or belief measure in the sense of the Dempster-Shafer theory of evidence. An efficient algorithm is proposed, within a Monte Carlo procedure, to calculate the fuzzy random system reliability and is effectively combined with the nondominated sorting genetic algorithm II (NSGAII) to derive the Pareto optimal design solutions. The newly proposed methodology is demonstrated with two case studies: the New York tunnels network and Hanoi network. The results from both cases indicate that the new methodology can effectively accommodate and handle various aleatoric and epistemic uncertainty sources arising from the design process and can provide optimal design solutions that are not only cost-effective but also have higher reliability to cope with severe future uncertainties.

  7. Design of supply chain in fuzzy environment

    NASA Astrophysics Data System (ADS)

    Rao, Kandukuri Narayana; Subbaiah, Kambagowni Venkata; Singh, Ganja Veera Pratap

    2013-05-01

    Nowadays, customer expectations are increasing and organizations are prone to operate in an uncertain environment. Under this uncertain environment, the ultimate success of the firm depends on its ability to integrate business processes among supply chain partners. Supply chain management emphasizes cross-functional links to improve the competitive strategy of organizations. Now, companies are moving from decoupled decision processes towards more integrated design and control of their components to achieve the strategic fit. In this paper, a new approach is developed to design a multi-echelon, multi-facility, and multi-product supply chain in fuzzy environment. In fuzzy environment, mixed integer programming problem is formulated through fuzzy goal programming in strategic level with supply chain cost and volume flexibility as fuzzy goals. These fuzzy goals are aggregated using minimum operator. In tactical level, continuous review policy for controlling raw material inventories in supplier echelon and controlling finished product inventories in plant as well as distribution center echelon is considered as fuzzy goals. A non-linear programming model is formulated through fuzzy goal programming using minimum operator in the tactical level. The proposed approach is illustrated with a numerical example.

  8. Security analysis for fingerprint fuzzy vaults

    NASA Astrophysics Data System (ADS)

    Hartloff, Jesse; Bileschi, Maxwell; Tulyakov, Sergey; Dobler, Jimmy; Rudra, Atri; Govindaraju, Venu

    2013-05-01

    In this work we place some of the traditional biometrics work on fingerprint verification via the fuzzy vault scheme within a cryptographic framework. We show that the breaking of a fuzzy vault leads to decoding of Reed-Solomon codes from random errors, which has been proposed as a hard problem in the cryptography community. We provide a security parameter for the fuzzy vault in terms of the decoding problem, which gives context for the breaking of the fuzzy vault, whereas most of the existing literature measures the strength of the fuzzy vault in terms of its resistance to pre-defined attacks or by the entropy of the vault. We keep track of our security parameter, and provide it alongside ROC statistics. We also aim to be more aware of the nature of the fingerprints when placing them in the fuzzy vault, noting that the distribution of minutiae is far from uniformly random. The results we show provide additional support that the fuzzy vault can be a viable scheme for secure fingerprint verification.

  9. Finding the maximal membership in a fuzzy set of an element from another fuzzy set

    NASA Astrophysics Data System (ADS)

    Yager, Ronald R.

    2010-11-01

    The problem of finding the maximal membership grade in a fuzzy set of an element from another fuzzy set is an important class of optimisation problems manifested in the real world by situations in which we try to find what is the optimal financial satisfaction we can get from a socially responsible investment. Here, we provide a solution to this problem. We then look at the proposed solution for fuzzy sets with various types of membership grades, ordinal, interval value and intuitionistic.

  10. Concurrent engineering use of fuzzy logic

    SciTech Connect

    Feraille, F.; Chedmail, P.

    1996-12-31

    In concurrent engineering a crucial problem is the management of conflicts. To avoid conflicts between the different viewpoints and activities, the usual tools and methods are rather poor. As we can group all the constraints from several viewpoints, and solve the global problem. But it`s difficult to manage the associated mathematical problem. However as proposed, every designer who is acting at the design of the product solves his own problem, the different solutions are collected. But in this case, we can`t avoid clash or conflicts. Therefore we propose a new approach including fuzzy design environment. On an example, we present a fuzzy environment approach to design in a concurrent engineering context. So we can reduce clashes between viewpoints. After this, we present the tools we need to design with fuzzy variables. First, we summarize the usual method to optimize a problem with fuzzy constraints or parameters, giving an usual solution vector. Secondly, we introduce the concept of fuzzy solutions-set of an optimization problem. This concept is a generalization of the {open_quotes}solution family{close_quotes} notion as proposed. Ours is stronger because we attach to the solution vector X a satisfaction function {mu}(X) which mathematically describes a fuzzy solution set. We also propose a method to obtain such sets. Third, we present how to collect the different fuzzy solutions-sets from different viewpoints, in order to obtain the global fuzzy solutions-set of a design. We must pay attention to the fact that two viewpoints may have several common variables. Finally we describe the exploration of this global fuzzy solutions-set by different viewpoints using such variables as parameters for a new optimization of concurrent engineering.

  11. Robust H∞ fuzzy control of a class of fuzzy bilinear systems with time-delay

    NASA Astrophysics Data System (ADS)

    Tsai, S.-H.; Li, T.-H. S.

    2008-02-01

    This paper presents robust H∞ fuzzy controllers for a class of T-S fuzzy bilinear systems (FBSs) with time-delay. First, the parallel distributed compensation (PDC) method is adopted to design a fuzzy controller which ensures the robust asymptotic stability of the FBS with time-delay and guarantees an H∞ norm bound constraint on disturbance attenuation. Based on the Schur complement and some variable transformation, the stability conditions of the overall fuzzy control system are formulated by linear matrix inequalities (LMIs). Finally, the validity and effectiveness of the proposed schemes are demonstrated by the simulation.

  12. Fuzzy mixed assembly line sequencing and scheduling optimization model using multiobjective dynamic fuzzy GA.

    PubMed

    Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

    2014-01-01

    A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

  13. Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

    PubMed Central

    Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

    2014-01-01

    A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

  14. Fuzzy Hybrid Deliberative/Reactive Paradigm (FHDRP)

    NASA Technical Reports Server (NTRS)

    Sarmadi, Hengameth

    2004-01-01

    This work aims to introduce a new concept for incorporating fuzzy sets in hybrid deliberative/reactive paradigm. After a brief review on basic issues of hybrid paradigm the definition of agent-based fuzzy hybrid paradigm, which enables the agents to proceed and extract their behavior through quantitative numerical and qualitative knowledge and to impose their decision making procedure via fuzzy rule bank, is discussed. Next an example performs a more applied platform for the developed approach and finally an overview of the corresponding agents architecture enhances agents logical framework.

  15. Adaptive Fuzzy Systems in Computational Intelligence

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1996-01-01

    In recent years, the interest in computational intelligence techniques, which currently includes neural networks, fuzzy systems, and evolutionary programming, has grown significantly and a number of their applications have been developed in the government and industry. In future, an essential element in these systems will be fuzzy systems that can learn from experience by using neural network in refining their performances. The GARIC architecture, introduced earlier, is an example of a fuzzy reinforcement learning system which has been applied in several control domains such as cart-pole balancing, simulation of to Space Shuttle orbital operations, and tether control. A number of examples from GARIC's applications in these domains will be demonstrated.

  16. Image Edge Extraction via Fuzzy Reasoning

    NASA Technical Reports Server (NTRS)

    Dominquez, Jesus A. (Inventor); Klinko, Steve (Inventor)

    2008-01-01

    A computer-based technique for detecting edges in gray level digital images employs fuzzy reasoning to analyze whether each pixel in an image is likely on an edge. The image is analyzed on a pixel-by-pixel basis by analyzing gradient levels of pixels in a square window surrounding the pixel being analyzed. An edge path passing through the pixel having the greatest intensity gradient is used as input to a fuzzy membership function, which employs fuzzy singletons and inference rules to assigns a new gray level value to the pixel that is related to the pixel's edginess degree.

  17. Fuzzy geometry, entropy, and image information

    NASA Technical Reports Server (NTRS)

    Pal, Sankar K.

    1991-01-01

    Presented here are various uncertainty measures arising from grayness ambiguity and spatial ambiguity in an image, and their possible applications as image information measures. Definitions are given of an image in the light of fuzzy set theory, and of information measures and tools relevant for processing/analysis e.g., fuzzy geometrical properties, correlation, bound functions and entropy measures. Also given is a formulation of algorithms along with management of uncertainties for segmentation and object extraction, and edge detection. The output obtained here is both fuzzy and nonfuzzy. Ambiguity in evaluation and assessment of membership function are also described.

  18. Fuzzy α-minimum spanning tree problem: definition and solutions

    NASA Astrophysics Data System (ADS)

    Zhou, Jian; Chen, Lu; Wang, Ke; Yang, Fan

    2016-04-01

    In this paper, the minimum spanning tree problem is investigated on the graph with fuzzy edge weights. The notion of fuzzy ? -minimum spanning tree is presented based on the credibility measure, and then the solutions of the fuzzy ? -minimum spanning tree problem are discussed under different assumptions. First, we respectively, assume that all the edge weights are triangular fuzzy numbers and trapezoidal fuzzy numbers and prove that the fuzzy ? -minimum spanning tree problem can be transformed to a classical problem on a crisp graph in these two cases, which can be solved by classical algorithms such as the Kruskal algorithm and the Prim algorithm in polynomial time. Subsequently, as for the case that the edge weights are general fuzzy numbers, a fuzzy simulation-based genetic algorithm using Prüfer number representation is designed for solving the fuzzy ? -minimum spanning tree problem. Some numerical examples are also provided for illustrating the effectiveness of the proposed solutions.

  19. Fuzzy control of battery chargers

    SciTech Connect

    Aldridge, J.

    1996-03-01

    The increasing reliance on battery power for portable terrestrial purposes, such as portable tools, portable computers, and telecommunications, provides motivation to optimize the battery charging process with respect to speed of charging and charging cycle lifetime of the battery. Fuzzy control, implemented on a small microcomputer, optimizes charging in the presence of nonlinear effects and large uncertainty in the voltage vs. charge state characteristics for the battery. Use of a small microcontroller makes possible a small, capable, and affordable package for the charger. Microcontroller-based chargers provide improved performance by adjusting both charging voltage and charging current during the entire charging process depending on a current estimate of the state of charge of the battery. The estimate is derived from the zero-current voltage of the battery and the temperature and their rates of change. All of these quantities are uncertain due to the variation in condition between the individual cells in a battery, the rapid and nonlinear dependence of the fundamental electrochemistry on the internal temperature, and the placement of a single temperature sensor within the battery package. While monitoring the individual cell voltages and temperatures would be desirable, cost and complexity considerations preclude the practice. NASA has developed considerable technology in batteries for supplying significant amounts of power for spacecraft and in fuzzy control techniques for the space applications. In this paper, we describe how we are using both technologies to build an optimal charger prototype as a precursor to a commercial version. {copyright} {ital 1996 American Institute of Physics.}

  20. FUZZY SUPERNOVA TEMPLATES. I. CLASSIFICATION

    SciTech Connect

    Rodney, Steven A.; Tonry, John L. E-mail: jt@ifa.hawaii.ed

    2009-12-20

    Modern supernova (SN) surveys are now uncovering stellar explosions at rates that far surpass what the world's spectroscopic resources can handle. In order to make full use of these SN data sets, it is necessary to use analysis methods that depend only on the survey photometry. This paper presents two methods for utilizing a set of SN light-curve templates to classify SN objects. In the first case, we present an updated version of the Bayesian Adaptive Template Matching program (BATM). To address some shortcomings of that strictly Bayesian approach, we introduce a method for Supernova Ontology with Fuzzy Templates (SOFT), which utilizes fuzzy set theory for the definition and combination of SN light-curve models. For well-sampled light curves with a modest signal-to-noise ratio (S/N >10), the SOFT method can correctly separate thermonuclear (Type Ia) SNe from core collapse SNe with >=98% accuracy. In addition, the SOFT method has the potential to classify SNe into sub-types, providing photometric identification of very rare or peculiar explosions. The accuracy and precision of the SOFT method are verified using Monte Carlo simulations as well as real SN light curves from the Sloan Digital Sky Survey and the SuperNova Legacy Survey. In a subsequent paper, the SOFT method is extended to address the problem of parameter estimation, providing estimates of redshift, distance, and host galaxy extinction without any spectroscopy.

