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Sample records for fuzzy logic analysis

  1. 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.

  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. Optimization of fuzzy logic analysis by diagonals for pattern recognition

    NASA Astrophysics Data System (ADS)

    Habiballa, Hashim; Hires, Matej

    2017-07-01

    The article presents an optimization of the fuzzy logic analysis method for pattern recognition. The enhancements of the original method through the usage of additional two types of pattern components - leftwise diagonal and rightwise diagonal ones. The method is described in theoretical background and further articles show the implementation and experimental verification of the approach.

  4. 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.

  5. Fuzzy Logic: A New Tool for the Analysis and Organization of International Business Communications.

    ERIC Educational Resources Information Center

    Sondak, Norman E.; Sondak, Eileen M.

    Classical western logic, built on a foundation of true/false, yes/no, right/wrong statements, leads to many difficulties and inconsistencies in the logical analysis and organization of international business communications. This paper presents the basic principles of classical logic and of fuzzy logic, a type of logic developed to allow for…

  6. 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.

  7. Soil productivity analysis based on a fuzzy logic system.

    PubMed

    Duru, Nevcihan; Dökmen, Funda; Canbay, M Mücella; Kurtuluş, Cengiz

    2010-10-01

    Maintaining soil productivity is essential if agriculture production systems are to be sustainable. However, there is a paucity of tools for measurement for the purpose of understanding changes in soil productivity. Fuzzy logic-based analysis offers this possibility. It is a new method on the evaluation of soil productivity in Turkey and even in the world. Values for pH, salinity, carbonate and organic matter were entered into the system as input variables so as to obtain soil productivity as the output. After the membership functions related to input and output were determined, rules were created. Then, the fuzzy logic system was applied separately to pH, salinity, lime and organic matter values of different soil types present in the Kocaeli region with the aim of obtaining corresponding fuzzy values. Thus, soil productivity profiles of the region were deciphered. Organic matter levels in the study field remained below 30 g kg(-1) and varied between 22 and 28 g kg(-1). Productivity values were obtained as a percentage and varied between 16.9% and 18.1%. The lime content of the study soils varied in the range of 33-88 g kg(-1). Average totals for salt values of the field changed between 0.58 and 0.77 g kg(-1). Copyright © 2010 Society of Chemical Industry.

  8. Fuzzy logic analysis optimizations for pattern recognition - Implementation and experimental results

    NASA Astrophysics Data System (ADS)

    Hires, Matej; Habiballa, Hashim

    2017-07-01

    The article presents an practical results of optimization of the fuzzy logic analysis method for pattern recognition. The theoretical background of the proposed theory is shown in the former article extending the original fuzzy logic analysis method. This article shows the implementation and experimental verification of the approach.

  9. 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.

  10. Risk analysis with a fuzzy-logic approach of a complex installation

    NASA Astrophysics Data System (ADS)

    Peikert, Tim; Garbe, Heyno; Potthast, Stefan

    2016-09-01

    This paper introduces a procedural method based on fuzzy logic to analyze systematic the risk of an electronic system in an intentional electromagnetic environment (IEME). The method analyzes the susceptibility of a complex electronic installation with respect to intentional electromagnetic interference (IEMI). It combines the advantages of well-known techniques as fault tree analysis (FTA), electromagnetic topology (EMT) and Bayesian networks (BN) and extends the techniques with an approach to handle uncertainty. This approach uses fuzzy sets, membership functions and fuzzy logic to handle the uncertainty with probability functions and linguistic terms. The linguistic terms add to the risk analysis the knowledge from experts of the investigated system or environment.

  11. 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.

  12. Fuzzy Logic Analysis of Kinase Pathway Crosstalk in TNF/EGF/Insulin-Induced Signaling

    PubMed Central

    Aldridge, Bree B.; Saez-Rodriguez, Julio; Muhlich, Jeremy L.; Sorger, Peter K.; Lauffenburger, Douglas A.

    2009-01-01

    When modeling cell signaling networks, a balance must be struck between mechanistic detail and ease of interpretation. In this paper we apply a fuzzy logic framework to the analysis of a large, systematic dataset describing the dynamics of cell signaling downstream of TNF, EGF, and insulin receptors in human colon carcinoma cells. Simulations based on fuzzy logic recapitulate most features of the data and generate several predictions involving pathway crosstalk and regulation. We uncover a relationship between MK2 and ERK pathways that might account for the previously identified pro-survival influence of MK2. We also find unexpected inhibition of IKK following EGF treatment, possibly due to down-regulation of autocrine signaling. More generally, fuzzy logic models are flexible, able to incorporate qualitative and noisy data, and powerful enough to produce quantitative predictions and new biological insights about the operation of signaling networks. PMID:19343194

  13. Anaesthesia monitoring using fuzzy logic.

    PubMed

    Baig, Mirza Mansoor; Gholamhosseini, Hamid; Kouzani, Abbas; Harrison, Michael J

    2011-10-01

    Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of off-line tests. The training and testing data set are selected randomly from 30 sets of patients' data. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.

  14. Using fuzzy logic analysis for siting decisions of infiltration trenches for highway runoff control.

    PubMed

    Ki, Seo Jin; Ray, Chittaranjan

    2014-09-15

    Determining optimal locations for best management practices (BMPs), including their field considerations and limitations, plays an important role for effective stormwater management. However, these issues have been often overlooked in modeling studies that focused on downstream water quality benefits. This study illustrates the methodology of locating infiltration trenches at suitable locations from spatial overlay analyses which combine multiple layers that address different aspects of field application into a composite map. Using seven thematic layers for each analysis, fuzzy logic was employed to develop a site suitability map for infiltration trenches, whereas the DRASTIC method was used to produce a groundwater vulnerability map on the island of Oahu, Hawaii, USA. In addition, the analytic hierarchy process (AHP), one of the most popular overlay analyses, was used for comparison to fuzzy logic. The results showed that the AHP and fuzzy logic methods developed significantly different index maps in terms of best locations and suitability scores. Specifically, the AHP method provided a maximum level of site suitability due to its inherent aggregation approach of all input layers in a linear equation. The most eligible areas in locating infiltration trenches were determined from the superposition of the site suitability and groundwater vulnerability maps using the fuzzy AND operator. The resulting map successfully balanced qualification criteria for a low risk of groundwater contamination and the best BMP site selection. The results of the sensitivity analysis showed that the suitability scores were strongly affected by the algorithms embedded in fuzzy logic; therefore, caution is recommended with their use in overlay analysis. Accordingly, this study demonstrates that the fuzzy logic analysis can not only be used to improve spatial decision quality along with other overlay approaches, but also is combined with general water quality models for initial and refined

  15. Fuzzy logic applications for failure analysis and diagnosis of a primary circuit of the HTR nuclear power plant

    NASA Astrophysics Data System (ADS)

    Kitowski, J.; Ksiażek, E.

    1985-10-01

    The paper presents some preliminary results of diagnostic analysis of the stimulated primary circuit of the THTR-300 nuclear power plant, based on the fuzzy sets theory. Two methods are used: the solution of the fuzzy relational equations and its extension to fuzzy logic. The first stage of the diagnostic system is the identification of the plant in the sense of fuzzy relations and the second stage is the failure or disturbance recognition. According to the fuzzy logic, lower and upper bounds of the failure vectors are obtained. Such an expert system can supply the operator in a correct recognition of the current state of the complex plant.

  16. An Analysis Regarding the Possibility of Using Fuzzy Logic in Inventory Management

    NASA Astrophysics Data System (ADS)

    Stoia, Claudiu-Leonardo

    2014-11-01

    The paper presents a brief state-of-the-art survey regarding the use of fuzzy logic in inventory management. Its goal is to motivate enthusiastic entrepreneurs to take into account the benefits of using fuzzy logic inventory control systems. It offers a guide to model an inventory system having a free fuzzy tool as starting point

  17. 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.

  18. 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.

  19. Analysis of atomic force microscopy data for surface characterization using fuzzy logic

    SciTech Connect

    Al-Mousa, Amjed; Niemann, Darrell L.; Niemann, Devin J.; Gunther, Norman G.; Rahman, Mahmud

    2011-07-15

    In this paper we present a methodology to characterize surface nanostructures of thin films. The methodology identifies and isolates nanostructures using Atomic Force Microscopy (AFM) data and extracts quantitative information, such as their size and shape. The fuzzy logic based methodology relies on a Fuzzy Inference Engine (FIE) to classify the data points as being top, bottom, uphill, or downhill. The resulting data sets are then further processed to extract quantitative information about the nanostructures. In the present work we introduce a mechanism which can consistently distinguish crowded surfaces from those with sparsely distributed structures and present an omni-directional search technique to improve the structural recognition accuracy. In order to demonstrate the effectiveness of our approach we present a case study which uses our approach to quantitatively identify particle sizes of two specimens each with a unique gold nanoparticle size distribution. - Research Highlights: {yields} A Fuzzy logic analysis technique capable of characterizing AFM images of thin films. {yields} The technique is applicable to different surfaces regardless of their densities. {yields} Fuzzy logic technique does not require manual adjustment of the algorithm parameters. {yields} The technique can quantitatively capture differences between surfaces. {yields} This technique yields more realistic structure boundaries compared to other methods.

  20. 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.

  1. Profitability analysis of a femtosecond laser system for cataract surgery using a fuzzy logic approach

    PubMed Central

    Trigueros, José Antonio; Piñero, David P; Ismail, Mahmoud M

    2016-01-01

    AIM To define the financial and management conditions required to introduce a femtosecond laser system for cataract surgery in a clinic using a fuzzy logic approach. METHODS In the simulation performed in the current study, the costs associated to the acquisition and use of a commercially available femtosecond laser platform for cataract surgery (VICTUS, TECHNOLAS Perfect Vision GmbH, Bausch & Lomb, Munich, Germany) during a period of 5y were considered. A sensitivity analysis was performed considering such costs and the countable amortization of the system during this 5y period. Furthermore, a fuzzy logic analysis was used to obtain an estimation of the money income associated to each femtosecond laser-assisted cataract surgery (G). RESULTS According to the sensitivity analysis, the femtosecond laser system under evaluation can be profitable if 1400 cataract surgeries are performed per year and if each surgery can be invoiced more than $500. In contrast, the fuzzy logic analysis confirmed that the patient had to pay more per surgery, between $661.8 and $667.4 per surgery, without considering the cost of the intraocular lens (IOL). CONCLUSION A profitability of femtosecond laser systems for cataract surgery can be obtained after a detailed financial analysis, especially in those centers with large volumes of patients. The cost of the surgery for patients should be adapted to the real flow of patients with the ability of paying a reasonable range of cost. PMID:27500115

  2. Profitability analysis of a femtosecond laser system for cataract surgery using a fuzzy logic approach.

    PubMed

    Trigueros, José Antonio; Piñero, David P; Ismail, Mahmoud M

    2016-01-01

    To define the financial and management conditions required to introduce a femtosecond laser system for cataract surgery in a clinic using a fuzzy logic approach. In the simulation performed in the current study, the costs associated to the acquisition and use of a commercially available femtosecond laser platform for cataract surgery (VICTUS, TECHNOLAS Perfect Vision GmbH, Bausch & Lomb, Munich, Germany) during a period of 5y were considered. A sensitivity analysis was performed considering such costs and the countable amortization of the system during this 5y period. Furthermore, a fuzzy logic analysis was used to obtain an estimation of the money income associated to each femtosecond laser-assisted cataract surgery (G). According to the sensitivity analysis, the femtosecond laser system under evaluation can be profitable if 1400 cataract surgeries are performed per year and if each surgery can be invoiced more than $500. In contrast, the fuzzy logic analysis confirmed that the patient had to pay more per surgery, between $661.8 and $667.4 per surgery, without considering the cost of the intraocular lens (IOL). A profitability of femtosecond laser systems for cataract surgery can be obtained after a detailed financial analysis, especially in those centers with large volumes of patients. The cost of the surgery for patients should be adapted to the real flow of patients with the ability of paying a reasonable range of cost.

  3. 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

  4. Machining analysis of natural fibre reinforced composites using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Balasubramanian, K.; Sultan, M. T. H.; Cardona, F.; Rajeswari, N.

    2016-10-01

    In this work, a new composite plate with natural jute fibre as the reinforcement fibres and isophthalic polyester as the resin was manufactured and subjected to a series of end milling operation by changing three input factors namely speed, feed rate and depth of cut. During each operation, the output responses namely thrust force and torque were measured. The responses were analyzed using Taguchi method to examine the relation between the input factors and output responses, and also to know the most influencing factors on the responses. The data was also analyzed using fuzzy rule model for prediction of responses for a range of input factors. The results showed that all three factors chosen have significant effect on the responses. The fuzzy model data in comparison with the experimental values shows only a marginal error and hence the prediction was highly satisfactory.

  5. Nursing and fuzzy logic: an integrative review.

    PubMed

    Jensen, Rodrigo; Lopes, Maria Helena Baena de Moraes

    2011-01-01

    This study conducted an integrative review investigating how fuzzy logic has been used in research with the participation of nurses. The article search was carried out in the CINAHL, EMBASE, SCOPUS, PubMed and Medline databases, with no limitation on time of publication. Articles written in Portuguese, English and Spanish with themes related to nursing and fuzzy logic with the authorship or participation of nurses were included. The final sample included 21 articles from eight countries. For the purpose of analysis, the articles were distributed into categories: theory, method and model. In nursing, fuzzy logic has significantly contributed to the understanding of subjects related to: imprecision or the need of an expert; as a research method; and in the development of models or decision support systems and hard technologies. The use of fuzzy logic in nursing has shown great potential and represents a vast field for research.

  6. 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.

  7. 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.

  8. 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.

  9. Fuzzy logic program at SGS-Thomson

    NASA Astrophysics Data System (ADS)

    Pagni, Andrea; Poluzzi, Rinaldo; Rizzotto, GianGuido

    1993-12-01

    From its conception by Professor Lotfi A. Zadeh in the early '60s, Fuzzy Logic has slowly won acceptance, first in the academic world, then in industry. Its success is mainly due to the different perspective with which problems are tackled. Thanks to Fuzzy Logic we have moved from a numerical/analytical description to a quantitative/qualitative one. It is important to stress that this different perspective not only allows us to solve analysis/control problems at lower costs but can also allow otherwise insoluble problems to be solved at acceptable costs. Of course, it must be stressed that Fuzzy Systems cannot match the computational precision of traditional techniques but seek, instead, to find acceptable solutions in shorter times. Recognizing the enormous importance of fuzzy logic in the markets of the future, SGS-THOMSON intends to produce devices belonging to a new class of machines: Fuzzy Computational Machines. For this purpose a major research project has been established considering the architectural aspects and system implications of fuzzy logic, the development of dedicated VLSI components and supporting software.

  10. 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

  11. 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.

  12. Pattern recognition using linguistic fuzzy logic predictors

    NASA Astrophysics Data System (ADS)

    Habiballa, Hashim

    2016-06-01

    The problem of pattern recognition has been solved with numerous methods in the Artificial Intelligence field. We present an unconventional method based on Lingustic Fuzzy Logic Forecaster which is primarily used for the task of time series analysis and prediction through logical deduction wtih linguistic variables. This method should be used not only to the time series prediction itself, but also for recognition of patterns in a signal with seasonal component.

  13. 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

  14. Fuzzy Logic for Incidence Geometry

    PubMed Central

    2016-01-01

    The paper presents a mathematical framework for approximate geometric reasoning with extended objects in the context of Geography, in which all entities and their relationships are described by human language. These entities could be labelled by commonly used names of landmarks, water areas, and so forth. Unlike single points that are given in Cartesian coordinates, these geographic entities are extended in space and often loosely defined, but people easily perform spatial reasoning with extended geographic objects “as if they were points.” Unfortunately, up to date, geographic information systems (GIS) miss the capability of geometric reasoning with extended objects. The aim of the paper is to present a mathematical apparatus for approximate geometric reasoning with extended objects that is usable in GIS. In the paper we discuss the fuzzy logic (Aliev and Tserkovny, 2011) as a reasoning system for geometry of extended objects, as well as a basis for fuzzification of the axioms of incidence geometry. The same fuzzy logic was used for fuzzification of Euclid's first postulate. Fuzzy equivalence relation “extended lines sameness” is introduced. For its approximation we also utilize a fuzzy conditional inference, which is based on proposed fuzzy “degree of indiscernibility” and “discernibility measure” of extended points. PMID:27689133

  15. Fuzzy Logic for Incidence Geometry.

    PubMed

    Tserkovny, Alex

    The paper presents a mathematical framework for approximate geometric reasoning with extended objects in the context of Geography, in which all entities and their relationships are described by human language. These entities could be labelled by commonly used names of landmarks, water areas, and so forth. Unlike single points that are given in Cartesian coordinates, these geographic entities are extended in space and often loosely defined, but people easily perform spatial reasoning with extended geographic objects "as if they were points." Unfortunately, up to date, geographic information systems (GIS) miss the capability of geometric reasoning with extended objects. The aim of the paper is to present a mathematical apparatus for approximate geometric reasoning with extended objects that is usable in GIS. In the paper we discuss the fuzzy logic (Aliev and Tserkovny, 2011) as a reasoning system for geometry of extended objects, as well as a basis for fuzzification of the axioms of incidence geometry. The same fuzzy logic was used for fuzzification of Euclid's first postulate. Fuzzy equivalence relation "extended lines sameness" is introduced. For its approximation we also utilize a fuzzy conditional inference, which is based on proposed fuzzy "degree of indiscernibility" and "discernibility measure" of extended points.

  16. Fuzzy forecasting based on fuzzy-trend logical relationship groups.

    PubMed

    Chen, Shyi-Ming; Wang, Nai-Yi

    2010-10-01

    In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.

  17. [Research on the Application of Fuzzy Logic to Systems Analysis and Control

    NASA Technical Reports Server (NTRS)

    1998-01-01

    Research conducted with the support of NASA Grant NCC2-275 has been focused in the main on the development of fuzzy logic and soft computing methodologies and their applications to systems analysis and control. with emphasis 011 problem areas which are of relevance to NASA's missions. One of the principal results of our research has been the development of a new methodology called Computing with Words (CW). Basically, in CW words drawn from a natural language are employed in place of numbers for computing and reasoning. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers, and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost, and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW.

  18. A Measurement-Theoretic Analysis of the Fuzzy Logic Model of Perception.

    ERIC Educational Resources Information Center

    Crowther, Court S.; And Others

    1995-01-01

    The fuzzy logic model of perception (FLMP) is analyzed from a measurement-theoretic perspective. The choice rule of FLMP is shown to be equivalent to a version of the Rasch model. In fact, FLMP can be reparameterized as a simple two-category logit model. (SLD)

  19. A Measurement-Theoretic Analysis of the Fuzzy Logic Model of Perception.

    ERIC Educational Resources Information Center

    Crowther, Court S.; And Others

    1995-01-01

    The fuzzy logic model of perception (FLMP) is analyzed from a measurement-theoretic perspective. The choice rule of FLMP is shown to be equivalent to a version of the Rasch model. In fact, FLMP can be reparameterized as a simple two-category logit model. (SLD)

  20. 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.

  1. 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.

  2. Molecular processors: from qubits to fuzzy logic.

    PubMed

    Gentili, Pier Luigi

    2011-03-14

    Single molecules or their assemblies are information processing devices. Herein it is demonstrated how it is possible to process different types of logic through molecules. As long as decoherent effects are maintained far away from a pure quantum mechanical system, quantum logic can be processed. If the collapse of superimposed or entangled wavefunctions is unavoidable, molecules can still be used to process either crisp (binary or multi-valued) or fuzzy logic. The way for implementing fuzzy inference engines is declared and it is supported by the examples of molecular fuzzy logic systems devised so far. Fuzzy logic is drawing attention in the field of artificial intelligence, because it models human reasoning quite well. This ability may be due to some structural analogies between a fuzzy logic system and the human nervous system. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. Probabilistic and fuzzy logic in clinical diagnosis.

    PubMed

    Licata, G

    2007-06-01

    In this study I have compared classic and fuzzy logic and their usefulness in clinical diagnosis. The theory of probability is often considered a device to protect the classical two-valued logic from the evidence of its inadequacy to understand and show the complexity of world [1]. This can be true, but it is not possible to discard the theory of probability. I will argue that the problems and the application fields of the theory of probability are very different from those of fuzzy logic. After the introduction on the theoretical bases of fuzzy approach to logic, I have reported some diagnostic argumentations employing fuzzy logic. The state of normality and the state of disease often fight their battle on scalar quantities of biological values and it is not hard to establish a correspondence between the biological values and the percent values of fuzzy logic. Accordingly, I have suggested some applications of fuzzy logic in clinical diagnosis and in particular I have utilised a fuzzy curve to recognise subjects with diabetes mellitus, renal failure and liver disease. The comparison between classic and fuzzy logic findings seems to indicate that fuzzy logic is more adequate to study the development of biological events. In fact, fuzzy logic is useful when we have a lot of pieces of information and when we dispose to scalar quantities. In conclusion, increasingly the development of technology offers new instruments to measure pathological parameters through scalar quantities, thus it is reasonable to think that in the future fuzzy logic will be employed more in clinical diagnosis.

  4. Performance analysis of a semiactive suspension system with particle swarm optimization and fuzzy logic control.

    PubMed

    Qazi, Abroon Jamal; de Silva, Clarence W; Khan, Afzal; Khan, Muhammad Tahir

    2014-01-01

    This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control.

  5. Performance Analysis of a Semiactive Suspension System with Particle Swarm Optimization and Fuzzy Logic Control

    PubMed Central

    Qazi, Abroon Jamal; de Silva, Clarence W.

    2014-01-01

    This paper uses a quarter model of an automobile having passive and semiactive suspension systems to develop a scheme for an optimal suspension controller. Semi-active suspension is preferred over passive and active suspensions with regard to optimum performance within the constraints of weight and operational cost. A fuzzy logic controller is incorporated into the semi-active suspension system. It is able to handle nonlinearities through the use of heuristic rules. Particle swarm optimization (PSO) is applied to determine the optimal gain parameters for the fuzzy logic controller, while maintaining within the normalized ranges of the controller inputs and output. The performance of resulting optimized system is compared with different systems that use various control algorithms, including a conventional passive system, choice options of feedback signals, and damping coefficient limits. Also, the optimized semi-active suspension system is evaluated for its performance in relation to variation in payload. Furthermore, the systems are compared with respect to the attributes of road handling and ride comfort. In all the simulation studies it is found that the optimized fuzzy logic controller surpasses the other types of control. PMID:24574868

  6. 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, T 7, T 3, T 1, T 9, T 10, T 8, T 11, T 12, T 2, T 6, T 5, T 4, 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

  7. Fuzzy logic and its application in football team ranking.

    PubMed

    Zeng, Wenyi; 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, T 7, T 3, T 1, T 9, T 10, T 8, T 11, T 12, T 2, T 6, T 5, T 4, 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.