  1. Improving land resource evaluation using fuzzy neural network ensembles

    USGS Publications Warehouse

    XUE, Y.-J.; HU, Y.-M.; Liu, S.-G.; YANG, J.-F.; CHEN, Q.-C.; BAO, S.-T.

    2007-01-01

    Land evaluation factors often contain continuous-, discrete- and nominal-valued attributes. In traditional land evaluation, these different attributes are usually graded into categorical indexes by land resource experts, and the evaluation results rely heavily on experts' experiences. In order to overcome the shortcoming, we presented a fuzzy neural network ensemble method that did not require grading the evaluation factors into categorical indexes and could evaluate land resources by using the three kinds of attribute values directly. A fuzzy back propagation neural network (BPNN), a fuzzy radial basis function neural network (RBFNN), a fuzzy BPNN ensemble, and a fuzzy RBFNN ensemble were used to evaluate the land resources in Guangdong Province. The evaluation results by using the fuzzy BPNN ensemble and the fuzzy RBFNN ensemble were much better than those by using the single fuzzy BPNN and the single fuzzy RBFNN, and the error rate of the single fuzzy RBFNN or fuzzy RBFNN ensemble was lower than that of the single fuzzy BPNN or fuzzy BPNN ensemble, respectively. By using the fuzzy neural network ensembles, the validity of land resource evaluation was improved and reliance on land evaluators' experiences was considerably reduced. ?? 2007 Soil Science Society of China.

  2. Fuzzy C e -I(ec, eo) and Fuzzy Completely C e -I(rc, eo) Functions via Fuzzy e-Open Sets.

    PubMed

    Seenivasan, V; Kamala, K

    2016-01-01

    We introduced the notions of fuzzy C e -I(ec, eo) functions and fuzzy completely C e -I(rc, eo) functions via fuzzy e-open sets. Some properties and several characterization of these types of functions are investigated. PMID:27051858

  3. Fuzzy Ce-I(ec, eo) and Fuzzy Completely Ce-I(rc, eo) Functions via Fuzzy e-Open Sets

    PubMed Central

    Kamala, K.

    2016-01-01

    We introduced the notions of fuzzy Ce-I(ec, eo) functions and fuzzy completely Ce-I(rc, eo) functions via fuzzy e-open sets. Some properties and several characterization of these types of functions are investigated. PMID:27051858

  4. Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance.

    PubMed

    Park, Jong-Wook; Kwak, Hwan-Joo; Kang, Young-Chang; Kim, Dong W

    2016-01-01

    An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller--advanced fuzzy potential field method (AFPFM)--that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot. PMID:27123001

  5. Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance

    PubMed Central

    Park, Jong-Wook; Kwak, Hwan-Joo; Kang, Young-Chang; Kim, Dong W.

    2016-01-01

    An advanced fuzzy potential field method for mobile robot obstacle avoidance is proposed. The potential field method primarily deals with the repulsive forces surrounding obstacles, while fuzzy control logic focuses on fuzzy rules that handle linguistic variables and describe the knowledge of experts. The design of a fuzzy controller—advanced fuzzy potential field method (AFPFM)—that models and enhances the conventional potential field method is proposed and discussed. This study also examines the rule-explosion problem of conventional fuzzy logic and assesses the performance of our proposed AFPFM through simulations carried out using a mobile robot. PMID:27123001

  6. Fuzzy Q-Learning for Generalization of Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1996-01-01

    Fuzzy Q-Learning, introduced earlier by the author, is an extension of Q-Learning into fuzzy environments. GARIC is a methodology for fuzzy reinforcement learning. In this paper, we introduce GARIC-Q, a new method for doing incremental Dynamic Programming using a society of intelligent agents which are controlled at the top level by Fuzzy Q-Learning and at the local level, each agent learns and operates based on GARIC. GARIC-Q improves the speed and applicability of Fuzzy Q-Learning through generalization of input space by using fuzzy rules and bridges the gap between Q-Learning and rule based intelligent systems.

  7. Fuzzy controller design by parallel genetic algorithms

    NASA Astrophysics Data System (ADS)

    Mondelli, G.; Castellano, G.; Attolico, Giovanni; Distante, Arcangelo

    1998-03-01

    Designing a fuzzy system involves defining membership functions and constructing rules. Carrying out these two steps manually often results in a poorly performing system. Genetic Algorithms (GAs) has proved to be a useful tool for designing optimal fuzzy controller. In order to increase the efficiency and effectiveness of their application, parallel GAs (PAGs), evolving synchronously several populations with different balances between exploration and exploitation, have been implemented using a SIMD machine (APE100/Quadrics). The parameters to be identified are coded in such a way that the algorithm implicitly provides a compact fuzzy controller, by finding only necessary rules and removing useless inputs from them. Early results, working on a fuzzy controller implementing the wall-following task for a real vehicle as a test case, provided better fitness values in less generations with respect to previous experiments made using a sequential implementation of GAs.

  8. Fuzzy logic controllers: From development to deployment

    SciTech Connect

    Bonissone, P.P.; Chiang, K.H.

    1994-12-31

    We view fuzzy logic control technology as a high level language in which we can efficiently define and synthesize non-linear controllers for a given process. We contrast fuzzy Proportional Integral (PI) controllers with conventional PI and two dimensional sliding mode controllers. Then we compare the development of Fuzzy Logic Controllers (FLC) with that of Knowledge Based System (KBS) applications. We decompose the comparison into reasoning tasks (representation, inference, and control) and application tasks (acquisition, development, validation, compilation, and deployment). After reviewing the reasoning tasks, we focus on the compilation of fuzzy rule bases into fast access lookup tables. These tables can be used by a simplified run-time engine to determine the TLC`s crisp output for a given input.

  9. Fuzzy expert system shell for scheduling

    NASA Astrophysics Data System (ADS)

    Turksen, I. B.; Yurtsever, Tanju; Demirli, K.

    1993-12-01

    The Fuzzy Logic Expert System Scheduler (FLES) is a unique, on-line, interactive shop floor scheduler that is designed to produce detailed, realistic schedules for day-to-day production management. The user can exercise the control of FLES to produce scheduling assignments over short or long term scheduling horizons or to simulate different plant capacity conditions to analyze their effect on future work plans. The unique and proprietary feature of FLES is its `Decision Engine', a fuzzy knowledge base system that models the reasoning process of a human expert is used to give job releasing and job dispatching decisions. Expert knowledge in terms of fuzzy production rules represented by the use of linguistic variables. The values of these linguistic variables are defined by context dependent fuzzy sets whose meanings are specified by graded membership functions.

  10. Software Packages to Deal with Fuzzy Systems

    NASA Astrophysics Data System (ADS)

    Zahariev, Z.

    2007-10-01

    This paper investigates currently available software packages dealing with fuzzy inference systems (FIS). Fifteen packages are investigated and are described here. Some comparisons are created. At the end there are some conclusions.

  11. Multilayer perceptron, fuzzy sets, and classification

    NASA Technical Reports Server (NTRS)

    Pal, Sankar K.; Mitra, Sushmita

    1992-01-01

    A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

  12. Microturbine control based on fuzzy neural network

    NASA Astrophysics Data System (ADS)

    Yan, Shijie; Bian, Chunyuan; Wang, Zhiqiang

    2006-11-01

    As microturbine generator (MTG) is a clean, efficient, low cost and reliable energy supply system. From outside characteristics of MTG, it is multi-variable, time-varying and coupling system, so it is difficult to be identified on-line and conventional control law adopted before cannot achieve desirable result. A novel fuzzy-neural networks (FNN) control algorithm was proposed in combining with the conventional PID control. In the paper, IF-THEN rules for tuning were applied by a first-order Sugeno fuzzy model with seven fuzzy rules and the membership function was given as the continuous GAUSSIAN function. Some sample data were utilized to train FNN. Through adjusting shape of membership function and weight continually, objective of auto-tuning fuzzy-rules can be achieved. The FNN algorithm had been applied to "100kW Microturbine control and power converter system". The results of simulation and experiment are shown that the algorithm can work very well.

  13. Fuzzy modal analysis: Prediction of experimental behaviours

    NASA Astrophysics Data System (ADS)

    Massa, F.; Tison, T.; Lallemand, B.

    2009-04-01

    The objective of this paper is to numerically predict the modal behaviours of a two-plate steel structure defined with variable parameters and to validate this prediction experimentally. First, the test structure, in which geometrical and material variability has been identified, is studied using a Fuzzy Finite Element Method. This method, named PAEM, allows the fuzzy numerical eigenfrequencies and eigenvectors to be calculated. Second, the test structure is analyzed experimentally to quantify the possible variation of the eigensolutions' modal behaviours and to build the experimental fuzzy sets. Finally, the fuzzy numerical quantities are compared with the experimental quantities to highlight the efficiency of our non-deterministic model for predicting the behavioural modifications of the test structure.

  14. Fuzzy finite element analysis of smart structures

    NASA Astrophysics Data System (ADS)

    Akpan, Unyime O.; Koko, Tamunoiyala S.; Orisamolu, Irewole R.; Gallant, B. Keith

    2000-06-01

    A fuzzy finite element based approach is developed for modelling smart structures with vague or imprecise uncertainties. Fuzzy sets are used to represent the uncertainties present in the piezoelectric, mechanical, thermal, and physical properties of the smart structure. In order to facilitate efficient computation, a sensitivity analysis procedure is used to streamline the number of input fuzzy variables, and the vertex fuzzy analysis technique is then used to compute the possibility distributions of the responses of the smart structural system. The methodology has been developed within the framework of the SMARTCOM computational tool for the design/analysis of smart composite structures. The methodology developed is found to be accurate and computationally efficient for solution of practical problems.

  15. Fuzzy logic and neural network technologies

    NASA Technical Reports Server (NTRS)

    Villarreal, James A.; Lea, Robert N.; Savely, Robert T.

    1992-01-01

    Applications of fuzzy logic technologies in NASA projects are reviewed to examine their advantages in the development of neural networks for aerospace and commercial expert systems and control. Examples of fuzzy-logic applications include a 6-DOF spacecraft controller, collision-avoidance systems, and reinforcement-learning techniques. The commercial applications examined include a fuzzy autofocusing system, an air conditioning system, and an automobile transmission application. The practical use of fuzzy logic is set in the theoretical context of artificial neural systems (ANSs) to give the background for an overview of ANS research programs at NASA. The research and application programs include the Network Execution and Training Simulator and faster training algorithms such as the Difference Optimized Training Scheme. The networks are well suited for pattern-recognition applications such as predicting sunspots, controlling posture maintenance, and conducting adaptive diagnoses.