  8. 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.

  9. Fuzzy logic and coarse coding using programmable logic devices

    NASA Astrophysics Data System (ADS)

    Brooks, Geoffrey

    2009-05-01

    Naturally-occurring sensory signal processing algorithms, such as those that inspired fuzzy-logic control, can be integrated into non-naturally-occurring high-performance technology, such as programmable logic devices, to realize novel bio-inspired designs. Research is underway concerning an investigation into using field programmable logic devices (FPLD's) to implement fuzzy logic sensory processing. A discussion is provided concerning the commonality between bio-inspired fuzzy logic algorithms and coarse coding that is prevalent in naturally-occurring sensory systems. Undergraduate design projects using fuzzy logic for an obstacle-avoidance robot has been accomplished at our institution and other places; numerous other successful fuzzy logic applications can be found as well. The long-term goal is to leverage such biomimetic algorithms for future applications. This paper outlines a design approach for implementing fuzzy-logic algorithms into reconfigurable computing devices. This paper is presented in an effort to connect with others who may be interested in collaboration as well as to establish a starting point for future research.

  10. Fuzzy logic color detection: Blue areas in melanoma dermoscopy images

    PubMed Central

    Lingala, Mounika; Stanley, R. Joe; Rader, Ryan K.; Hagerty, Jason; Rabinovitz, Harold S.; Oliviero, Margaret; Choudhry, Iqra; Stoecker, William V.

    2014-01-01

    Fuzzy logic image analysis techniques were used to analyze three shades of blue (lavender blue, light blue, and dark blue) in dermoscopic images for melanoma detection. A logistic regression model provided up to 82.7% accuracy for melanoma discrimination for 866 images. With a support vector machines (SVM) classifier, lower accuracy was obtained for individual shades (79.9–80.1%) compared with up to 81.4% accuracy with multiple shades. All fuzzy blue logic alpha cuts scored higher than the crisp case. Fuzzy logic techniques applied to multiple shades of blue can assist in melanoma detection. These vector-based fuzzy logic techniques can be extended to other image analysis problems involving multiple colors or color shades. PMID:24786720

  11. Fuzzy logic color detection: Blue areas in melanoma dermoscopy images.

    PubMed

    Lingala, Mounika; Stanley, R Joe; Rader, Ryan K; Hagerty, Jason; Rabinovitz, Harold S; Oliviero, Margaret; Choudhry, Iqra; Stoecker, William V

    2014-07-01

    Fuzzy logic image analysis techniques were used to analyze three shades of blue (lavender blue, light blue, and dark blue) in dermoscopic images for melanoma detection. A logistic regression model provided up to 82.7% accuracy for melanoma discrimination for 866 images. With a support vector machines (SVM) classifier, lower accuracy was obtained for individual shades (79.9-80.1%) compared with up to 81.4% accuracy with multiple shades. All fuzzy blue logic alpha cuts scored higher than the crisp case. Fuzzy logic techniques applied to multiple shades of blue can assist in melanoma detection. These vector-based fuzzy logic techniques can be extended to other image analysis problems involving multiple colors or color shades.

  12. 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.

  13. Fuzzy-logic-based network for complex systems risk assessment: application to ship performance analysis.

    PubMed

    Abou, Seraphin C

    2012-03-01

    In this paper, a new interpretation of intuitionistic fuzzy sets in the advanced framework of the Dempster-Shafer theory of evidence is extended to monitor safety-critical systems' performance. Not only is the proposed approach more effective, but it also takes into account the fuzzy rules that deal with imperfect knowledge/information and, therefore, is different from the classical Takagi-Sugeno fuzzy system, which assumes that the rule (the knowledge) is perfect. We provide an analytical solution to the practical and important problem of the conceptual probabilistic approach for formal ship safety assessment using the fuzzy set theory that involves uncertainties associated with the reliability input data. Thus, the overall safety of the ship engine is investigated as an object of risk analysis using the fuzzy mapping structure, which considers uncertainty and partial truth in the input-output mapping. The proposed method integrates direct evidence of the frame of discernment and is demonstrated through references to examples where fuzzy set models are informative. These simple applications illustrate how to assess the conflict of sensor information fusion for a sufficient cooling power system of vessels under extreme operation conditions. It was found that propulsion engine safety systems are not only a function of many environmental and operation profiles but are also dynamic and complex. Copyright © 2011 Elsevier Ltd. All rights reserved.

  14. Electronic nose guided determination of frying disposal time of sunflower oil using fuzzy logic analysis.

    PubMed

    Upadhyay, Rohit; Sehwag, Sneha; Mishra, Hari Niwas

    2017-04-15

    An electronic nose (e-nose), having 18 metal oxide semiconductor (MOS) sensors, guided determination of frying disposal time of sunflower oil is reported. The ranking and screening of MOS sensors, specific for volatile organic compounds, was performed using fuzzy logic. A correlation was examined between rancidity indices of fried oil (total polar compounds (TPC), and triglyceride dimers-polymers (TGDP), among others) and e-nose based odor index. Fuzzy logic screened 6 MOS sensors (LY2/G, LY2/AA, LY2/GH, LY2/gCT1, T30/1, and P30/1) to deconvolute the rancid fried oils using hierarchical clustering on principal component space. A good relationship was noted between rancidity indices and odor index (R(2)>0.85). Based on maximum discard limits of rancidity indices (25% TPC and 10% TGDP), the frying disposal time of 15.2h (TPC) vs. 15.8h (e-nose) and 15.5h (TGDP) vs. 16.3h (e-nose) was determined. The demonstrated methodology holds a potential extension to different fried oils and products. Copyright © 2016 Elsevier Ltd. All rights reserved.

  15. Fuzzy Logic Controller Stability Analysis Using a Satisfiability Modulo Theories Approach

    NASA Technical Reports Server (NTRS)

    Arnett, Timothy; Cook, Brandon; Clark, Matthew A.; Rattan, Kuldip

    2017-01-01

    While many widely accepted methods and techniques exist for validation and verification of traditional controllers, at this time no solutions have been accepted for Fuzzy Logic Controllers (FLCs). Due to the highly nonlinear nature of such systems, and the fact that developing a valid FLC does not require a mathematical model of the system, it is quite difficult to use conventional techniques to prove controller stability. Since safety-critical systems must be tested and verified to work as expected for all possible circumstances, the fact that FLC controllers cannot be tested to achieve such requirements poses limitations on the applications for such technology. Therefore, alternative methods for verification and validation of FLCs needs to be explored. In this study, a novel approach using formal verification methods to ensure the stability of a FLC is proposed. Main research challenges include specification of requirements for a complex system, conversion of a traditional FLC to a piecewise polynomial representation, and using a formal verification tool in a nonlinear solution space. Using the proposed architecture, the Fuzzy Logic Controller was found to always generate negative feedback, but inconclusive for Lyapunov stability.

  16. Control loop noise rejection using fuzzy logic.

    PubMed

    Hay, Glen; Svrcek, William; Ross, Timothy; Young, Brent

    2005-10-01

    This paper describes an application of fuzzy logic to noise rejection in a control loop. This new use of fuzzy logic solves the problem of sluggish control loop response when using a set-point range to stop constant valve chattering due to noise in the output signal being sent to a control valve. Multiple related variables and a general understanding of their inter-relationship must be available for this method to be successfully applied. An overview of the specific fuzzy logic method used for this application is presented along with guidelines for the practical application. In addition, this paper includes results from the successful implementation of fuzzy logic to a control loop on a pilot plant distillation column.

  17. Fuzzy logic-based spike sorting system.

    PubMed

    Balasubramanian, Karthikeyan; Obeid, Iyad

    2011-05-15

    We present a new method for autonomous real-time spike sorting using a fuzzy logic inference engine. The engine assigns each detected event a 'spikiness index' from zero to one that quantifies the extent to which the detected event is like an ideal spike. Spikes can then be sorted by simply clustering the spikiness indices. The sorter is defined in terms of natural language rules that, once defined, are static and thus require no user intervention or calibration. The sorter was tested using extracellular recordings from three animals: a macaque, an owl monkey and a rat. Simulation results show that the fuzzy sorter performed equal to or better than the benchmark principal component analysis (PCA) based sorter. Importantly, there was no degradation in fuzzy sorter performance when the spikes were not temporally aligned prior to sorting. In contrast, PCA sorter performance dropped by 27% when sorting unaligned spikes. Since the fuzzy sorter is computationally trivial and requires no spike alignment, it is suitable for scaling into large numbers of parallel channels where computational overhead and the need for operator intervention would preclude other spike sorters.

  18. 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.

  19. Can fuzzy logic make things more clear?

    PubMed

    Hazelzet, Jan A

    2009-01-01

    Intensive care is a complex environment involving many signals, data and observations. Clinical decision support and artificial intelligence using fuzzy logic and closed loop techniques are methods that might help us to handle this complexity in a safe, effective and efficient way. Merouani and colleagues have performed a study using fuzzy logic and closed loop techniques to more effectively wean patients with sepsis from norepinephrine infusion.

  20. Analysis of microseismicity using fuzzy logic and fractals for fracture network characterization

    NASA Astrophysics Data System (ADS)

    Aminzadeh, F.; Ayatollahy Tafti, T.; Maity, D.; Boyle, K.; Sahimi, M.; Sammis, C. G.

    2010-12-01

    The area where microseismic events occur may be correlated with the fracture network at a geothermal field. For an Enhanced Geothermal System (EGS) reservoir, an extensive fracture network with a large aerial distribution is required. Pore-pressure increase, temperature changes, volume change due to fluid withdrawal/injection and chemical alteration of fracture surfaces are all mechanisms that may explain microseismic events at a geothermal field. If these mechanisms are operative, any fuzzy cluster of the microseismic events should represent a connected fracture network. Drilling new EGS wells (both injection and production wells) in these locations may facilitate the creation of an EGS reservoir. In this article we use the fuzzy clustering technique to find the location and characteristics of fracture networks in the Geysers geothermal field. We also show that the centers of these fuzzy clusters move in time, which may represent fracture propagation or fluid movement within the fracture network. Furthermore, analyzing the distribution of fuzzy hypocenters and quantifying their fractal structure helps us to develop an accurate fracture map for the reservoir. Combining the fuzzy clustering results with the fractal analysis allows us to better understand the mechanisms for fracture stimulation and better characterize the evolution of the fracture network. We also show how micro-earthquake date collected in different time periods can be correlated with drastic changes in the distribution of active fractures resulting from injection, production or other transient events.

  1. 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.

  2. Determining a human cardiac pacemaker using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Varnavsky, A. N.; Antonenco, A. V.

    2017-01-01

    The paper presents a possibility of estimating a human cardiac pacemaker using combined application of nonlinear integral transformation and fuzzy logic, which allows carrying out the analysis in the real-time mode. The system of fuzzy logical conclusion is proposed, membership functions and rules of fuzzy products are defined. It was shown that the ratio of the value of a truth degree of the winning rule condition to the value of a truth degree of any other rule condition is at least 3.

  3. Seismic event interpretation using fuzzy logic and neural networks

    SciTech Connect

    Maurer, W.J.; Dowla, F.U.

    1994-01-01

    In the computer interpretation of seismic data, unknown sources of seismic events must be represented and reasoned about using measurements from the recorded signal. In this report, we develop the use of fuzzy logic to improve our ability to interpret weak seismic events. Processing strategies for the use of fuzzy set theory to represent vagueness and uncertainty, a phenomena common in seismic data analysis, are developed. A fuzzy-assumption based truth-maintenance-inferencing engine is also developed. Preliminary results in interpreting seismic events using the fuzzy neural network knowledge-based system are presented.

  4. 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.

  5. Fuzzy Logic Analysis of Alumina-Titania Deposition Yield During APS Process

    NASA Astrophysics Data System (ADS)

    Kanta, Abdoul-Fatah; Montavon, Ghislain; Planche, Marie-Pierre; Coddet, Christian

    2007-12-01

    The behavior modeling of Atmospheric Plasma Spray (APS) process requires a global approach which considers interrelated non-linear relationships between coating characteristics/properties in-service and process parameters (power, feedstock injection, kinematics, etc.). Such an approach would permit to reduce the development costs. To reach this objective, the knowledge of the interactions between process parameters plays a relevant role in the optimization. This work intends to develop a behavior model based on fuzzy logic concepts. Here, the model considers the deposition yield as the result of the process and it establishes relationships with power process parameter (arc current intensity, plasma gas total flow rate, hydrogen content) on the basis of fuzzy rules. The model hence permits to discriminate the role and the effects of each power process parameters. The modeling results are compared to experimental data. The specific case of the deposition of alumina-titania (Al2O3-TiO2, 13% by weight) by Atmospheric Plasma Spraying (APS) is considered.

  6. 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.

  7. Fuzzy logic-based flight control system design

    NASA Astrophysics Data System (ADS)

    Nho, Kyungmoon

    The application of fuzzy logic to aircraft motion control is studied in this dissertation. The self-tuning fuzzy techniques are developed by changing input scaling factors to obtain a robust fuzzy controller over a wide range of operating conditions and nonlinearities for a nonlinear aircraft model. It is demonstrated that the properly adjusted input scaling factors can meet the required performance and robustness in a fuzzy controller. For a simple demonstration of the easy design and control capability of a fuzzy controller, a proportional-derivative (PD) fuzzy control system is compared to the conventional controller for a simple dynamical system. This thesis also describes the design principles and stability analysis of fuzzy control systems by considering the key features of a fuzzy control system including the fuzzification, rule-base and defuzzification. The wing-rock motion of slender delta wings, a linear aircraft model and the six degree of freedom nonlinear aircraft dynamics are considered to illustrate several self-tuning methods employing change in input scaling factors. Finally, this dissertation is concluded with numerical simulation of glide-slope capture in windshear demonstrating the robustness of the fuzzy logic based flight control system.

  8. Photorefractive optical fuzzy-logic processor based on grating degeneracy

    NASA Astrophysics Data System (ADS)

    Wu, Weishu; Yang, Changxi; Campbell, Scott; Yeh, Pochi

    1995-04-01

    A novel optical fuzzy-logic processor using light-induced gratings in photorefractive crystals is proposed and demonstrated. By exploiting grating degeneracy, one can easily implement parallel fuzzy-logic functions in disjunctive normal form.

  9. Robust Fuzzy Logic Stabilization with Disturbance Elimination

    PubMed Central

    Danapalasingam, Kumeresan A.

    2014-01-01

    A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design. PMID:25177713

  10. 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.

  11. Robust fuzzy logic stabilization with disturbance elimination.

    PubMed

    Danapalasingam, Kumeresan A

    2014-01-01

    A robust fuzzy logic controller is proposed for stabilization and disturbance rejection in nonlinear control systems of a particular type. The dynamic feedback controller is designed as a combination of a control law that compensates for nonlinear terms in a control system and a dynamic fuzzy logic controller that addresses unknown model uncertainties and an unmeasured disturbance. Since it is challenging to derive a highly accurate mathematical model, the proposed controller requires only nominal functions of a control system. In this paper, a mathematical derivation is carried out to prove that the controller is able to achieve asymptotic stability by processing state measurements. Robustness here refers to the ability of the controller to asymptotically steer the state vector towards the origin in the presence of model uncertainties and a disturbance input. Simulation results of the robust fuzzy logic controller application in a magnetic levitation system demonstrate the feasibility of the control design.

  12. Answer Sets in a Fuzzy Equilibrium Logic

    NASA Astrophysics Data System (ADS)

    Schockaert, Steven; Janssen, Jeroen; Vermeir, Dirk; de Cock, Martine

    Since its introduction, answer set programming has been generalized in many directions, to cater to the needs of real-world applications. As one of the most general “classical” approaches, answer sets of arbitrary propositional theories can be defined as models in the equilibrium logic of Pearce. Fuzzy answer set programming, on the other hand, extends answer set programming with the capability of modeling continuous systems. In this paper, we combine the expressiveness of both approaches, and define answer sets of arbitrary fuzzy propositional theories as models in a fuzzification of equilibrium logic. We show that the resulting notion of answer set is compatible with existing definitions, when the syntactic restrictions of the corresponding approaches are met. We furthermore locate the complexity of the main reasoning tasks at the second level of the polynomial hierarchy. Finally, as an illustration of its modeling power, we show how fuzzy equilibrium logic can be used to find strong Nash equilibria.

  13. A Fuzzy Logic Decision Support Tool

    DTIC Science & Technology

    1989-10-01

    also be improved . The Navy and Air Force have developed expert systems , the Air Strike Plan- ning Advisor (ASPA) and the Knowledge- based Replanning...use of fuzzy logic not only in industrial process con- trol but, more generally, in knowledge- based systems in which the deduction of an answer to a...having to move closer. If not already known, these percentages can be computed based on a number of known parameters and distribution functions. Fuzzy

  14. Fuzzy logic for personalized healthcare and diagnostics: FuzzyApp--a fuzzy logic based allergen-protein predictor.

    PubMed

    Saravanan, Vijayakumar; Lakshmi, P T V

    2014-09-01

    The path to personalized medicine demands the use of new and customized biopharmaceutical products containing modified proteins. Hence, assessment of these products for allergenicity becomes mandatory before they are introduced as therapeutics. Despite the availability of different tools to predict the allergenicity of proteins, it remains challenging to predict the allergens and nonallergens, when they share significant sequence similarity with known nonallergens and allergens, respectively. Hence, we propose "FuzzyApp," a novel fuzzy rule based system to evaluate the quality of the query protein to be an allergen. It measures the allergenicity of the protein based on the fuzzy IF-THEN rules derived from five different modules. On various datasets, FuzzyApp outperformed other existing methods and retained balance between sensitivity and specificity, with positive Mathew's correlation coefficient. The high specificity of allergen-like putative nonallergens (APN) revealed the FuzzyApp's capability in distinguishing the APN from allergens. In addition, the error analysis and whole proteome dataset analysis suggest the efficiency and consistency of the proposed method. Further, FuzzyApp predicted the Tropomyosin from various allergenic and nonallergenic sources accurately. The web service created allows batch sequence submission, and outputs the result as readable sentences rather than values alone, which assists the user in understanding why and what features are responsible for the prediction. FuzzyApp is implemented using PERL CGI and is freely accessible at http://fuzzyapp.bicpu.edu.in/predict.php . We suggest the use of Fuzzy logic has much potential in biomarker and personalized medicine research to enhance predictive capabilities of post-genomics diagnostics.

  15. Fuzzy Logic-Based Audio Pattern Recognition

    NASA Astrophysics Data System (ADS)

    Malcangi, M.

    2008-11-01

    Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

  16. Design and implementation of fuzzy logic controllers. Thesis Final Report, 27 Jul. 1992 - 1 Jan. 1993

    NASA Technical Reports Server (NTRS)

    Abihana, Osama A.; Gonzalez, Oscar R.

    1993-01-01

    The main objectives of our research are to present a self-contained overview of fuzzy sets and fuzzy logic, develop a methodology for control system design using fuzzy logic controllers, and to design and implement a fuzzy logic controller for a real system. We first present the fundamental concepts of fuzzy sets and fuzzy logic. Fuzzy sets and basic fuzzy operations are defined. In addition, for control systems, it is important to understand the concepts of linguistic values, term sets, fuzzy rule base, inference methods, and defuzzification methods. Second, we introduce a four-step fuzzy logic control system design procedure. The design procedure is illustrated via four examples, showing the capabilities and robustness of fuzzy logic control systems. This is followed by a tuning procedure that we developed from our design experience. Third, we present two Lyapunov based techniques for stability analysis. Finally, we present our design and implementation of a fuzzy logic controller for a linear actuator to be used to control the direction of the Free Flight Rotorcraft Research Vehicle at LaRC.

  17. On the use of fuzzy logic assessment for high consequence implementation risk analysis

    SciTech Connect

    Spray, S.; Cooper, A.; Bennett, R.

    1994-05-01

    ``High consequence`` operations are systems, structures, and/or strategies for which it is crucial to provide assured protection against some potential catastrophe or catastrophes. The word ``catastrophe`` implies a significant loss of a resource (e.g., money, lives, health, environment, national security, etc.). The implementation of operations that are to be as catastrophe-free as possible must incorporate a very high level of protection. Unfortunately, real world limitations on available resources, mainly money and time, preclude absolute protection. For this reason, conventional ``risk analysis`` focuses on ``cost-effective`` protection, demonstrating through analysis that the benefits of any protective measures chosen outweigh their cost. This is a ``crisp`` one-parameter (usually monetary) comparison. A major problem with this approach, especially for high consequence operations, is that it may not be possible to accurately determine quantitative ``costs,`` and furthermore, the costs may not be accurately quantifiable. Similarly, it may not be possible to accurately determine or to quantify the benefits of protection in high consequence operations. These weaknesses are addressed in this paper by introducing multiple parameters instead of a single monetary measure both for costs of implementing protective measures and their benefits. In addition, a fuzzy-algebra comparison based on fuzzy number theory is introduced as a tool in providing cost/benefit tradeoff depiction, with the incorporation of measures of the uncertainty that necessarily exists in the input information. The result allows a more informative comparison to be made through use of fuzzy results, especially at the extreme bounds of the uncertainty.

  18. 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.

  19. 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.

  20. Professional Learning: A Fuzzy Logic-Based Modelling Approach

    ERIC Educational Resources Information Center

    Gravani, M. N.; Hadjileontiadou, S. J.; Nikolaidou, G. N.; Hadjileontiadis, L. J.

    2007-01-01

    Studies have suggested that professional learning is influenced by two key parameters, i.e., climate and planning, and their associated variables (mutual respect, collaboration, mutual trust, supportiveness, openness). In this paper, we applied analysis of the relationships between the proposed quantitative, fuzzy logic-based model and a series of…

  1. Professional Learning: A Fuzzy Logic-Based Modelling Approach

    ERIC Educational Resources Information Center

    Gravani, M. N.; Hadjileontiadou, S. J.; Nikolaidou, G. N.; Hadjileontiadis, L. J.

    2007-01-01

    Studies have suggested that professional learning is influenced by two key parameters, i.e., climate and planning, and their associated variables (mutual respect, collaboration, mutual trust, supportiveness, openness). In this paper, we applied analysis of the relationships between the proposed quantitative, fuzzy logic-based model and a series of…

  2. Fuzzy Logic Approaches to Multi-Objective Decision-Making in Aerospace Applications

    NASA Technical Reports Server (NTRS)

    Hardy, Terry L.