  16. Competitive Facility Location with Fuzzy Random Demands

    NASA Astrophysics Data System (ADS)

    Uno, Takeshi; Katagiri, Hideki; Kato, Kosuke

    2010-10-01

    This paper proposes a new location problem of competitive facilities, e.g. shops, with uncertainty and vagueness including demands for the facilities in a plane. By representing the demands for facilities as fuzzy random variables, the location problem can be formulated as a fuzzy random programming problem. For solving the fuzzy random programming problem, first the α-level sets for fuzzy numbers are used for transforming it to a stochastic programming problem, and secondly, by using their expectations and variances, it can be reformulated to a deterministic programming problem. After showing that one of their optimal solutions can be found by solving 0-1 programming problems, their solution method is proposed by improving the tabu search algorithm with strategic oscillation. The efficiency of the proposed method is shown by applying it to numerical examples of the facility location problems.

  17. Fuzzy controllers in nuclear material accounting

    SciTech Connect

    Zardecki, A.

    1994-10-01

    Fuzzy controllers are applied to predicting and modeling a time series, with particular emphasis on anomaly detection in nuclear material inventory differences. As compared to neural networks, the fuzzy controllers can operate in real time; their learning process does not require many iterations to converge. For this reason fuzzy controllers are potentially useful in time series forecasting, where the authors want to detect and identify trends in real time. They describe an object-oriented implementation of the algorithm advanced by Wang and Mendel. Numerical results are presented both for inventory data and time series corresponding to chaotic situations, such as encountered in the context of strange attractors. In the latter case, the effects of noise on the predictive power of the fuzzy controller are explored.

  18. On Fuzzy Sets: Reply to Cerny.

    ERIC Educational Resources Information Center

    Robertson, Stephen E.

    1979-01-01

    Responds to Barbara A. Cerny's reaction to Robinson's article on the role of fuzzy set theory in information science, addressing Cerny's points about probability theory and statistical uncertainty. (FM)

  19. Refining Linear Fuzzy Rules by Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap S.; Malkani, Anil

    1996-01-01

    Linear fuzzy rules are increasingly being used in the development of fuzzy logic systems. Radial basis functions have also been used in the antecedents of the rules for clustering in product space which can automatically generate a set of linear fuzzy rules from an input/output data set. Manual methods are usually used in refining these rules. This paper presents a method for refining the parameters of these rules using reinforcement learning which can be applied in domains where supervised input-output data is not available and reinforcements are received only after a long sequence of actions. This is shown for a generalization of radial basis functions. The formation of fuzzy rules from data and their automatic refinement is an important step in closing the gap between the application of reinforcement learning methods in the domains where only some limited input-output data is available.

  20. Single board system for fuzzy inference

    NASA Technical Reports Server (NTRS)

    Symon, James R.; Watanabe, Hiroyuki

    1991-01-01

    The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.

  1. Japanese advances in fuzzy systems research

    NASA Astrophysics Data System (ADS)

    Schwartz, Daniel G.

    1992-07-01

    During this past summer (1991), I spent two months on an appointment as visiting researcher at Kansai University, Osaka, Japan, and five weeks at the Laboratory for International Fuzzy Engineering Research (LIFE), in Yokohama. Part of the expenses for the time in Osaka, and all the expenses for the visit at LIFE, were covered by ONR. While there I met with most of the key researchers in both fuzzy systems and case-based reasoning. This involved trips to numerous universities and research laboratories at Matsushita/Panasonic, Omron, and Hitachi Corporations. In addition, I spent three days at the Fuzzy Logic Systems Institute (FLSI), Iizuka, and I attended the annual meeting of the Japan Society for Fuzzy Theory and Research (SOFT-91) in Nagoya. The following report elaborates what I learned as a result of those activities.

  2. Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology

    NASA Astrophysics Data System (ADS)

    Bonissone, Piero P.

    1995-06-01

    We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.

  3. Aggregation operations for multiaspect fuzzy soft sets

    NASA Astrophysics Data System (ADS)

    Sulaiman, Nor Hashimah; Mohamad, Daud

    2015-10-01

    Multiaspect fuzzy soft set (MAFSS) is one of the generalized forms of fuzzy soft sets. In this paper, we introduce two types of aggregation operations for MAFSSs, namely the weighted arithmetic mean (WAM)-based MAFSS aggregation, and the ordered weighted aggregation (OWA)-based MAFSS aggregation. The applicability of the two MAFSS-aggregation operations is illustrated with numerical examples in group decision making.

  4. A Fuzzy Aproach For Facial Emotion Recognition

    NASA Astrophysics Data System (ADS)

    Gîlcă, Gheorghe; Bîzdoacă, Nicu-George

    2015-09-01

    This article deals with an emotion recognition system based on the fuzzy sets. Human faces are detected in images with the Viola - Jones algorithm and for its tracking in video sequences we used the Camshift algorithm. The detected human faces are transferred to the decisional fuzzy system, which is based on the variable fuzzyfication measurements of the face: eyebrow, eyelid and mouth. The system can easily determine the emotional state of a person.

  5. Application of Fuzzy Logic to Matrix FMECA

    NASA Astrophysics Data System (ADS)

    Shankar, N. Ravi; Prabhu, B. S.

    2001-04-01

    A methodology combining the benefits of Fuzzy Logic and Matrix FMEA is presented in this paper. The presented methodology extends the risk prioritization beyond the conventional Risk Priority Number (RPN) method. Fuzzy logic is used to calculate the criticality rank. Also the matrix approach is improved further to develop a pictorial representation retaining all relevant qualitative and quantitative information of several FMEA elements relationships. The methodology presented is demonstrated by application to an illustrative example.

  6. Neuro-fuzzy models in pattern recognition

    NASA Astrophysics Data System (ADS)

    Mitra, Sunanda; Kim, Yong Soo

    1993-12-01

    Research in the last decade emphasized the potential of designing adaptive pattern recognition classifiers based on algorithms using multi-layered artificial neural nets. The greatest potential in such endeavors was anticipated to be not only in the adaptivity but also in the high-speed processing through massively parallel VLSI implementation and optical computing. Computational advantages of such algorithms have been demonstrated in a number of papers. Neural networks particularly the self-organizing types have been found quite suitable crisp pattern for clustering of unlabeled datasets. The generalization of Kohonen-type learning vector quantization (LVQ) clustering algorithm to fuzzy LVQ clustering algorithm and its equivalence to fuzzy c-means has been clearly demonstrated recently. On the other hand, Carpenter/Grossberg's ART-type self organizing neural networks have been modified to perform fuzzy clustering by a number of researches in the past few years. The performance of such neuro-fuzzy models in clustering unlabeled data patterns is addressed in this paper. A recent development of a new similarity measure and a new learning rule for updating the centroid of the winning cluster in a fuzzy ART-type neural network is also described. The capability of the above neuro-fuzzy model in better partitioning of datasets into clusters of any shape is demonstrated.

  7. Adaptive fuzzy system for 3-D vision

    NASA Technical Reports Server (NTRS)

    Mitra, Sunanda

    1993-01-01

    An adaptive fuzzy system using the concept of the Adaptive Resonance Theory (ART) type neural network architecture and incorporating fuzzy c-means (FCM) system equations for reclassification of cluster centers was developed. The Adaptive Fuzzy Leader Clustering (AFLC) architecture is a hybrid neural-fuzzy system which learns on-line in a stable and efficient manner. The system uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) network to identify the cluster centers initially. The initial classification of an input takes place in a two stage process; a simple competitive stage and a distance metric comparison stage. The cluster prototypes are then incrementally updated by relocating the centroid positions from Fuzzy c-Means (FCM) system equations for the centroids and the membership values. The operational characteristics of AFLC and the critical parameters involved in its operation are discussed. The performance of the AFLC algorithm is presented through application of the algorithm to the Anderson Iris data, and laser-luminescent fingerprint image data. The AFLC algorithm successfully classifies features extracted from real data, discrete or continuous, indicating the potential strength of this new clustering algorithm in analyzing complex data sets. The hybrid neuro-fuzzy AFLC algorithm will enhance analysis of a number of difficult recognition and control problems involved with Tethered Satellite Systems and on-orbit space shuttle attitude controller.

  8. Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method.

    PubMed

    Alguliyev, Rasim M; Aliguliyev, Ramiz M; Mahmudova, Rasmiyya S

    2015-01-01

    Personnel evaluation is an important process in human resource management. The multicriteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. In this study, a fuzzy hybrid multicriteria decision-making (MCDM) model is proposed to personnel evaluation. This model solves personnel evaluation problem in a fuzzy environment where both criteria and weights could be fuzzy sets. The triangular fuzzy numbers are used to evaluate the suitability of personnel and the approximate reasoning of linguistic values. For evaluation, we have selected five information culture criteria. The weights of the criteria were calculated using worst-case method. After that, modified fuzzy VIKOR is proposed to rank the alternatives. The outcome of this research is ranking and selecting best alternative with the help of fuzzy VIKOR and modified fuzzy VIKOR techniques. A comparative analysis of results by fuzzy VIKOR and modified fuzzy VIKOR methods is presented. Experiments showed that the proposed modified fuzzy VIKOR method has some advantages over fuzzy VIKOR method. Firstly, from a computational complexity point of view, the presented model is effective. Secondly, compared to fuzzy VIKOR method, it has high acceptable advantage compared to fuzzy VIKOR method. PMID:26516634

  9. Multicriteria Personnel Selection by the Modified Fuzzy VIKOR Method

    PubMed Central

    Alguliyev, Rasim M.; Aliguliyev, Ramiz M.; Mahmudova, Rasmiyya S.

    2015-01-01

    Personnel evaluation is an important process in human resource management. The multicriteria nature and the presence of both qualitative and quantitative factors make it considerably more complex. In this study, a fuzzy hybrid multicriteria decision-making (MCDM) model is proposed to personnel evaluation. This model solves personnel evaluation problem in a fuzzy environment where both criteria and weights could be fuzzy sets. The triangular fuzzy numbers are used to evaluate the suitability of personnel and the approximate reasoning of linguistic values. For evaluation, we have selected five information culture criteria. The weights of the criteria were calculated using worst-case method. After that, modified fuzzy VIKOR is proposed to rank the alternatives. The outcome of this research is ranking and selecting best alternative with the help of fuzzy VIKOR and modified fuzzy VIKOR techniques. A comparative analysis of results by fuzzy VIKOR and modified fuzzy VIKOR methods is presented. Experiments showed that the proposed modified fuzzy VIKOR method has some advantages over fuzzy VIKOR method. Firstly, from a computational complexity point of view, the presented model is effective. Secondly, compared to fuzzy VIKOR method, it has high acceptable advantage compared to fuzzy VIKOR method. PMID:26516634

  10. Directed Laplacians For Fuzzy Autocatalytic Set Of Fuzzy Graph Type-3 Of An Incineration Process

    NASA Astrophysics Data System (ADS)

    Ahmad, Tahir; Baharun, Sabariah; Bakar, Sumarni Abu

    2010-11-01

    Fuzzy Autocatalytic Set (FACS) of Fuzzy Graph Type-3 was used in the modeling of a clinical waste incineration process in Malacca. FACS provided more accurate explanations of the incineration process than using crisp graph. In this paper we explore further FACS. Directed and combinatorial Laplacian of FACS are developed and their basic properties are presented.

  11. Twenty-Five Years of the Fuzzy Factor: Fuzzy Logic, the Courts, and Student Press Law.

    ERIC Educational Resources Information Center

    Plopper, Bruce L.; McCool, Lauralee

    A study applied the structure of fuzzy logic, a fairly modern development in mathematical set theory, to judicial opinions concerning non-university, public school student publications, from 1975 to 1999. The study examined case outcomes (19 cases generated 27 opinions) as a function of fuzzy logic, and it evaluated interactions between fuzzy…

  12. Fuzzy automata and pattern matching

    NASA Technical Reports Server (NTRS)

    Setzer, C. B.; Warsi, N. A.