    1994-01-01

    Fuzzy logic allows for the quantitative representation of multi-objective decision-making problems which have vague or fuzzy objectives and parameters. As such, fuzzy logic approaches are well-suited to situations where alternatives must be assessed by using criteria that are subjective and of unequal importance. This paper presents an overview of fuzzy logic and provides sample applications from the aerospace industry. Applications include an evaluation of vendor proposals, an analysis of future space vehicle options, and the selection of a future space propulsion system. On the basis of the results provided in this study, fuzzy logic provides a unique perspective on the decision-making process, allowing the evaluator to assess the degree to which each option meets the evaluation criteria. Future decision-making should take full advantage of fuzzy logic methods to complement existing approaches in the selection of alternatives.

  3. 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.

  4. Automated cloud classification with a fuzzy logic expert system

    NASA Technical Reports Server (NTRS)

    Tovinkere, Vasanth; Baum, Bryan A.

    1993-01-01

    An unresolved problem in current cloud retrieval algorithms concerns the analysis of scenes containing overlapping cloud layers. Cloud parameterizations are very important both in global climate models and in studies of the Earth's radiation budget. Most cloud retrieval schemes, such as the bispectral method used by the International Satellite Cloud Climatology Project (ISCCP), have no way of determining whether overlapping cloud layers exist in any group of satellite pixels. One promising method uses fuzzy logic to determine whether mixed cloud and/or surface types exist within a group of pixels, such as cirrus, land, and water, or cirrus and stratus. When two or more class types are present, fuzzy logic uses membership values to assign the group of pixels partially to the different class types. The strength of fuzzy logic lies in its ability to work with patterns that may include more than one class, facilitating greater information extraction from satellite radiometric data. The development of the fuzzy logic rule-based expert system involves training the fuzzy classifier with spectral and textural features calculated from accurately labeled 32x32 regions of Advanced Very High Resolution Radiometer (AVHRR) 1.1-km data. The spectral data consists of AVHRR channels 1 (0.55-0.68 mu m), 2 (0.725-1.1 mu m), 3 (3.55-3.93 mu m), 4 (10.5-11.5 mu m), and 5 (11.5-12.5 mu m), which include visible, near-infrared, and infrared window regions. The textural features are based on the gray level difference vector (GLDV) method. A sophisticated new interactive visual image Classification System (IVICS) is used to label samples chosen from scenes collected during the FIRE IFO II. The training samples are chosen from predefined classes, chosen to be ocean, land, unbroken stratiform, broken stratiform, and cirrus. The November 28, 1991 NOAA overpasses contain complex multilevel cloud situations ideal for training and validating the fuzzy logic expert system.

  5. Fuzzy logic membership implementation using optical hardware components

    NASA Astrophysics Data System (ADS)

    Moniem, T. A.; Saleh, M. H.

    2012-10-01

    Intelligent control techniques consist of knowledge-based expert or fuzzy logic control. One obvious drawback in many such applications is that fuzzy logic memberships are implemented at the lowest level. In high-bandwidth processes, this form of fuzzy logic membership implementation would require high speed and accuracy in the presence of strong nonlinearities and dynamic coupling. This paper presents a novel methodology called the Opto-fuzzy method to design a fuzzy logic membership using an optical hardware component. The proposed scheme is applied to triangular-shaped and half trapezoidal-shaped membership functions.

  6. Modelling with ANIMO: between fuzzy logic and differential equations.

    PubMed

    Schivo, Stefano; Scholma, Jetse; van der Vet, Paul E; Karperien, Marcel; Post, Janine N; van de Pol, Jaco; Langerak, Rom

    2016-07-27

    Computational support is essential in order to reason on the dynamics of biological systems. We have developed the software tool ANIMO (Analysis of Networks with Interactive MOdeling) to provide such computational support and allow insight into the complex networks of signaling events occurring in living cells. ANIMO makes use of timed automata as an underlying model, thereby enabling analysis techniques from computer science like model checking. Biology experts are able to use ANIMO via a user interface specifically tailored for biological applications. In this paper we compare the use of ANIMO with some established formalisms on two case studies. ANIMO is a powerful and user-friendly tool that can compete with existing continuous and discrete paradigms. We show this by presenting ANIMO models for two case studies: Drosophila melanogaster circadian clock, and signal transduction events downstream of TNF α and EGF in HT-29 human colon carcinoma cells. The models were originally developed with ODEs and fuzzy logic, respectively. Two biological case studies that have been modeled with respectively ODE and fuzzy logic models can be conveniently modeled using ANIMO. The ANIMO models require less parameters than ODEs and are more precise than fuzzy logic. For this reason we position the modelling paradigm of ANIMO between ODEs and fuzzy logic.

  7. 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)

  8. 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)

  9. Autonomous vehicle motion control, approximate maps, and fuzzy logic

    NASA Technical Reports Server (NTRS)

    Ruspini, Enrique H.

    1993-01-01

    Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.

  10. Autonomous vehicle motion control, approximate maps, and fuzzy logic

    NASA Technical Reports Server (NTRS)

    Ruspini, Enrique H.

    1993-01-01

    Progress on research on the control of actions of autonomous mobile agents using fuzzy logic is presented. The innovations described encompass theoretical and applied developments. At the theoretical level, results of research leading to the combined utilization of conventional artificial planning techniques with fuzzy logic approaches for the control of local motion and perception actions are presented. Also formulations of dynamic programming approaches to optimal control in the context of the analysis of approximate models of the real world are examined. Also a new approach to goal conflict resolution that does not require specification of numerical values representing relative goal importance is reviewed. Applied developments include the introduction of the notion of approximate map. A fuzzy relational database structure for the representation of vague and imprecise information about the robot's environment is proposed. Also the central notions of control point and control structure are discussed.

  11. Stock and option portfolio using fuzzy logic approach

    NASA Astrophysics Data System (ADS)

    Sumarti, Novriana; Wahyudi, Nanang

    2014-03-01

    Fuzzy Logic in decision-making process has been widely implemented in various problems in industries. It is the theory of imprecision and uncertainty that was not based on probability theory. Fuzzy Logic adds values of degree between absolute true and absolute false. It starts with and builds on a set of human language rules supplied by the user. The fuzzy systems convert these rules to their mathematical equivalents. This could simplify the job of the system designer and the computer, and results in much more accurate representations of the way systems behave in the real world. In this paper we examine the decision making process of stock and option trading by the usage of MACD (Moving Average Convergence Divergence) technical analysis and Option Pricing with Fuzzy Logic approach. MACD technical analysis is for the prediction of the trends of underlying stock prices, such as bearish (going downward), bullish (going upward), and sideways. By using Fuzzy C-Means technique and Mamdani Fuzzy Inference System, we define the decision output where the value of MACD is high then decision is "Strong Sell", and the value of MACD is Low then the decision is "Strong Buy". We also implement the fuzzification of the Black-Scholes option-pricing formula. The stock and options methods are implemented on a portfolio of one stock and its options. Even though the values of input data, such as interest rates, stock price and its volatility, cannot be obtain accurately, these fuzzy methods can give a belief degree of the calculated the Black-Scholes formula so we can make the decision on option trading. The results show the good capability of the methods in the prediction of stock price trends. The performance of the simulated portfolio for a particular period of time also shows good return.

  12. Fuzzy logic controller to improve powerline communication

    NASA Astrophysics Data System (ADS)

    Tirrito, Salvatore

    2015-12-01

    The Power Line Communications (PLC) technology allows the use of the power grid in order to ensure the exchange of data information among devices. This work proposes an approach, based on Fuzzy Logic, that dynamically manages the amplitude of the signal, with which each node transmits, by processing the master-slave link quality measured and the master-slave distance. The main objective of this is to reduce both the impact of communication interferences induced and power consumption.

  13. Recycling troubleshooting experience with fuzzy logic

    NASA Astrophysics Data System (ADS)

    Lirov, Yuval

    1993-12-01

    Increasing complexity of systems requires improved support capabilities. Automation controls the support costs while meeting the growing demands at the same time. Proteus is a firm-wide proactive problem management system with automated advisory capabilities. Proteus non-obtrusively accumulates troubleshooting expertise and quickly recycles it by combining case-based reasoning with text retrieval and fuzzy logic pattern matching. It has linear on-line and sub-quadratic preprocessing computational time complexities.

  14. Application of fuzzy logic to forecast seasonal runoff

    NASA Astrophysics Data System (ADS)

    Mahabir, C.; Hicks, F. E.; Robinson Fayek, A.

    2003-12-01

    Each spring in Alberta, Canada, the potential snowmelt runoff is forecast for several basins to assess the water supply situation. Water managers need this forecast to plan water allocations for the following summer season. The Lodge Creek and Middle Creek basins, located in southeastern Alberta, are two basins that require this type of late winter forecast of potential spring runoff. Historically, the forecast has been based upon a combination of regression equations. These results are then interpreted by a forecaster and are modified based on the forecaster's heuristic knowledge of the basin. Unfortunately, this approach has had limited success in the past, in terms of the accuracy of these forecasts, and consequently an alternative methodology is needed.In this study, the applicability of fuzzy logic modelling techniques for forecasting water supply was investigated. Fuzzy logic has been applied successfully in several fields where the relationship between cause and effect (variable and results) are vague. Fuzzy variables were used to organize knowledge that is expressed linguistically into a formal analysis. For example, high snowpack, average snowpack and low snowpack became variables. By applying fuzzy logic, a water supply forecast was created that classified potential runoff into three forecast zones: low, average and high. Spring runoff forecasts from the fuzzy expert systems were found to be considerably more reliable than the regression models in forecasting the appropriate runoff zone, especially in terms of identifying low or average runoff years. Based on the modelling results in these two basins, it is concluded that fuzzy logic has a promising potential for providing reliable water supply forecasts. Copyright

  15. Fuzzy logic control of an AGV

    NASA Astrophysics Data System (ADS)

    Kelkar, Nikhal; Samu, Tayib; Hall, Ernest L.

    1997-09-01

    Automated guided vehicles (AGVs) have many potential applications in manufacturing, medicine, space and defense. The purpose of this paper is to describe exploratory research on the design of a modular autonomous mobile robot controller. The controller incorporates a fuzzy logic approach for steering and speed control, a neuro-fuzzy approach for ultrasound sensing (not discussed in this paper) and an overall expert system. The advantages of a modular system are related to portability and transportability, i.e. any vehicle can become autonomous with minimal modifications. A mobile robot test-bed has been constructed using a golf cart base. This cart has full speed control with guidance provided by a vision system and obstacle avoidance using ultrasonic sensors. The speed and steering fuzzy logic controller is supervised by a 486 computer through a multi-axis motion controller. The obstacle avoidance system is based on a micro-controller interfaced with six ultrasonic transducers. This micro- controller independently handles all timing and distance calculations and sends a steering angle correction back to the computer via the serial line. This design yields a portable independent system in which high speed computer communication is not necessary. Vision guidance is accomplished with a CCD camera with a zoom lens. The data is collected by a vision tracking device that transmits the X, Y coordinates of the lane marker to the control computer. Simulation and testing of these systems yielded promising results. This design, in its modularity, creates a portable autonomous fuzzy logic controller applicable to any mobile vehicle with only minor adaptations.