    1986-01-01

    A wide-ranging search for articles and books concerned with fuzzy automata and syntactic pattern recognition is presented. A number of survey articles on image processing and feature detection were included. Hough's algorithm is presented to illustrate the way in which knowledge about an image can be used to interpret the details of the image. It was found that in hand generated pictures, the algorithm worked well on following the straight lines, but had great difficulty turning corners. An algorithm was developed which produces a minimal finite automaton recognizing a given finite set of strings. One difficulty of the construction is that, in some cases, this minimal automaton is not unique for a given set of strings and a given maximum length. This algorithm compares favorably with other inference algorithms. More importantly, the algorithm produces an automaton with a rigorously described relationship to the original set of strings that does not depend on the algorithm itself.

  13. Robust Fuzzy Controllers Using FPGAs

    NASA Technical Reports Server (NTRS)

    Monroe, Author Gene S., Jr.

    2007-01-01

    Electro-mechanical device controllers typically come in one of three forms, proportional (P), Proportional Derivative (PD), and Proportional Integral Derivative (PID). Two methods of control are discussed in this paper; they are (1) the classical technique that requires an in-depth mathematical use of poles and zeros, and (2) the fuzzy logic (FL) technique that is similar to the way humans think and make decisions. FL controllers are used in multiple industries; examples include control engineering, computer vision, pattern recognition, statistics, and data analysis. Presented is a study on the development of a PD motor controller written in very high speed hardware description language (VHDL), and implemented in FL. Four distinct abstractions compose the FL controller, they are the fuzzifier, the rule-base, the fuzzy inference system (FIS), and the defuzzifier. FL is similar to, but different from, Boolean logic; where the output value may be equal to 0 or 1, but it could also be equal to any decimal value between them. This controller is unique because of its VHDL implementation, which uses integer mathematics. To compensate for VHDL's inability to synthesis floating point numbers, a scale factor equal to 10(sup (N/4) is utilized; where N is equal to data word size. The scaling factor shifts the decimal digits to the left of the decimal point for increased precision. PD controllers are ideal for use with servo motors, where position control is effective. This paper discusses control methods for motion-base platforms where a constant velocity equivalent to a spectral resolution of 0.25 cm(exp -1) is required; however, the control capability of this controller extends to various other platforms.

  14. Comparison of fuzzy AHP and fuzzy TODIM methods for landfill location selection.

    PubMed

    Hanine, Mohamed; Boutkhoum, Omar; Tikniouine, Abdessadek; Agouti, Tarik

    2016-01-01

    Landfill location selection is a multi-criteria decision problem and has a strategic importance for many regions. The conventional methods for landfill location selection are insufficient in dealing with the vague or imprecise nature of linguistic assessment. To resolve this problem, fuzzy multi-criteria decision-making methods are proposed. The aim of this paper is to use fuzzy TODIM (the acronym for Interactive and Multi-criteria Decision Making in Portuguese) and the fuzzy analytic hierarchy process (AHP) methods for the selection of landfill location. The proposed methods have been applied to a landfill location selection problem in the region of Casablanca, Morocco. After determining the criteria affecting the landfill location decisions, fuzzy TODIM and fuzzy AHP methods are applied to the problem and results are presented. The comparisons of these two methods are also discussed. PMID:27186465

  15. Online tuning of fuzzy inference systems using dynamic fuzzy Q-learning.

    PubMed

    Er, Meng Joo; Deng, Chang

    2004-06-01

    This paper presents a dynamic fuzzy Q-learning (DFQL) method that is capable of tuning fuzzy inference systems (FIS) online. A novel online self-organizing learning algorithm is developed so that structure and parameters identification are accomplished automatically and simultaneously based only on Q-learning. Self-organizing fuzzy inference is introduced to calculate actions and Q-functions so as to enable us to deal with continuous-valued states and actions. Fuzzy rules provide a natural mean of incorporating the bias components for rapid reinforcement learning. Experimental results and comparative studies with the fuzzy Q-learning (FQL) and continuous-action Q-learning in the wall-following task of mobile robots demonstrate that the proposed DFQL method is superior. PMID:15484918

  16. Fuzzy stochastic elements method. Spectral approach

    NASA Astrophysics Data System (ADS)

    Sniady, Pawel; Mazur-Sniady, Krystyna; Sieniawska, Roza; Zukowski, Stanislaw

    2013-05-01

    We study a complex dynamic problem, which concerns a structure with uncertain parameters subjected to a stochastic excitation. Formulation of such a problem introduces fuzzy random variables for parameters of the structure and fuzzy stochastic processes for the load process. The uncertainty has two sources, namely the randomness of structural parameters such as geometry characteristics, material and damping properties, load process and imprecision of the theoretical model and incomplete information or uncertain data. All of these have a great influence on the response of the structure. By analyzing such problems we describe the random variability using the probability theory and the imprecision by use of fuzzy sets. Due to the fact that it is difficult to find an analytic expression for the inversion of the stochastic operator in the stochastic differential equation, a number of approximate methods have been proposed in the literature which can be connected to the finite element method. To evaluate the effects of excitation in the frequency domain we use the spectral density function. The spectral analysis is widely used in stochastic dynamics field of linear systems for stationary random excitation. The concept of the evolutionary spectral density is used in the case of non-stationary random excitation. We solve the considered problem using fuzzy stochastic finite element method. The solution is based on the idea of a fuzzy random frequency response vector for stationary input excitation and a transient fuzzy random frequency response vector for the fuzzy non-stationary one. We use the fuzzy random frequency response vector and the transient fuzzy random frequency response vector in the context of spectral analysis in order to determine the influence of structural uncertainty on the fuzzy random response of the structure. We study a linear system with random parameters subjected to two particular cases of stochastic excitation in a frequency domain. The first one

  17. Fuzzy model-based observers for fault detection in CSTR.

    PubMed

    Ballesteros-Moncada, Hazael; Herrera-López, Enrique J; Anzurez-Marín, Juan

    2015-11-01

    Under the vast variety of fuzzy model-based observers reported in the literature, what would be the properone to be used for fault detection in a class of chemical reactor? In this study four fuzzy model-based observers for sensor fault detection of a Continuous Stirred Tank Reactor were designed and compared. The designs include (i) a Luenberger fuzzy observer, (ii) a Luenberger fuzzy observer with sliding modes, (iii) a Walcott-Zak fuzzy observer, and (iv) an Utkin fuzzy observer. A negative, an oscillating fault signal, and a bounded random noise signal with a maximum value of ±0.4 were used to evaluate and compare the performance of the fuzzy observers. The Utkin fuzzy observer showed the best performance under the tested conditions. PMID:26521723

  18. Research on bounded rationality of fuzzy choice functions.

    PubMed

    Wu, Xinlin; Zhao, Yong

    2014-01-01

    The rationality of a fuzzy choice function is a hot research topic in the study of fuzzy choice functions. In this paper, two common fuzzy sets are studied and analyzed in the framework of the Banerjee choice function. The complete rationality and bounded rationality of fuzzy choice functions are defined based on the two fuzzy sets. An assumption is presented to study the fuzzy choice function, and especially the fuzzy choice function with bounded rationality is studied combined with some rationality conditions. Results show that the fuzzy choice function with bounded rationality also satisfies some important rationality conditions, but not vice versa. The research gives supplements to the investigation in the framework of the Banerjee choice function. PMID:24782677

  19. Comparison between the performance of two classes of fuzzy controllers

    NASA Technical Reports Server (NTRS)

    Janabi, T. H.; Sultan, L. H.

    1992-01-01

    This paper presents an application comparison between two classes of fuzzy controllers: the Clearness Transformation Fuzzy Controller (CTFC) and the CRI-based Fuzzy Controller. The comparison is performed by studying the application of the controllers to simulation examples of nonlinear systems. The CTFC is a new approach for the organization of fuzzy controllers based on a cognitive model of parameter driven control, the notion of fuzzy patterns to represent fuzzy knowledge and the Clearness Transformation Rule of Inference (CTRI) for approximate reasoning. The approach facilitates the implementation of the basic modules of the controller: the fuzzifier, defuzzifier, and the control protocol in a rule-based architecture. The CTRI scheme for approximate reasoning does not require the formation of fuzzy relation matrices yielding improved performance in comparison with the traditional organization of fuzzy controllers.

  20. Encoding spatial images: A fuzzy set theory approach

    NASA Technical Reports Server (NTRS)

    Sztandera, Leszek M.

    1992-01-01

    As the use of fuzzy set theory continues to grow, there is an increased need for methodologies and formalisms to manipulate obtained fuzzy subsets. Concepts involving relative position of fuzzy patterns are acknowledged as being of high importance in many areas. In this paper, we present an approach based on the concept of dominance in fuzzy set theory for modelling relative positions among fuzzy subsets of a plane. In particular, we define the following spatial relations: to the left (right), in front of, behind, above, below, near, far from, and touching. This concept has been implemented to define spatial relationships among fuzzy subsets of the image plane. Spatial relationships based on fuzzy set theory, coupled with a fuzzy segmentation, should therefore yield realistic results in scene understanding.

  1. Evolving fuzzy rules in a learning classifier system

    NASA Technical Reports Server (NTRS)

    Valenzuela-Rendon, Manuel

    1993-01-01

    The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.

  2. Fuzzy Versions of Epistemic and Deontic Logic

    NASA Technical Reports Server (NTRS)

    Gounder, Ramasamy S.; Esterline, Albert C.

    1998-01-01

    Epistemic and deontic logics are modal logics, respectively, of knowledge and of the normative concepts of obligation, permission, and prohibition. Epistemic logic is useful in formalizing systems of communicating processes and knowledge and belief in AI (Artificial Intelligence). Deontic logic is useful in computer science wherever we must distinguish between actual and ideal behavior, as in fault tolerance and database integrity constraints. We here discuss fuzzy versions of these logics. In the crisp versions, various axioms correspond to various properties of the structures used in defining the semantics of the logics. Thus, any axiomatic theory will be characterized not only by its axioms but also by the set of properties holding of the corresponding semantic structures. Fuzzy logic does not proceed with axiomatic systems, but fuzzy versions of the semantic properties exist and can be shown to correspond to some of the axioms for the crisp systems in special ways that support dependency networks among assertions in a modal domain. This in turn allows one to implement truth maintenance systems. For the technical development of epistemic logic, and for that of deontic logic. To our knowledge, we are the first to address fuzzy epistemic and fuzzy deontic logic explicitly and to consider the different systems and semantic properties available. We give the syntax and semantics of epistemic logic and discuss the correspondence between axioms of epistemic logic and properties of semantic structures. The same topics are covered for deontic logic. Fuzzy epistemic and fuzzy deontic logic discusses the relationship between axioms and semantic properties for these logics. Our results can be exploited in truth maintenance systems.

  3. Implication-based fuzzy semiautomaton of a finite group and its properties

    NASA Astrophysics Data System (ADS)

    Selva Rathi, M.; Michael Anna Spinneli, J.

    2016-06-01

    Implication-based fuzzy semiautomaton (IBFSA) of a finite group is defined. The ideas of an implication-based fuzzy kernel and implication-based fuzzy subsemiautomaton of an IBFSA over a finite group are developed using the concept of implication-based fuzzy subgroup and implication-based fuzzy normal subgroup. The necessary and sufficient condition for the implication-based fuzzy kernel and implication-based fuzzy subsemiautomaton of an IBFSA and few other results are proved in this paper.

  4. Terminology and concepts of control and Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Aldridge, Jack; Lea, Robert; Jani, Yashvant; Weiss, Jonathan

    1990-01-01

    Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.

  5. The Impact of Fuzzy Logic on Student Press Law.

    ERIC Educational Resources Information Center

    McCool, Lauralee; Plopper, Bruce L.

    2001-01-01

    Uses the relatively new science of fuzzy logic to review lower court and appellate court decisions from the last four decades regarding free expression in student publications. Finds pronounced effects, showing that fuzzy sets inherently favor administrators, while students show a strikingly high win/loss ratio when courts avoid fuzzy logic. (SR)

  6. Type-II Fuzzy Decision Support System for Fertilizer

    PubMed Central

    Ashraf, Ather; Sarwar, Mansoor

    2014-01-01

    Type-II fuzzy sets are used to convey the uncertainties in the membership function of type-I fuzzy sets. Linguistic information in expert rules does not give any information about the geometry of the membership functions. These membership functions are mostly constructed through numerical data or range of classes. But there exists an uncertainty about the shape of the membership, that is, whether to go for a triangle membership function or a trapezoidal membership function. In this paper we use a type-II fuzzy set to overcome this uncertainty, and develop a fuzzy decision support system of fertilizers based on a type-II fuzzy set. This type-II fuzzy system takes cropping time and soil nutrients in the form of spatial surfaces as input, fuzzifies it using a type-II fuzzy membership function, and implies fuzzy rules on it in the fuzzy inference engine. The output of the fuzzy inference engine, which is in the form of interval value type-II fuzzy sets, reduced to an interval type-I fuzzy set, defuzzifies it to a crisp value and generates a spatial surface of fertilizers. This spatial surface shows the spatial trend of the required amount of fertilizer needed to cultivate a specific crop. The complexity of our algorithm is O(mnr), where m is the height of the raster, n is the width of the raster, and r is the number of expert rules. PMID:24892071

  7. Evaluation of Soil Quality: Application of Fuzzy Indicators

    Technology Transfer Automated Retrieval System (TEKTRAN)

    The problem of assessing soil quality is considered as the fuzzy modeling task. Fuzzy indicator concept (FIC) is used as a general platform for the assessment of soil quality as a "degree or grade of perfection”. The FIC can be realized through the utilization of fuzzy soil quality indicators (FSQI)...

  8. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation.

    PubMed

    Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

    2015-01-01

    Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment. PMID:26368541

  9. Medical application of fuzzy logic: fuzzy patient state in arterial hypertension analysis

    NASA Astrophysics Data System (ADS)

    Blinowska, Aleksandra; Duckstein, Lucien

    1993-12-01

    A few existing applications of fuzzy logic in medicine are briefly described and some potential applications are reviewed. The problem of classification of patient states and medical decision making is discussed more in detail and illustrated by the example of a fuzzy rule based model developed to elicit, analyze and reproduce the opinions of multiple medical experts in the case of arterial hypertension. The goal was to reproduce the average coded answers using an adequate fuzzy procedure, here a fuzzy rule. State categories and the initial set of experimental parameters were defined according to medical practice. The fuzzy set membership functions were then assessed for each parameter in each category and a small subset of representative and pertinent parameters selected for each question. The data were split into two sets of 50 patient files each, the calibration set and the validation set. Two evaluation criteria were used: the sum of squared deviations and the sum of deviations. Fuzzy rules were then sought that reproduced the target, which was the average coded answer. Only one fuzzy rule `and' appeared to be necessary to describe the patient state in a continuous way and to approach the target as closely as the majority of experts.

  10. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation

    PubMed Central

    Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

    2015-01-01

    Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment. PMID:26368541

  11. m-Polar Fuzzy Sets: An Extension of Bipolar Fuzzy Sets

    PubMed Central

    Chen, Juanjuan; Li, Shenggang; Ma, Shengquan; Wang, Xueping

    2014-01-01

    Recently, bipolar fuzzy sets have been studied and applied a bit enthusiastically and a bit increasingly. In this paper we prove that bipolar fuzzy sets and [0,1]2-sets (which have been deeply studied) are actually cryptomorphic mathematical notions. Since researches or modelings on real world problems often involve multi-agent, multi-attribute, multi-object, multi-index, multi-polar information, uncertainty, or/and limit process, we put forward (or highlight) the notion of m-polar fuzzy set (actually, [0,1]m-set which can be seen as a generalization of bipolar fuzzy set, where m is an arbitrary ordinal number) and illustrate how many concepts have been defined based on bipolar fuzzy sets and many results which are related to these concepts can be generalized to the case of m-polar fuzzy sets. We also give examples to show how to apply m-polar fuzzy sets in real world problems. PMID:25025087

  12. Fuzzy control of a boiler steam drum

    SciTech Connect

    Mayer, K.; Crockett, W.K.

    1995-10-01

    The authors controlled the inlet water flow to a dynamic model of a steam drum using fuzzy logic. The drum level varied little with step inputs in steam flow. The fuzzy logic controller performed at least as well as a well-tuned traditional PI (which is notoriously difficult to tune). Using plant data in the model provided further evidence that fuzzy logic control gave excellent results. The drum level is a function of inlet water, steam production, and blowdown. To compensate for upsets caused by steam production, independent variables used in the fuzzy controller were drum level and change in drum level. The dependent variable was the change required in the inlet flow. By modeling a 175,000 lb/hr Riley-Stoker boiler, they determined the universe of discourse for each of the three variables. Three triangular and two trapezoidal membership functions characterize each of these universes. The knowledge of experts provided the fuzzy associative memory (FAM) for the variables. The authors modeled the complete dynamic system using Tutsim (Tutsim Products, 200 California Ave., Palo Alto, CA 94306).

  13. Decentralized fuzzy control of multiple nonholonomic vehicles

    SciTech Connect

    Driessen, B.J.; Feddema, J.T.; Kwok, K.S.

    1997-09-01

    This work considers the problem of controlling multiple nonholonomic vehicles so that they converge to a scent source without colliding with each other. Since the control is to be implemented on simple 8-bit microcontrollers, fuzzy control rules are used to simplify a linear quadratic regulator control design. The inputs to the fuzzy controllers for each vehicle are the (noisy) direction to the source, the distance to the closest neighbor vehicle, and the direction to the closest vehicle. These directions are discretized into four values: Forward, Behind, Left, and Right, and the distance into three values: Near, Far, Gone. The values of the control at these discrete values are obtained based on the collision-avoidance repulsive forces and the change of variables that reduces the motion control problem of each nonholonomic vehicle to a nonsingular one with two degrees of freedom, instead of three. A fuzzy inference system is used to obtain control values for inputs between the small number of discrete input values. Simulation results are provided which demonstrate that the fuzzy control law performs well compared to the exact controller. In fact, the fuzzy controller demonstrates improved robustness to noise.

  14. Fuzzy logic control of telerobot manipulators

    NASA Technical Reports Server (NTRS)

    Franke, Ernest A.; Nedungadi, Ashok

    1992-01-01

    Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems.

  15. Learning fuzzy information in a hybrid connectionist, symbolic model

    NASA Technical Reports Server (NTRS)

    Romaniuk, Steve G.; Hall, Lawrence O.

    1993-01-01

    An instance-based learning system is presented. SC-net is a fuzzy hybrid connectionist, symbolic learning system. It remembers some examples and makes groups of examples into exemplars. All real-valued attributes are represented as fuzzy sets. The network representation and learning method is described. To illustrate this approach to learning in fuzzy domains, an example of segmenting magnetic resonance images of the brain is discussed. Clearly, the boundaries between human tissues are ill-defined or fuzzy. Example fuzzy rules for recognition are generated. Segmentations are presented that provide results that radiologists find useful.

  16. [Study of fuzzy analytical system for physiological signals during sleep].

    PubMed

    Xu, Xian-tong; Lu, Guang-wen; Ma, Bo

    2003-11-01

    A new approach to sleep analysis based on fuzzy prediction theory is described. This article gives a general introduction to detection and processing of biologic signals with LabVIEW software, and the application of the designed fuzzy measurement system in fuzzy prediction analysis of the physiological signals recorded during sleep. The results of trials of the fuzzy prediction analysis demonstrated the reliability of this method. LabVIEW-based fuzzy prediction analysis can be helpful for early diagnosis, monitoring and prognostic assessment of some diseases, and may be valuable in the analysis of the physiologic signals of patients with obstructive sleep apnea syndrome (OSAS) during sleep. PMID:14625181

  17. A fuzzy logic approach to modeling a vehicle crash test

    NASA Astrophysics Data System (ADS)

    Pawlus, Witold; Karimi, Hamid; Robbersmyr, Kjell

    2013-03-01

    This paper presents an application of fuzzy approach to vehicle crash modeling. A typical vehicle to pole collision is described and kinematics of a car involved in this type of crash event is thoroughly characterized. The basics of fuzzy set theory and modeling principles based on fuzzy logic approach are presented. In particular, exceptional attention is paid to explain the methodology of creation of a fuzzy model of a vehicle collision. Furthermore, the simulation results are presented and compared to the original vehicle's kinematics. It is concluded which factors have influence on the accuracy of the fuzzy model's output and how they can be adjusted to improve the model's fidelity.

  18. Novel Applications of Intuitionistic Fuzzy Digraphs in Decision Support Systems

    PubMed Central

    Sarwar, Mansoor

    2014-01-01

    Many problems of practical interest can be modeled and solved by using graph algorithms. In general, graph theory has a wide range of applications in diverse fields. In this paper, the intuitionistic fuzzy organizational and neural network models, intuitionistic fuzzy neurons in medical diagnosis, intuitionistic fuzzy digraphs in vulnerability assessment of gas pipeline networks, and intuitionistic fuzzy digraphs in travel time are presented as examples of intuitionistic fuzzy digraphs in decision support system. We have also designed and implemented the algorithms for these decision support systems. PMID:25045752

  19. Variable-order fuzzy fractional PID controller.

    PubMed

    Liu, Lu; Pan, Feng; Xue, Dingyu

    2015-03-01

    In this paper, a new tuning method of variable-order fractional fuzzy PID controller (VOFFLC) is proposed for a class of fractional-order and integer-order control plants. Fuzzy logic control (FLC) could easily deal with parameter variations of control system, but the fractional-order parameters are unable to change through this way and it has confined the effectiveness of FLC. Therefore, an attempt is made in this paper to allow all the five parameters of fractional-order PID controller vary along with the transformation of system structure as the outputs of FLC, and the influence of fractional orders λ and μ on control systems has been investigated to make the fuzzy rules for VOFFLC. Four simulation results of different plants are shown to verify the availability of the proposed control strategy. PMID:25440947

  20. Fuzzy efficiency optimization of AC induction motors

    NASA Technical Reports Server (NTRS)

    Jani, Yashvant; Sousa, Gilberto; Turner, Wayne; Spiegel, Ron; Chappell, Jeff

    1993-01-01

    This paper describes the early states of work to implement a fuzzy logic controller to optimize the efficiency of AC induction motor/adjustable speed drive (ASD) systems running at less than optimal speed and torque conditions. In this paper, the process by which the membership functions of the controller were tuned is discussed and a controller which operates on frequency as well as voltage is proposed. The membership functions for this dual-variable controller are sketched. Additional topics include an approach for fuzzy logic to motor current control which can be used with vector-controlled drives. Incorporation of a fuzzy controller as an application-specific integrated circuit (ASIC) microchip is planned.

  1. Diagnosing Parkinson's Diseases Using Fuzzy Neural System

    PubMed Central

    Abiyev, Rahib H.; Abizade, Sanan

    2016-01-01

    This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson's disease. A diagnosing of Parkinson's diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system. PMID:26881009

  2. Engineering application based on fuzzy approach

    NASA Astrophysics Data System (ADS)

    Pislaru, Marius; Avasilcai, Silvia; Trandabat, Alexandru

    2011-12-01

    The article focus on an application of chemical engineering. A fuzzy modeling methodology designed to determinate two relevant characteristics of a chemical compound (ferrocenylsiloxane polyamide) for self-assembling - surface tension and maximum UV absorbance measured as temperature and concentration functions. One of the most important parts of a fuzzy rule-based inference system for the polyamide solution characteristics determinations is that it allows to interpret the knowledge contained in the model and also to improve it with a-priori knowledge. The results obtained through proposed method are highly accurate and its can be optimized by utilizing the available information during the modeling process. The results showed that it is feasible in theory and reliable on calculation applying Mamdani fuzzy inference system to the estimation of optical and surface properties of a polyamide solution.