  16. Astronomical pipeline processing using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Shamir, Lior

    In the past few years, pipelines providing astronomical data have been becoming increasingly important. The wide use of robotic telescopes has provided significant discoveries, and sky survey projects such as SDSS and the future LSST are now considered among the premier projects in the field astronomy. The huge amount of data produced by these pipelines raises the need for automatic processing. Astronomical pipelines introduce several well-defined problems such as astronomical image compression, cosmic-ray hit rejection, transient detection, meteor triangulation and association of point sources with their corresponding known stellar objects. We developed and applied soft computing algorithms that provide new or improved solutions to these growing problems in the field of pipeline processing of astronomical data. One new approach that we use is fuzzy logic-based algorithms, which enables the automatic analysis of the astronomical pipelines and allows mining the data for not-yet-known astronomical discoveries such as optical transients and variable stars. The developed algorithms have been tested with excellent results on the NightSkyLive sky survey, which provides a pipeline of 150 astronomical pictures per hour, and covers almost the entire global night sky.

  17. 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.

  18. Designing a Software Tool for Fuzzy Logic Programming

    NASA Astrophysics Data System (ADS)

    Abietar, José M.; Morcillo, Pedro J.; Moreno, Ginés

    2007-12-01

    Fuzzy Logic Programming is an interesting and still growing research area that agglutinates the efforts for introducing fuzzy logic into logic programming (LP), in order to incorporate more expressive resources on such languages for dealing with uncertainty and approximated reasoning. The multi-adjoint logic programming approach is a recent and extremely flexible fuzzy logic paradigm for which, unfortunately, we have not found practical tools implemented so far. In this work, we describe a prototype system which is able to directly translate fuzzy logic programs into Prolog code in order to safely execute these residual programs inside any standard Prolog interpreter in a completely transparent way for the final user. We think that the development of such fuzzy languages and programing tools might play an important role in the design of advanced software applications for computational physics, chemistry, mathematics, medicine, industrial control and so on.

  19. Training Signaling Pathway Maps to Biochemical Data with Constrained Fuzzy Logic: Quantitative Analysis of Liver Cell Responses to Inflammatory Stimuli

    PubMed Central

    Morris, Melody K.; Saez-Rodriguez, Julio; Clarke, David C.; Sorger, Peter K.; Lauffenburger, Douglas A.

    2011-01-01

    Predictive understanding of cell signaling network operation based on general prior knowledge but consistent with empirical data in a specific environmental context is a current challenge in computational biology. Recent work has demonstrated that Boolean logic can be used to create context-specific network models by training proteomic pathway maps to dedicated biochemical data; however, the Boolean formalism is restricted to characterizing protein species as either fully active or inactive. To advance beyond this limitation, we propose a novel form of fuzzy logic sufficiently flexible to model quantitative data but also sufficiently simple to efficiently construct models by training pathway maps on dedicated experimental measurements. Our new approach, termed constrained fuzzy logic (cFL), converts a prior knowledge network (obtained from literature or interactome databases) into a computable model that describes graded values of protein activation across multiple pathways. We train a cFL-converted network to experimental data describing hepatocytic protein activation by inflammatory cytokines and demonstrate the application of the resultant trained models for three important purposes: (a) generating experimentally testable biological hypotheses concerning pathway crosstalk, (b) establishing capability for quantitative prediction of protein activity, and (c) prediction and understanding of the cytokine release phenotypic response. Our methodology systematically and quantitatively trains a protein pathway map summarizing curated literature to context-specific biochemical data. This process generates a computable model yielding successful prediction of new test data and offering biological insight into complex datasets that are difficult to fully analyze by intuition alone. PMID:21408212

  20. Correlation Between Miocene Global Climatic Changes (d18O) and Magnetic Properties, Using Neuro Fuzzy Logic Analysis

    NASA Astrophysics Data System (ADS)

    Costanzo, Vincenzo; da Silva, Ana; Hurtado, Nuri

    2010-05-01

    We have used the hybrid algorithm of neuro fuzzy logic (NFL), to establish a correlation between global climatic changes (benthic foraminiferal d18O data), experimental S-ratios and magnetic susceptibility (χ), in 44 samples of the Colombian stratigraphic well Saltarín 1A (Llanos foreland basin). χ and S-ratios were linked to global d18O data based on a constant accumulation rate for the stratigraphic interval flanked by the two age constrains available. A good inference (over 64%) is obtained using 4 fuzzy clusters or TKS type relationships. A stronger correlation is perhaps prevented by the likely influence of local and regional tectonic events and climatic changes that could have affected the Colombian Llanos foreland basin during Miocene times. For the Guayabo and León lithologies, it seems that the late diagenesis of the primary magnetic minerals and the assumption of a constant accumulation rate might have a minor influence on these results.

  1. Advanced PID type fuzzy logic power system stabilizer

    SciTech Connect

    Hiyama, Takashi; Kugimiya, Masahiko; Satoh, Hironori . Dept. of Electrical Engineering and Computer Science)

    1994-09-01

    An advanced fuzzy logic control scheme has been proposed for a micro-computer based power system stabilizer to enhance the overall stability of power systems. The proposed control scheme utilizes the PID information of the generator speed. The input signal to the stabilizer is the real power output of a study unit. Simulations show the effectiveness of the advanced fuzzy logic control scheme.

  2. The Influence of Fuzzy Logic Theory on Students' Achievement

    ERIC Educational Resources Information Center

    Semerci, Çetin

    2004-01-01

    As science and technology develop, the use's areas of Fuzzy Logic Theory develop too. Measurement and evaluation in education is one of these areas. The purpose of this research is to explain the influence of fuzzy logic theory on students' achievement. An experimental method is employed in the research. The traditional achievement marks and The…

  3. 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.

  4. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy logical relationships.

    PubMed

    Chen, Shyi-Ming; Chen, Shen-Wen

    2015-03-01

    In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and the probabilities of trends of fuzzy-trend logical relationships. Firstly, the proposed method fuzzifies the historical training data of the main factor and the secondary factor into fuzzy sets, respectively, to form two-factors second-order fuzzy logical relationships. Then, it groups the obtained two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, it calculates the probability of the "down-trend," the probability of the "equal-trend" and the probability of the "up-trend" of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group, respectively. Finally, it performs the forecasting based on the probabilities of the down-trend, the equal-trend, and the up-trend of the two-factors second-order fuzzy-trend logical relationships in each two-factors second-order fuzzy-trend logical relationship group. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) and the NTD/USD exchange rates. The experimental results show that the proposed method outperforms the existing methods.

  5. Combining fuzzy logic and eigenvector centrality measure in social network analysis

    NASA Astrophysics Data System (ADS)

    Parand, Fereshteh-Azadi; Rahimi, Hossein; Gorzin, Mohsen

    2016-10-01

    The rapid growth of social networks use has made a great platform to present different services, increasing beneficiary of services and business profit. Therefore considering different levels of member activities in these networks, finding highly active members who can have the influence on the choice and the role of other members of the community is one the most important and challenging issues in recent years. These nodes that usually have a high number of relations with a lot of quality interactions are called influential nodes. There are various types of methods and measures presented to find these nodes. Among all the measures, centrality is the one that identifies various types of influential nodes in a network. Here we define four different factors which affect the strength of a relationship. A fuzzy inference system calculates the strength of each relation, creates a crisp matrix in which the corresponding elements identify the strength of each relation, and using this matrix eigenvector measure calculates the most influential node. Applying our suggested method resulted in choosing a more realistic central node with consideration of the strength of all friendships.

  6. Active structural control by fuzzy logic rules: An introduction

    SciTech Connect

    Tang, Yu; Wu, Kung C.

    1996-12-31

    A zeroth level introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single- degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.

  7. Active structural control by fuzzy logic rules: An introduction

    SciTech Connect

    Tang, Y.

    1995-07-01

    An introduction to fuzzy logic control applied to the active structural control to reduce the dynamic response of structures subjected to earthquake excitations is presented. It is hoped that this presentation will increase the attractiveness of the methodology to structural engineers in research as well as in practice. The basic concept of the fuzzy logic control are explained by examples and by diagrams with a minimum of mathematics. The effectiveness and simplicity of the fuzzy logic control is demonstrated by a numerical example in which the response of a single-degree-of-freedom system subjected to earthquake excitations is controlled by making use of the fuzzy logic controller. In the example, the fuzzy rules are first learned from the results obtained from linear control theory; then they are fine tuned to improve their performance. It is shown that the performance of fuzzy logic control surpasses that of the linear control theory. The paper shows that linear control theory provides experience for fuzzy logic control, and fuzzy logic control can provide better performance; therefore, two controllers complement each other.

  8. Analysis of the noise reduction property of type-2 fuzzy logic systems using a novel type-2 membership function.

    PubMed

    Khanesar, Mojtaba Ahmadieh; Kayacan, Erdal; Teshnehlab, Mohammad; Kaynak, Okyay

    2011-10-01

    In this paper, the noise reduction property of type-2 fuzzy logic (FL) systems (FLSs) (T2FLSs) that use a novel type-2 fuzzy membership function is studied. The proposed type-2 membership function has certain values on both ends of the support and the kernel and some uncertain values for the other values of the support. The parameter tuning rules of a T2FLS that uses such a membership function are derived using the gradient descend learning algorithm. There exist a number of papers in the literature that claim that the performance of T2FLSs is better than type-1 FLSs under noisy conditions, and the claim is tried to be justified by simulation studies only for some specific systems. In this paper, a simpler T2FLS is considered with the novel membership function proposed in which the effect of input noise in the rule base is shown numerically in a general way. The proposed type-2 fuzzy neuro structure is tested on different input-output data sets, and it is shown that the T2FLS with the proposed novel membership function has better noise reduction property when compared to the type-1 counterparts.

  9. Security risk assessment: applying the concepts of fuzzy logic.

    PubMed

    Bajpai, Shailendra; Sachdeva, Anish; Gupta, J P

    2010-01-15

    Chemical process industries (CPI) handling hazardous chemicals in bulk can be attractive targets for deliberate adversarial actions by terrorists, criminals and disgruntled employees. It is therefore imperative to have comprehensive security risk management programme including effective security risk assessment techniques. In an earlier work, it has been shown that security risk assessment can be done by conducting threat and vulnerability analysis or by developing Security Risk Factor Table (SRFT). HAZOP type vulnerability assessment sheets can be developed that are scenario based. In SRFT model, important security risk bearing factors such as location, ownership, visibility, inventory, etc., have been used. In this paper, the earlier developed SRFT model has been modified using the concepts of fuzzy logic. In the modified SRFT model, two linguistic fuzzy scales (three-point and four-point) are devised based on trapezoidal fuzzy numbers. Human subjectivity of different experts associated with previous SRFT model is tackled by mapping their scores to the newly devised fuzzy scale. Finally, the fuzzy score thus obtained is defuzzyfied to get the results. A test case of a refinery is used to explain the method and compared with the earlier work.

  10. Control of a fluidized bed combustor using fuzzy logic

    SciTech Connect

    Koffman, S.J.; Brown, R.C.; Fullmer, R.R.

    1996-01-01

    Fuzzy logic--an artificial intelligence technique--can be employed to exploit the wealth of information human experts have learned about complex systems while attempting to control them. This information is usually of a qualitative nature that is unusable by rigid conventional control techniques. Fuzzy logic, uses as a control method, manipulates linguistically expressed, heuristic knowledge from a human expert to derive control actions for a described system. As an alternative approach to classical controls, fuzzy logic is examined for start-up control and normal regulation of a bubbling fluidized bed combustor. To validate the fuzzy logic approach, the fuzzy controller is compared to a classical proportional and integral (PI) controller, commonly used in industrial applications, designed by Ziegler-Nichols tuning.