  3. Engineering application based on fuzzy approach

    NASA Astrophysics Data System (ADS)

    Pislaru, Marius; Avasilcai, Silvia; Trandabat, Alexandru

    2012-01-01

    The article focus on an application of chemical engineering. A fuzzy modeling methodology designed to determinate two relevant characteristics of a chemical compound (ferrocenylsiloxane polyamide) for self-assembling - surface tension and maximum UV absorbance measured as temperature and concentration functions. One of the most important parts of a fuzzy rule-based inference system for the polyamide solution characteristics determinations is that it allows to interpret the knowledge contained in the model and also to improve it with a-priori knowledge. The results obtained through proposed method are highly accurate and its can be optimized by utilizing the available information during the modeling process. The results showed that it is feasible in theory and reliable on calculation applying Mamdani fuzzy inference system to the estimation of optical and surface properties of a polyamide solution.

  4. Fuzzy architecture assessment for critical infrastructure resilience

    SciTech Connect

    Muller, George

    2012-12-01

    This paper presents an approach for the selection of alternative architectures in a connected infrastructure system to increase resilience of the overall infrastructure system. The paper begins with a description of resilience and critical infrastructure, then summarizes existing approaches to resilience, and presents a fuzzy-rule based method of selecting among alternative infrastructure architectures. This methodology includes considerations which are most important when deciding on an approach to resilience. The paper concludes with a proposed approach which builds on existing resilience architecting methods by integrating key system aspects using fuzzy memberships and fuzzy rule sets. This novel approach aids the systems architect in considering resilience for the evaluation of architectures for adoption into the final system architecture.

  5. Neurocontrol and fuzzy logic: Connections and designs

    NASA Technical Reports Server (NTRS)

    Werbos, Paul J.

    1991-01-01

    Artificial neural networks (ANNs) and fuzzy logic are complementary technologies. ANNs extract information from systems to be learned or controlled, while fuzzy techniques mainly use verbal information from experts. Ideally, both sources of information should be combined. For example, one can learn rules in a hybrid fashion, and then calibrate them for better whole-system performance. ANNs offer universal approximation theorems, pedagogical advantages, very high-throughput hardware, and links to neurophysiology. Neurocontrol - the use of ANNs to directly control motors or actuators, etc. - uses five generalized designs, related to control theory, which can work on fuzzy logic systems as well as ANNs. These designs can copy what experts do instead of what they say, learn to track trajectories, generalize adaptive control, and maximize performance or minimize cost over time, even in noisy environments. Design tradeoffs and future directions are discussed throughout.

  6. Fuzzy control of flexible structure using piezoelements

    NASA Astrophysics Data System (ADS)

    Doosthoseini, Alireza; Yousefi koma, Aghil; Shasti, Behrouz; Rohani, Omid

    2008-03-01

    In this paper the vibration suppression of a flexible structure using fuzzy controller with bonded piezoelements is investigated. A flexible beam with PZT piezoceramics as sensor and actuators is fabricated at the Advanced Dynamic and Control Systems lab (ADCSL). A dynamic model of the smart structure is derived from an experimental system ID. On the other hand using finite element method (FEM), a theoretical model of the structure is obtained which is in good agreement with the experimental model. A fuzzy control system is then designed and implemented for vibration suppression of the smart beam subjected to the impulse excitation and resonance disturbances. Results show the effectiveness of the fuzzy controller and its advantage over conventional controllers.

  7. Systems of fuzzy equations in structural mechanics

    NASA Astrophysics Data System (ADS)

    Skalna, Iwona; Rama Rao, M. V.; Pownuk, Andrzej

    2008-08-01

    Systems of linear and nonlinear equations with fuzzy parameters are relevant to many practical problems arising in structure mechanics, electrical engineering, finance, economics and physics. In this paper three methods for solving such equations are discussed: method for outer interval solution of systems of linear equations depending linearly on interval parameters, fuzzy finite element method proposed by Rama Rao and sensitivity analysis method. The performance and advantages of presented methods are described with illustrative examples. Extended version of the present paper can be downloaded from the web page of the UTEP [I. Skalna, M.V. Rama Rao, A. Pownuk, Systems of fuzzy equations in structural mechanics, The University of Texas at El Paso, Department of Mathematical Sciences Research Reports Series, , Texas Research Report No. 2007-01, 2007].

  8. Fuzzy logic in autonomous orbital operations

    NASA Technical Reports Server (NTRS)

    Lea, Robert N.; Jani, Yashvant

    1991-01-01

    Fuzzy logic can be used advantageously in autonomous orbital operations that require the capability of handling imprecise measurements from sensors. Several applications are underway to investigate fuzzy logic approaches and develop guidance and control algorithms for autonomous orbital operations. Translational as well as rotational control of a spacecraft have been demonstrated using space shuttle simulations. An approach to a camera tracking system has been developed to support proximity operations and traffic management around the Space Station Freedom. Pattern recognition and object identification algorithms currently under development will become part of this camera system at an appropriate level in the future. A concept to control environment and life support systems for large Lunar based crew quarters is also under development. Investigations in the area of reinforcement learning, utilizing neural networks, combined with a fuzzy logic controller, are planned as a joint project with the Ames Research Center.

  9. Comments on some theories of fuzzy computation

    NASA Astrophysics Data System (ADS)

    Gerla, Giangiacomo

    2016-05-01

    In classical computability theory, there are several (equivalent) definitions of computable function, decidable subset and semi-decidable subset. This paper is devoted to the discussion of some proposals for extending these definitions to the framework of fuzzy set theory. The paper mainly focuses on the notions of fuzzy Turing machine and fuzzy computability by limit processes. The basic idea of this paper is that the presence of real numbers in the interval [0,1] forces us to refer to endless approximation processes (as in recursive analysis) and not to processes terminating after a finite number of steps and giving the exact output (as in recursive arithmetic). In accordance with such a point of view, an extension of the famous Church thesis is proposed.

  10. Towards quantifying fuzzy stream power

    NASA Astrophysics Data System (ADS)

    Schwanghart, W.; Korup, O.

    2012-04-01

    Deterministic flow direction algorithms such as the D8 have wide application in numerical models of landscape evolution. These simple algorithms play a central role in quantifying drainage basin area, and hence approximating—via empirically derived relationships from regional flood frequency and hydraulic geometry—stream power or fluvial erosion potential. Here we explore how alternative algorithms that employ a probabilistic choice of flow direction affect quantitative estimates of stream power. We test a probabilistic multi-flow direction algorithm within the MATLAB TopoToolbox in model and real landscapes of low topographic relief and minute gradients, where potentially fuzzy drainage divides are dictated by, among others, alluvial fan dynamics, playa infill, and groundwater fluxes and seepage. We employ a simplistic numerical landscape evolution model that simulates fluvial incision and hillslope diffusion and explicitly models the existence and capture of endorheic basins that prevail in (semi-)arid, low-relief landscapes. We discuss how using this probabilistic multi-flow direction algorithm helps represent and quantify uncertainty about spatio-temporal drainage divide locations and how this bears on quantitative estimates of downstream stream power and fluvial erosion potential as well as their temporal dynamics.

  11. Edge Preserved Speckle Noise Reduction Using Integrated Fuzzy Filters

    PubMed Central

    Dewal, M. L.; Rohit, Manoj Kumar

    2014-01-01

    Echocardiographic images are inherent with speckle noise which makes visual reading and analysis quite difficult. The multiplicative speckle noise masks finer details, necessary for diagnosis of abnormalities. A novel speckle reduction technique based on integration of geometric, wiener, and fuzzy filters is proposed and analyzed in this paper. The denoising applications of fuzzy filters are studied and analyzed along with 26 denoising techniques. It is observed that geometric filter retains noise and, to address this issue, wiener filter is embedded into the geometric filter during iteration process. The performance of geometric-wiener filter is further enhanced using fuzzy filters and the proposed despeckling techniques are called integrated fuzzy filters. Fuzzy filters based on moving average and median value are employed in the integrated fuzzy filters. The performances of integrated fuzzy filters are tested on echocardiographic images and synthetic images in terms of image quality metrics. It is observed that the performance parameters are highest in case of integrated fuzzy filters in comparison to fuzzy and geometric-fuzzy filters. The clinical validation reveals that the output images obtained using geometric-wiener, integrated fuzzy, nonlocal means, and details preserving anisotropic diffusion filters are acceptable. The necessary finer details are retained in the denoised echocardiographic images.

  12. Data Processing on Database Management Systems with Fuzzy Query

    NASA Astrophysics Data System (ADS)

    Şimşek, Irfan; Topuz, Vedat

    In this study, a fuzzy query tool (SQLf) for non-fuzzy database management systems was developed. In addition, samples of fuzzy queries were made by using real data with the tool developed in this study. Performance of SQLf was tested with the data about the Marmara University students' food grant. The food grant data were collected in MySQL database by using a form which had been filled on the web. The students filled a form on the web to describe their social and economical conditions for the food grant request. This form consists of questions which have fuzzy and crisp answers. The main purpose of this fuzzy query is to determine the students who deserve the grant. The SQLf easily found the eligible students for the grant through predefined fuzzy values. The fuzzy query tool (SQLf) could be used easily with other database system like ORACLE and SQL server.

  13. Fuzzy Logic and Its Application in Football Team Ranking

    PubMed Central

    Li, Junhong

    2014-01-01

    Fuzzy set theory and fuzzy logic are a highly suitable and applicable basis for developing knowledge-based systems in physical education for tasks such as the selection for athletes, the evaluation for different training approaches, the team ranking, and the real-time monitoring of sports data. In this paper, we use fuzzy set theory and apply fuzzy clustering analysis in football team ranking. Based on some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T7, T3, T1, T9, T10, T8, T11, T12, T2, T6, T5, T4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range. PMID:25032227

  14. Generalizations of fuzzy linguistic control points in geometric design

    NASA Astrophysics Data System (ADS)

    Sallehuddin, M. H.; Wahab, A. F.; Gobithaasan, R. U.

    2014-07-01

    Control points are geometric primitives that play an important role in designing the geometry curve and surface. When these control points are blended with some basis functions, there are several geometric models such as Bezier, B-spline and NURBS(Non-Uniform Rational B-Spline) will be produced. If the control points are defined by the theory of fuzzy sets, then fuzzy geometric models are produced. But the fuzzy geometric models can only solve the problem of uncertainty complex. This paper proposes a new definition of fuzzy control points with linguistic terms. When the fuzzy control points with linguistic terms are blended with basis functions, then a fuzzy linguistic geometric model is produced. This paper ends with some numerical examples illustrating linguistic control attributes of fuzzy geometric models.

  15. Assessment of heart disease using fuzzy classification techniques.

    PubMed

    Pop, H F; Pop, T L; Sarbu, C

    2001-08-17

    In this paper we discuss the classification results of cardiac patients of ischemical cardiopathy, valvular heart disease, and arterial hypertension, based on 19 characteristics (descriptors) including ECHO data, effort testings, and age and weight. In this order we have used different fuzzy clustering algorithms, namely hierarchical fuzzy clustering, hierarchical and horizontal fuzzy characteristics clustering, and a new clustering technique, fuzzy hierarchical cross-classification. The characteristics clustering techniques produce fuzzy partitions of the characteristics involved and, thus, are useful tools for studying the similarities between different characteristics and for essential characteristics selection. The cross-classification algorithm produces not only a fuzzy partition of the cardiac patients analyzed, but also a fuzzy partition of their considered characteristics. In this way it is possible to identify which characteristics are responsible for the similarities or dissimilarities observed between different groups of patients. PMID:12806074

  16. Fuzzy Behavior-Based Navigation for Planetary

    NASA Technical Reports Server (NTRS)

    Tunstel, Edward; Danny, Harrison; Lippincott, Tanya; Jamshidi, Mo

    1997-01-01

    Adaptive behavioral capabilities are necessary for robust rover navigation in unstructured and partially-mapped environments. A control approach is described which exploits the approximate reasoning capability of fuzzy logic to produce adaptive motion behavior. In particular, a behavior-based architecture for hierarchical fuzzy control of microrovers is presented. Its structure is described, as well as mechanisms of control decision-making which give rise to adaptive behavior. Control decisions for local navigation result from a consensus of recommendations offered only by behaviors that are applicable to current situations. Simulation predicts the navigation performance on a microrover in simplified Mars-analog terrain.

  17. Image segmentation using fuzzy LVQ clustering networks

    NASA Technical Reports Server (NTRS)

    Tsao, Eric Chen-Kuo; Bezdek, James C.; Pal, Nikhil R.

    1992-01-01

    In this note we formulate image segmentation as a clustering problem. Feature vectors extracted from a raw image are clustered into subregions, thereby segmenting the image. A fuzzy generalization of a Kohonen learning vector quantization (LVQ) which integrates the Fuzzy c-Means (FCM) model with the learning rate and updating strategies of the LVQ is used for this task. This network, which segments images in an unsupervised manner, is thus related to the FCM optimization problem. Numerical examples on photographic and magnetic resonance images are given to illustrate this approach to image segmentation.

  18. Palmprint based multidimensional fuzzy vault scheme.

    PubMed

    Liu, Hailun; Sun, Dongmei; Xiong, Ke; Qiu, Zhengding

    2014-01-01

    Fuzzy vault scheme (FVS) is one of the most popular biometric cryptosystems for biometric template protection. However, error correcting code (ECC) proposed in FVS is not appropriate to deal with real-valued biometric intraclass variances. In this paper, we propose a multidimensional fuzzy vault scheme (MDFVS) in which a general subspace error-tolerant mechanism is designed and embedded into FVS to handle intraclass variances. Palmprint is one of the most important biometrics; to protect palmprint templates; a palmprint based MDFVS implementation is also presented. Experimental results show that the proposed scheme not only can deal with intraclass variances effectively but also could maintain the accuracy and meanwhile enhance security. PMID:24892094

  19. Neural and fuzzy robotic hand control.

    PubMed

    Tascillo, A; Bourbakis, N

    1999-01-01

    An efficient first grasp for a wheelchair robotic arm-hand with pressure sensing is determined and presented. The grasp is learned by combining the advantages of neural networks and fuzzy logic into a hybrid control algorithm which learns from its tip and slip control experiences. Neurofuzzy modifications are outlined, and basic steps are demonstrated in preparation for physical implementation. Choice of object approach vector based on fuzzy tip and slip data and an expert supervisor, as well as training of a diagnostic neural tip and slip controller, are the focus of this work. PMID:18252342

  20. Signal trend identification with fuzzy methods.

    SciTech Connect

    Reifman, J.; Tsoukalas, L. H.; Wang, X.; Wei, T. Y. C.

    1999-08-19

    A fuzzy-logic-based methodology for on-line signal trend identification is introduced. Although signal trend identification is complicated by the presence of noise, fuzzy logic can help capture important features of on-line signals and classify incoming power plant signals into increasing, decreasing and steady-state trend categories. In order to verify the methodology, a code named PROTREN is developed and tested using plant data. The results indicate that the code is capable of detecting transients accurately, identifying trends reliably, and not misinterpreting a steady-state signal as a transient one.

  1. Palmprint Based Multidimensional Fuzzy Vault Scheme

    PubMed Central

    Liu, Hailun; Sun, Dongmei; Xiong, Ke; Qiu, Zhengding

    2014-01-01

    Fuzzy vault scheme (FVS) is one of the most popular biometric cryptosystems for biometric template protection. However, error correcting code (ECC) proposed in FVS is not appropriate to deal with real-valued biometric intraclass variances. In this paper, we propose a multidimensional fuzzy vault scheme (MDFVS) in which a general subspace error-tolerant mechanism is designed and embedded into FVS to handle intraclass variances. Palmprint is one of the most important biometrics; to protect palmprint templates; a palmprint based MDFVS implementation is also presented. Experimental results show that the proposed scheme not only can deal with intraclass variances effectively but also could maintain the accuracy and meanwhile enhance security. PMID:24892094

  2. The cognitive bases for the design of a new class of fuzzy logic controllers: The clearness transformation fuzzy logic controller

    NASA Technical Reports Server (NTRS)

    Sultan, Labib; Janabi, Talib

    1992-01-01

    This paper analyses the internal operation of fuzzy logic controllers as referenced to the human cognitive tasks of control and decision making. Two goals are targeted. The first goal focuses on the cognitive interpretation of the mechanisms employed in the current design of fuzzy logic controllers. This analysis helps to create a ground to explore the potential of enhancing the functional intelligence of fuzzy controllers. The second goal is to outline the features of a new class of fuzzy controllers, the Clearness Transformation Fuzzy Logic Controller (CT-FLC), whereby some new concepts are advanced to qualify fuzzy controllers as 'cognitive devices' rather than 'expert system devices'. The operation of the CT-FLC, as a fuzzy pattern processing controller, is explored, simulated, and evaluated.

  3. Application and classification of fuzzy dynamic system and fuzzy linguistic controller with examples illustrated

    NASA Astrophysics Data System (ADS)

    Wang, Paul P.; Tyan, Ching-Yu

    1993-12-01

    This paper presents the classification of fuzzy dynamic systems and fuzzy linguistic controllers (FLC) into standard types (TYPE 1 through TYPE 7). The need, utility value, and the logic behind this classification are given. The proposed classification is the result of studying many known examples of FLC applications. The impact of this classification to new designs and to the improved performance of classical and modern control systems is an important consideration.

  4. Hesitant Fuzzy Soft Subalgebras and Ideals in BCK/BCI-Algebras

    PubMed Central

    Jun, Young Bae; Ahn, Sun Shin

    2014-01-01

    As a link between classical soft sets and hesitant fuzzy sets, the notion of hesitant fuzzy soft sets is introduced and applied to a decision making problem in the papers by Babitha and John (2013) and Wang et al. (2014). The aim of this paper is to apply hesitant fuzzy soft set for dealing with several kinds of theories in BCK/BCI-algebras. The notions of hesitant fuzzy soft subalgebras and (closed) hesitant fuzzy soft ideals are introduced, and related properties are investigated. Relations between a hesitant fuzzy soft subalgebra and a (closed) hesitant fuzzy soft ideal are discussed. Conditions for a hesitant fuzzy soft set to be a hesitant fuzzy soft subalgebra are given, and conditions for a hesitant fuzzy soft subalgebra to be a hesitant fuzzy soft ideal are provided. Characterizations of a (closed) hesitant fuzzy soft ideal are considered. PMID:25405234

  5. A Laboratory Testbed for Embedded Fuzzy Control

    ERIC Educational Resources Information Center

    Srivastava, S.; Sukumar, V.; Bhasin, P. S.; Arun Kumar, D.

    2011-01-01

    This paper presents a novel scheme called "Laboratory Testbed for Embedded Fuzzy Control of a Real Time Nonlinear System." The idea is based upon the fact that project-based learning motivates students to learn actively and to use their engineering skills acquired in their previous years of study. It also fosters initiative and focuses students'…

  6. Fuzzy modelling of Atlantic salmon physical habitat

    NASA Astrophysics Data System (ADS)

    St-Hilaire, André; Mocq, Julien; Cunjak, Richard

    2015-04-01

    Fish habitat models typically attempt to quantify the amount of available river habitat for a given fish species for various flow and hydraulic conditions. To achieve this, information on the preferred range of values of key physical habitat variables (e.g. water level, velocity, substrate diameter) for the targeted fishs pecies need to be modelled. In this context, we developed several habitat suitability indices sets for three Atlantic salmon life stages (young-of-the-year (YOY), parr, spawning adults) with the help of fuzzy logic modeling. Using the knowledge of twenty-seven experts, from both sides of the Atlantic Ocean, we defined fuzzy sets of four variables (depth, substrate size, velocity and Habitat Suitability Index, or HSI) and associated fuzzy rules. When applied to the Romaine River (Canada), median curves of standardized Weighted Usable Area (WUA) were calculated and a confidence interval was obtained by bootstrap resampling. Despite the large range of WUA covered by the expert WUA curves, confidence intervals were relatively narrow: an average width of 0.095 (on a scale of 0 to 1) for spawning habitat, 0.155 for parr rearing habitat and 0.160 for YOY rearing habitat. When considering an environmental flow value corresponding to 90% of the maximum reached by WUA curve, results seem acceptable for the Romaine River. Generally, this proposed fuzzy logic method seems suitable to model habitat availability for the three life stages, while also providing an estimate of uncertainty in salmon preferences.

  7. Fuzzy Control/Space Station automation

    NASA Technical Reports Server (NTRS)

    Gersh, Mark

    1990-01-01

    Viewgraphs on fuzzy control/space station automation are presented. Topics covered include: Space Station Freedom (SSF); SSF evolution; factors pointing to automation & robotics (A&R); astronaut office inputs concerning A&R; flight system automation and ground operations applications; transition definition program; and advanced automation software tools.

  8. Fuzzy cellular automata models in immunology

    NASA Astrophysics Data System (ADS)

    Ahmed, E.

    1996-10-01

    The self-nonself character of antigens is considered to be fuzzy. The Chowdhury et al. cellular automata model is generalized accordingly. New steady states are found. The first corresponds to a below-normal help and suppression and is proposed to be related to autoimmune diseases. The second corresponds to a below-normal B-cell level.

  9. Competencies: Fuzzy Concepts to Context. Symposium.

    ERIC Educational Resources Information Center

    2002

    This document contains three papers from a symposium titled "Competence: Fuzzy Concepts to Context.""Sales Superstars: Defining Competencies Needed for Sales Performance" (Darlene Russ-Eft, Edward Del Gaizo, Jeannie Moulton, Ruth Pangilinan) discusses a study in which an analysis of 1,688 critical incidents revealed 16 competencies that define the…

  10. Information Clustering Based on Fuzzy Multisets.

    ERIC Educational Resources Information Center

    Miyamoto, Sadaaki

    2003-01-01

    Proposes a fuzzy multiset model for information clustering with application to information retrieval on the World Wide Web. Highlights include search engines; term clustering; document clustering; algorithms for calculating cluster centers; theoretical properties concerning clustering algorithms; and examples to show how the algorithms work.…

  11. Fuzzy-Trace Theory and Memory Development

    ERIC Educational Resources Information Center

    Brainerd, C. J.; Reyna, V. F.