  11. Landslide identification and classification by object-based image analysis and fuzzy logic: An example from the Azdavay region (Kastamonu, Turkey)

    NASA Astrophysics Data System (ADS)

    Aksoy, Beliz; Ercanoglu, Murat

    2012-01-01

    This study presents a data-driven and semiautomatic classification system carried out by object-based image analysis and fuzzy logic in a selected landslide-prone area in the Western Black Sea region of Turkey. In the first stage, a multiresolution segmentation process was performed using Landsat ETM+ satellite images of the study area. The model was established on 5235 image objects obtained by the segmentation process. A total of 70 landslide locations and 10 input parameters including normalized difference vegetation index, slope angle, curvature, brightness, mean band blue, asymmetry, shape index, length/width ratio, gray level co-occurrence matrix, and mean difference to infrared band were considered in the analyses. Membership functions were used to classify the study area by five fuzzy operators such as "and", "or", "mean arithmetic", "mean geometric", and "algebraic product". In order to assess the performances of the so-produced maps, 700 image objects, which were not used in the model, were taken into consideration. Based on the results, the map produced by "fuzzy and" operator performed better than those classified by the other fuzzy operators. The proposed methodology applied in this study may be useful for decision makers, local administrations, and scientists interested in landslides. It may also be useful in landslide-prone areas for planning, management, and regional development purposes.

  12. A simple fuzzy logic real-time camera tracking system

    NASA Technical Reports Server (NTRS)

    Magee, Kevin N.; Cheatham, John B., Jr.

    1993-01-01

    A fuzzy logic control of camera pan and tilt has been implemented to provide real-time camera tracking of a moving object. The user clicks a mouse button to identify the object that is to be tracked. A rapid centroid estimation algorithm is used to estimate the location of the moving object, and based on simple fuzzy membership functions, fuzzy x and y values are input into a six-rule fuzzy logic rule base. The output of this system is de-fuzzified to provide pan and tilt velocities required to keep the image of the object approximately centered in the camera field of view.

  13. Fruit Sorting Using Fuzzy Logic Techniques

    NASA Astrophysics Data System (ADS)

    Elamvazuthi, Irraivan; Sinnadurai, Rajendran; Aftab Ahmed Khan, Mohamed Khan; Vasant, Pandian

    2009-08-01

    Fruit and vegetables market is getting highly selective, requiring their suppliers to distribute the goods according to very strict standards of quality and presentation. In the last years, a number of fruit sorting and grading systems have appeared to fulfill the needs of the fruit processing industry. However, most of them are overly complex and too costly for the small and medium scale industry (SMIs) in Malaysia. In order to address these shortcomings, a prototype machine was developed by integrating the fruit sorting, labeling and packing processes. To realise the prototype, many design issues were dealt with. Special attention is paid to the electronic weighing sub-system for measuring weight, and the opto-electronic sub-system for determining the height and width of the fruits. Specifically, this paper discusses the application of fuzzy logic techniques in the sorting process.

  14. Fuzzy Logic Enhanced Digital PIV Processing Software

    NASA Technical Reports Server (NTRS)

    Wernet, Mark P.

    1999-01-01

    Digital Particle Image Velocimetry (DPIV) is an instantaneous, planar velocity measurement technique that is ideally suited for studying transient flow phenomena in high speed turbomachinery. DPIV is being actively used at the NASA Glenn Research Center to study both stable and unstable operating conditions in a high speed centrifugal compressor. Commercial PIV systems are readily available which provide near real time feedback of the PIV image data quality. These commercial systems are well designed to facilitate the expedient acquisition of PIV image data. However, as with any general purpose system, these commercial PIV systems do not meet all of the data processing needs required for PIV image data reduction in our compressor research program. An in-house PIV PROCessing (PIVPROC) code has been developed for reducing PIV data. The PIVPROC software incorporates fuzzy logic data validation for maximum information recovery from PIV image data. PIVPROC enables combined cross-correlation/particle tracking wherein the highest possible spatial resolution velocity measurements are obtained.

  15. 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…

  16. 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.

  17. An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller

    ERIC Educational Resources Information Center

    Mamdani, E. H.; Assilian, S.

    1975-01-01

    This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. (Author)

  18. An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller

    ERIC Educational Resources Information Center

    Mamdani, E. H.; Assilian, S.

    1975-01-01

    This paper describes an experiment on the "linguistic" synthesis of a controller for a model industrial plant (a steam engine). Fuzzy logic is used to convert heuristic control rules stated by a human operator into an automatic control strategy. (Author)

  19. Organizational coevolutionary classifiers with fuzzy logic used in intrusion detection

    NASA Astrophysics Data System (ADS)

    Chen, Zhenguo

    2009-07-01

    Intrusion detection is an important technique in the defense-in-depth network security framework and a hot topic in computer security in recent years. To solve the intrusion detection question, we introduce the fuzzy logic into Organization CoEvolutionary algorithm [1] and present the algorithm of Organization CoEvolutionary Classification with Fuzzy Logic. In this paper, we give an intrusion detection models based on Organization CoEvolutionary Classification with Fuzzy Logic. After illustrating our model with a representative dataset and applying it to the real-world network datasets KDD Cup 1999. The experimental result shown that the intrusion detection based on Organizational Coevolutionary Classifiers with Fuzzy Logic can give higher recognition accuracy than the general method.

  20. Fuzzy logic based ELF magnetic field estimation in substations.

    PubMed

    Kosalay, Ilhan

    2008-01-01

    This paper examines estimation of the extremely low frequency magnetic fields (MF) in the power substation. First, the results of the previous relevant research studies and the MF measurements in a sample power substation are presented. Then, a fuzzy logic model based on the geometric definitions in order to estimate the MF distribution is explained. Visual software, which has a three-dimensional screening unit, based on the fuzzy logic technique, has been developed.

  1. Fuzzy logic controllers: A knowledge-based system perspective

    NASA Technical Reports Server (NTRS)

    Bonissone, Piero P.

    1993-01-01

    Over the last few years we have seen an increasing number of applications of Fuzzy Logic Controllers. These applications range from the development of auto-focus cameras, to the control of subway trains, cranes, automobile subsystems (automatic transmissions), domestic appliances, and various consumer electronic products. In summary, we consider a Fuzzy Logic Controller to be a high level language with its local semantics, interpreter, and compiler, which enables us to quickly synthesize non-linear controllers for dynamic systems.

  2. Rocket engine system reliability analyses using probabilistic and fuzzy logic techniques

    NASA Technical Reports Server (NTRS)

    Hardy, Terry L.; Rapp, Douglas C.

    1994-01-01

    The reliability of rocket engine systems was analyzed by using probabilistic and fuzzy logic techniques. Fault trees were developed for integrated modular engine (IME) and discrete engine systems, and then were used with the two techniques to quantify reliability. The IRRAS (Integrated Reliability and Risk Analysis System) computer code, developed for the U.S. Nuclear Regulatory Commission, was used for the probabilistic analyses, and FUZZYFTA (Fuzzy Fault Tree Analysis), a code developed at NASA Lewis Research Center, was used for the fuzzy logic analyses. Although both techniques provided estimates of the reliability of the IME and discrete systems, probabilistic techniques emphasized uncertainty resulting from randomness in the system whereas fuzzy logic techniques emphasized uncertainty resulting from vagueness in the system. Because uncertainty can have both random and vague components, both techniques were found to be useful tools in the analysis of rocket engine system reliability.

  3. A Priority Fuzzy Logic Extension of the XQuery Language

    NASA Astrophysics Data System (ADS)

    Škrbić, Srdjan; Wettayaprasit, Wiphada; Saeueng, Pannipa

    2011-09-01

    In recent years there have been significant research findings in flexible XML querying techniques using fuzzy set theory. Many types of fuzzy extensions to XML data model and XML query languages have been proposed. In this paper, we introduce priority fuzzy logic extensions to XQuery language. Describing these extensions we introduce a new query language. Moreover, we describe a way to implement an interpreter for this language using an existing XML native database.

  4. A new way of predicting cement strength -- Fuzzy logic

    SciTech Connect

    Gao Faliang

    1997-06-01

    This paper is to analyze the fuzzy logic method of predicting cement strength and to calculate some samples with fuzzy models. In order to compare, samples of them are calculated with regression method. All of results are shown in both root mean square error and scattered map.

  5. Fuzzy logic structure analysis of trabecular bone of the calcaneus to estimate proximal femur fracture load and discriminate subjects with and without vertebral fractures using high-resolution magnetic resonance imaging at 1.5 T and 3 T.

    PubMed

    Patel, Priyesh V; Eckstein, Felix; Carballido-Gamio, Julio; Phan, Catherine; Matsuura, Maiko; Lochmüller, Eva-Maria; Majumdar, Sharmila; Link, Thomas M

    2007-10-01

    Newly developed fuzzy logic-derived structural parameters were used to characterize trabecular bone architecture in high-resolution magnetic resonance imaging (HR-MRI) of human cadaver calcaneus specimens. These parameters were compared to standard histomorphological structural measures and analyzed concerning performance in discriminating vertebral fracture status and estimating proximal femur fracture load. Sets of 60 sagittal 1.5 T and 3.0 T HR-MRI images of the calcaneus were obtained in 39 cadavers using a fast gradient recalled echo sequence. Structural parameters equivalent to bone histomorphometry and fuzzy logic-derived parameters were calculated using two chosen regions of interest. Calcaneal, spine, and hip bone mineral density (BMD) measurements were also obtained. Fracture status of the thoracic and lumbar spine was assessed on lateral radiographs. Finally, mechanical strength testing of the proximal femur was performed. Diagnostic performance in discriminating vertebral fracture status and estimating femoral fracture load was calculated using regression analyses, two-tailed t-tests of significance, and receiver operating characteristic (ROC) analyses. Significant correlations were obtained at both field strengths between all structural and fuzzy logic parameters (r up to 0.92). Correlations between histomorphological or fuzzy logic parameters and calcaneal BMD were mostly significant (r up to 0.78). ROC analyses demonstrated that standard structural parameters were able to differentiate persons with and without vertebral fractures (area under the curve [A(Z)] up to 0.73). However, none of the parameters obtained in the 1.5-T images and none of the fuzzy logic parameters discriminated persons with and without vertebral fractures. Significant correlations were found between fuzzy or structural parameters and femoral fracture load. Using multiple regression analysis, none of the structural or fuzzy parameters were found to add discriminative value to BMD

  6. Application of fuzzy logic in automated cow status monitoring.

    PubMed

    de Mol, R M; Woldt, W E

    2001-02-01

    Sensors that measure yield, temperature, electrical conductivity of milk, and animal activity can be used for automated cow status monitoring. The occurrence of false-positive alerts, generated by a detection model, creates problems in practice. We used fuzzy logic to classify mastitis and estrus alerts; our objective was to reduce the number of false-positive alerts and not to change the level of detected cases of mastitis and estrus. Inputs for the fuzzy logic model were alerts from the detection model and additional information, such as the reproductive status. The output was a classification, true or false, of each alert. Only alerts that were classified true should be presented to the herd manager. Additional information was used to check whether deviating sensor measurements were caused by mastitis or estrus, or by other influences. A fuzzy logic model for the classification of mastitis alerts was tested on a data set from cows milked in an automatic milking system. All clinical cases without measurement errors were classified correctly. The number of false-positive alerts over time from a subset of 25 cows was reduced from 1266 to 64 by applying the fuzzy logic model. A fuzzy logic model for the classification of estrus alerts was tested on two data sets. The number of detected cases decreased slightly after classification, and the number of false-positive alerts decreased considerably. Classification by a fuzzy logic model proved to be very useful in increasing the applicability of automated cow status monitoring.