    2004-01-01

    We review recent applications of fuzzy-trace theory to memory development, organizing the presentation around two themes: the theory's explanatory principles and experimental findings about memory development that follow as predictions from those principles. The featured explanatory principles are: parallel storage of verbatim and gist traces,…

  12. Fuzzy Functional Dependencies and Redundancy Elimination.

    ERIC Educational Resources Information Center

    Bosc, Patrick; Dubois, Didier; Prade, Henri

    1998-01-01

    For about 10 years, several proposals to deal with ill-known information in database management systems have been made, and extensions of the relational data model have been proposed. An overview of functional dependencies is provided, and the connection between fuzzy functional dependencies and database design is discussed. (AEF)

  13. FUZZY LOGIC CONTROL OF AC INDUCTION MOTORS

    EPA Science Inventory

    The paper discusses the fuzzy logic control (FLC) of electric motors, being investigated under the sponsorship of the U.S. EPA to reduce energy consumption when motors are operated at less than rated speeds and loads. lectric motors use 60% of the electrical energy generated in t...

  14. Fuzzy Expert System to Characterize Students

    ERIC Educational Resources Information Center

    Van Hecke, T.

    2011-01-01

    Students wanting to succeed in higher education are required to adopt an adequate learning approach. By analyzing individual learning characteristics, teachers can give personal advice to help students identify their learning success factors. An expert system based on fuzzy logic can provide economically viable solutions to help students identify…

  15. Fuzzy Logic Connectivity in Semiconductor Defect Clustering

    SciTech Connect

    Gleason, S.S.; Kamowski, T.P.; Tobin, K.W.

    1999-01-24

    In joining defects on semiconductor wafer maps into clusters, it is common for defects caused by different sources to overlap. Simple morphological image processing tends to either join too many unrelated defects together or not enough together. Expert semiconductor fabrication engineers have demonstrated that they can easily group clusters of defects from a common manufacturing problem source into a single signature. Capturing this thought process is ideally suited for fuzzy logic. A system of rules was developed to join disconnected clusters based on properties such as elongation, orientation, and distance. The clusters are evaluated on a pair-wise basis using the fuzzy rules and are joined or not joined based on a defuzzification and threshold. The system continuously re-evaluates the clusters under consideration as their fuzzy memberships change with each joining action. The fuzzy membership functions for each pair-wise feature, the techniques used to measure the features, and methods for improving the speed of the system are all developed. Examples of the process are shown using real-world semiconductor wafer maps obtained from chip manufacturers. The algorithm is utilized in the Spatial Signature Analyzer (SSA) software, a joint development project between Oak Ridge National Lab (ORNL) and SEMATECH.

  16. FuzzyCLIPS from research to product

    NASA Technical Reports Server (NTRS)

    Bochsler, Dan; Dohmann, Edgar

    1994-01-01

    This paper describes the commercial productization of FuzzyCLIPS which was developed under a NASA Phase 2 SBIR contract. The intent of this paper is to provide a general roadmap of the processes that are required to make a viable, marketable product once its concept and development are complete.

  17. Fuzzy Set Theoretical Approach to Document Retrieval.

    ERIC Educational Resources Information Center

    Radecki, Tadeusz

    1979-01-01

    Presents a new method of document retrieval based on the fundamental operations of fuzzy set theory. Basic notions are introduced. Then the syntax and semantics of the proposed language for document retrieval is given, and an algorithm allocating documents to particular queries is described and its properties are discussed. (Author/CWM)

  18. SOIL QUALITY ASSESSMENT USING FUZZY MODELING

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Maintaining soil productivity is essential if agriculture production systems are to be sustainable, thus soil quality is an essential issue. However, there is a paucity of tools for measurement for the purpose of understanding changes in soil quality. Here the possibility of using fuzzy modeling t...

  19. Evaluation of Yield Maps Using Fuzzy Indicators

    Technology Transfer Automated Retrieval System (TEKTRAN)

    This paper presents a new methodology for the evaluation of yield maps using fuzzy indicators, which takes into account atypical phenomena and expert opinions regarding the maps. This methodology could allow for improved methods for deciding boundary locations for precision application of production...

  20. Neuro-Fuzzy Phasing of Segmented Mirrors

    NASA Technical Reports Server (NTRS)

    Olivier, Philip D.

    1999-01-01

    A new phasing algorithm for segmented mirrors based on neuro-fuzzy techniques is described. A unique feature of this algorithm is the introduction of an observer bank. Its effectiveness is tested in a very simple model with remarkable success. The new algorithm requires much less computational effort than existing algorithms and therefore promises to be quite useful when implemented on more complex models.

  1. Indeterminacy, linguistic semantics and fuzzy logic

    SciTech Connect

    Novak, V.

    1996-12-31

    In this paper, we discuss the indeterminacy phenomenon which has two distinguished faces, namely uncertainty modeled especially by the probability theory and vagueness, modeled by fuzzy logic. Other important mathematical model of vagueness is provided by the Alternative Set Theory. We focus on some of the basic concepts of these theories in connection with mathematical modeling of the linguistic semantics.

  2. Revisiting separation properties of convex fuzzy sets

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Separation of convex sets by hyperplanes has been extensively studied on crisp sets. In a seminal paper separability and convexity are investigated, however there is a flaw on the definition of degree of separation. We revisited separation on convex fuzzy sets that have level-wise (crisp) disjointne...

  3. Fuzzy Cognitive Map Modelling Educational Software Adoption

    ERIC Educational Resources Information Center

    Hossain, Sarmin; Brooks, Laurence

    2008-01-01

    Educational software adoption across UK secondary schools is seen as unsatisfactory. Based on stakeholders' perceptions, this paper uses fuzzy cognitive maps (FCMs) to model this adoption context. It discusses the development of the FCM model, using a mixed-methods approach and drawing on participants from three UK secondary schools. The study…

  4. Further Study of Multigranulation T-Fuzzy Rough Sets

    PubMed Central

    Zhang, Xiaoyan; Sun, Wenxin

    2014-01-01

    The optimistic multigranulation T-fuzzy rough set model was established based on multiple granulations under T-fuzzy approximation space by Xu et al., 2012. From the reference, a natural idea is to consider pessimistic multigranulation model in T-fuzzy approximation space. So, in this paper, the main objective is to make further studies according to Xu et al., 2012. The optimistic multigranulation T-fuzzy rough set model is improved deeply by investigating some further properties. And a complete multigranulation T-fuzzy rough set model is constituted by addressing the pessimistic multigranulation T-fuzzy rough set. The full important properties of multigranulation T-fuzzy lower and upper approximation operators are also presented. Moreover, relationships between multigranulation and classical T-fuzzy rough sets have been studied carefully. From the relationships, we can find that the T-fuzzy rough set model is a special instance of the two new types of models. In order to interpret and illustrate optimistic and pessimistic multigranulation T-fuzzy rough set models, a case is considered, which is helpful for applying these theories to practical issues. PMID:25215336

  5. The Intelligence of Dual Simplex Method to Solve Linear Fractional Fuzzy Transportation Problem

    PubMed Central

    Narayanamoorthy, S.; Kalyani, S.

    2015-01-01

    An approach is presented to solve a fuzzy transportation problem with linear fractional fuzzy objective function. In this proposed approach the fractional fuzzy transportation problem is decomposed into two linear fuzzy transportation problems. The optimal solution of the two linear fuzzy transportations is solved by dual simplex method and the optimal solution of the fractional fuzzy transportation problem is obtained. The proposed method is explained in detail with an example. PMID:25810713

  6. Robust fuzzy logic control of mechanical systems

    NASA Astrophysics Data System (ADS)

    Kohn-Rich, Sylvia

    An approach for the design of robust fuzzy control laws for a large class of mechanical systems was developed. The approach applies Lyapunov's Stability Theory to ensure closed loop stability in the presence of plant perturbations and bounded disturbances. It uses inherent properties of an important class of mechanical and aerospace systems, such as robotic manipulators and large spacecraft, to derive closed-loop stability conditions. Based on these conditions, a methodology for the design of robust fuzzy control systems with guaranteed closed-loop stability was developed. Two classes of control laws for mechanical systems were considered. First, a methodology for point-to-point control was formulated. It combines an energy-type approach with Lyapunov's Stability Theory and its extensions, to obtain robust stability conditions for the closed-loop system. A procedure for control system development based on the above conditions is presented. Finally, a procedure for the implementation of the fuzzy control system with guaranteed performance and closed-loop stability characteristics is formulated. In the second part of the dissertation, the problem of robust tracking for mechanical systems was considered. Based on Lyapunov's Stability Theory and its extensions due to Leitmann and Corless, conditions were developed to prove robust stability and performance in the presence of plant uncertainties, bounded disturbances and control saturation. These conditions involve a large number of parameters and functional dependencies that can be chosen by the designer, therefore are well suited for Fuzzy Logic Control implementation. Three different fuzzy implementation methods for the proposed controls system were analyzed and their relative advantages were discussed. An extensive simulation study of the proposed approach was conducted. It demonstrated the excellent performance of the proposed control systems. The proposed method showed superior performance compared to other robust

  7. A novel approach for analyzing fuzzy system reliability using different types of intuitionistic fuzzy failure rates of components.

    PubMed

    Kumar, Mohit; Yadav, Shiv Prasad

    2012-03-01

    This paper addresses the fuzzy system reliability analysis using different types of intuitionistic fuzzy numbers. Till now, in the literature, to analyze the fuzzy system reliability, it is assumed that the failure rates of all components of a system follow the same type of fuzzy set or intuitionistic fuzzy set. However, in practical problems, such type of situation rarely occurs. Therefore, in the present paper, a new algorithm has been introduced to construct the membership function and non-membership function of fuzzy reliability of a system having components following different types of intuitionistic fuzzy failure rates. Functions of intuitionistic fuzzy numbers are calculated to construct the membership function and non-membership function of fuzzy reliability via non-linear programming techniques. Using the proposed algorithm, membership functions and non-membership functions of fuzzy reliability of a series system and a parallel systems are constructed. Our study generalizes the various works of the literature. Numerical examples are given to illustrate the proposed algorithm. PMID:22134065

  8. Fault classification by neural networks and fuzzy logic

    SciTech Connect

    Chwan-Hwa ``John`` Wu; Chihwen Li; Shih, H.; Alexion, C.C.; Ovick, N.L.; Murphy, J.H.

    1995-01-25

    A neural fuzzy-based and a backpropagation neural network-based fault classifier for a three-phase motor will be described in this paper. In order to acquire knowledge, the neural fuzzy classifier incorporates a learning technique to automatically generate membership functions for fuzzy rules, and the backpropagation algorithm is used to train the neural network model. Therefore, in this paper, the preprocessing of signals, fuzzy and neural models, training methods, implementations for real-time response and testing results will be discussed in detail. Furthermore, the generalization capabilities of the neural fuzzy- and backpropagation-based classifiers for waveforms with varying magnitudes, frequencies, noises and positions of spikes and chops in a cycle of a sine wave will be investigated, and the computation requirements needed to achieve real-time response for both fuzzy and neural methods will be compared. {copyright} 1995 {ital American} {ital Institute} {ital of} {ital Physics}

  9. Intelligent fuzzy controller for event-driven real time systems

    NASA Technical Reports Server (NTRS)

    Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

    1992-01-01

    Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

  10. North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 2

    NASA Technical Reports Server (NTRS)

    Villarreal, James A. (Compiler)

    1992-01-01

    This document contains papers presented at the NAFIPS '92 North American Fuzzy Information Processing Society Conference. More than 75 papers were presented at this Conference, which was sponsored by NAFIPS in cooperation with NASA, the Instituto Tecnologico de Morelia, the Indian Society for Fuzzy Mathematics and Information Processing (ISFUMIP), the Instituto Tecnologico de Estudios Superiores de Monterrey (ITESM), the International Fuzzy Systems Association (IFSA), the Japan Society for Fuzzy Theory and Systems, and the Microelectronics and Computer Technology Corporation (MCC). The fuzzy set theory has led to a large number of diverse applications. Recently, interesting applications have been developed which involve the integration of fuzzy systems with adaptive processes such a neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.