  7. Development of a self-tuning fuzzy logic controller

    SciTech Connect

    Huang, S.H.; Nelson, R.M.

    1999-07-01

    To avoid the laborious task of modifying control rule sets for fuzzy logic controllers, a novel model-based self-tuning strategy has been developed. The performance of this advanced fuzzy logic controller is measured and analyzed in a linguistic plane. An optimal performance trajectory functions as the control model. The self-tuning strategy improves the performance automatically until it converges to a predetermined optimal global criterion. The experimental results indicate that the actual performance trajectory of the advanced fuzzy controller with the self-tuning strategy has reached the optimal criterion.

  8. Parallel optical fuzzy logic inference using improved fuzzy associative memories

    NASA Astrophysics Data System (ADS)

    Zhang, ShuQun; Karim, Mohammad A.

    1999-10-01

    A new optoelectronic fuzzy inference system is proposed for processing a large number of fuzzy rules in parallel. The proposed system using spatial light modulator implements various membership functions as well as max-min inference. It has the features of easy implementation and large data processing capability. The membership function decomposition method in the improved fuzzy associative memory is used to save both space bandwidth and accommodate multiple-input fuzzy inference.

  9. A Fuzzy Description Logic with Automatic Object Membership Measurement

    NASA Astrophysics Data System (ADS)

    Cai, Yi; Leung, Ho-Fung

    In this paper, we propose a fuzzy description logic named f om -DL by combining the classical view in cognitive psychology and fuzzy set theory. A formal mechanism used to determine object memberships automatically in concepts is also proposed, which is lacked in previous work fuzzy description logics. In this mechanism, object membership is based on the defining properties of concept definition and properties in object description. Moreover, while previous works cannot express the qualitative measurements of an object possessing a property, we introduce two kinds of properties named N-property and L-property, which are quantitative measurements and qualitative measurements of an object possessing a property respectively. The subsumption and implication of concepts and properties are also explored in our work. We believe that it is useful to the Semantic Web community for reasoning the fuzzy membership of objects for concepts in fuzzy ontologies.

  10. Fuzzy-logic-assisted surgical planning in adolescent idiopathic scoliosis.

    PubMed

    Nault, Marie-Lyne; Labelle, Hubert; Aubin, Carl-Eric; Sangole, Archana; Balazinski, Marek

    2009-06-01

    Selection of appropriate curve fusion levels for surgery in adolescent idiopathic scoliosis (AIS) is a complex and difficult task and, despite numerous publications, still remains a highly controversial topic. To evaluate a fuzzy-logic-based surgical planning tool by comparing the results suggested by the software with the average outcome recommended by a panel of 5 expert spinal deformity surgeons. It is hypothesized that, given the same information, the fuzzy-logic tool will perform as favorably as the surgeons. Proof-of-concept study evaluating the use of a fuzzy-logic-assisted surgical planning tool in AIS to select the appropriate spinal curve to be instrumented. A cohort of 30 AIS surgical cases with a main thoracic curve was used. Each case included standard measurements recorded from preoperative standing postero-anterior and lateral, supine side bending, and 1-year postoperative standing radiographs. Five experienced spinal deformity surgeons evaluated each case independently and gave their preferred levels of instrumentation and fusion. The cases were then presented to the fuzzy-logic tool to determine whether the high thoracic and/or lumbar curves were to be instrumented. For each case, a percentage value was obtained indicating inclusion/exclusion of the respective curves in the surgical instrumentation procedure. Kappa statistics was used to compare the model output and the average decision of the surgeons. Kappa values of 0.71 and 0.64 were obtained, respectively, for the proximal thoracic and lumbar curves models, thus suggesting a good agreement of the fusion recommendations made by the fuzzy-logic tool and the surgeons. Given the same information, the fuzzy-logic-assisted recommendation of the curve to be instrumented compared favorably with the collective decision of the surgeons. The findings thus suggest that a fuzzy-logic approach is helpful in assisting surgeons with the preoperative selection of curve instrumentation and fusion levels in AIS.

  11. Performance Analysis of Extracted Rule-Base Multivariable Type-2 Self-Organizing Fuzzy Logic Controller Applied to Anesthesia

    PubMed Central

    Fan, Shou-Zen; Shieh, Jiann-Shing

    2014-01-01

    We compare type-1 and type-2 self-organizing fuzzy logic controller (SOFLC) using expert initialized and pretrained extracted rule-bases applied to automatic control of anaesthesia during surgery. We perform experimental simulations using a nonfixed patient model and signal noise to account for environmental and patient drug interaction uncertainties. The simulations evaluate the performance of the SOFLCs in their ability to control anesthetic delivery rates for maintaining desired physiological set points for muscle relaxation and blood pressure during a multistage surgical procedure. The performances of the SOFLCs are evaluated by measuring the steady state errors and control stabilities which indicate the accuracy and precision of control task. Two sets of comparisons based on using expert derived and extracted rule-bases are implemented as Wilcoxon signed-rank tests. Results indicate that type-2 SOFLCs outperform type-1 SOFLC while handling the various sources of uncertainties. SOFLCs using the extracted rules are also shown to outperform those using expert derived rules in terms of improved control stability. PMID:25587533

  12. Life insurance risk assessment using a fuzzy logic expert system

    NASA Technical Reports Server (NTRS)

    Carreno, Luis A.; Steel, Roy A.

    1992-01-01

    In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.

  13. Life insurance risk assessment using a fuzzy logic expert system

    NASA Technical Reports Server (NTRS)

    Carreno, Luis A.; Steel, Roy A.

    1992-01-01

    In this paper, we present a knowledge based system that combines fuzzy processing with rule-based processing to form an improved decision aid for evaluating risk for life insurance. This application illustrates the use of FuzzyCLIPS to build a knowledge based decision support system possessing fuzzy components to improve user interactions and KBS performance. The results employing FuzzyCLIPS are compared with the results obtained from the solution of the problem using traditional numerical equations. The design of the fuzzy solution consists of a CLIPS rule-based system for some factors combined with fuzzy logic rules for others. This paper describes the problem, proposes a solution, presents the results, and provides a sample output of the software product.

  14. Navigating a Mobile Robot Across Terrain Using Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Seraji, Homayoun; Howard, Ayanna; Bon, Bruce

    2003-01-01

    A strategy for autonomous navigation of a robotic vehicle across hazardous terrain involves the use of a measure of traversability of terrain within a fuzzy-logic conceptual framework. This navigation strategy requires no a priori information about the environment. Fuzzy logic was selected as a basic element of this strategy because it provides a formal methodology for representing and implementing a human driver s heuristic knowledge and operational experience. Within a fuzzy-logic framework, the attributes of human reasoning and decision- making can be formulated by simple IF (antecedent), THEN (consequent) rules coupled with easily understandable and natural linguistic representations. The linguistic values in the rule antecedents convey the imprecision associated with measurements taken by sensors onboard a mobile robot, while the linguistic values in the rule consequents represent the vagueness inherent in the reasoning processes to generate the control actions. The operational strategies of the human expert driver can be transferred, via fuzzy logic, to a robot-navigation strategy in the form of a set of simple conditional statements composed of linguistic variables. These linguistic variables are defined by fuzzy sets in accordance with user-defined membership functions. The main advantages of a fuzzy navigation strategy lie in the ability to extract heuristic rules from human experience and to obviate the need for an analytical model of the robot navigation process.

  15. Fuzzy logic, neural networks, and soft computing

    NASA Technical Reports Server (NTRS)

    Zadeh, Lofti A.

    1994-01-01

    The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

  16. Fuzzy logic, neural networks, and soft computing

    NASA Technical Reports Server (NTRS)

    Zadeh, Lofti A.

    1994-01-01

    The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

  17. Fuzzy logic-based prognostic score for outcome prediction in esophageal cancer.

    PubMed

    Wang, Chang-Yu; Lee, Tsair-Fwu; Fang, Chun-Hsiung; Chou, Jyh-Horng

    2012-11-01

    Given the poor prognosis of esophageal cancer and the invasiveness of combined modality treatment, improved prognostic scoring systems are needed. We developed a fuzzy logic-based system to improve the predictive performance of a risk score based on the serum concentrations of C-reactive protein (CRP) and albumin in a cohort of 271 patients with esophageal cancer before radiotherapy. Univariate and multivariate survival analyses were employed to validate the independent prognostic value of the fuzzy risk score. To further compare the predictive performance of the fuzzy risk score with other prognostic scoring systems, time-dependent receiver operating characteristic curve (ROC) analysis was used. Application of fuzzy logic to the serum values of CRP and albumin increased predictive performance for 1-year overall survival (AUC=0.773) compared with that of a single marker (AUC=0.743 and 0.700 for CRP and albumin, respectively), where the AUC denotes the area under curve. This fuzzy logic-based approach also performed consistently better than the Glasgow Prognostic Score (GPS) (AUC=0.745). Thus, application of fuzzy logic to the analysis of serum markers can more accurately predict the outcome for patients with esophageal cancer.

  18. Uncertainty analysis of a combined Artificial Neural Network - Fuzzy logic - Kriging system for spatial and temporal simulation of Hydraulic Head.

    NASA Astrophysics Data System (ADS)

    Tapoglou, Evdokia; Karatzas, George P.; Trichakis, Ioannis C.; Varouchakis, Emmanouil A.

    2015-04-01

    The purpose of this study is to evaluate the uncertainty, using various methodologies, in a combined Artificial Neural Network (ANN) - Fuzzy logic - Kriging system, which can simulate spatially and temporally the hydraulic head in an aquifer. This system uses ANNs for the temporal prediction of hydraulic head in various locations, one ANN for every location with available data, and Kriging for the spatial interpolation of ANN's results. A fuzzy logic is used for the interconnection of these two methodologies. The full description of the initial system and its functionality can be found in Tapoglou et al. (2014). Two methodologies were used for the calculation of uncertainty for the implementation of the algorithm in a study area. First, the uncertainty of Kriging parameters was examined using a Bayesian bootstrap methodology. In this case the variogram is calculated first using the traditional methodology of Ordinary Kriging. Using the parameters derived and the covariance function of the model, the covariance matrix is constructed. A common method for testing a statistical model is the use of artificial data. Normal random numbers generation is the first step in this procedure and by multiplying them by the decomposed covariance matrix, correlated random numbers (sample set) can be calculated. These random values are then fitted into a variogram and the value in an unknown location is estimated using Kriging. The distribution of the simulated values using the Kriging of different correlated random values can be used in order to derive the prediction intervals of the process. In this study 500 variograms were constructed for every time step and prediction point, using the method described above, and their results are presented as the 95th and 5th percentile of the predictions. The second methodology involved the uncertainty of ANNs training. In this case, for all the data points 300 different trainings were implemented having different training datasets each time

  19. Fuzzy logic control and optimization system

    DOEpatents

    Lou, Xinsheng [West Hartford, CT

    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.

  20. Fuzzy logic applications to expert systems and control

    NASA Technical Reports Server (NTRS)

    Lea, Robert N.; Jani, Yashvant

    1991-01-01

    A considerable amount of work on the development of fuzzy logic algorithms and application to space related control problems has been done at the Johnson Space Center (JSC) over the past few years. Particularly, guidance control systems for space vehicles during proximity operations, learning systems utilizing neural networks, control of data processing during rendezvous navigation, collision avoidance algorithms, camera tracking controllers, and tether controllers have been developed utilizing fuzzy logic technology. Several other areas in which fuzzy sets and related concepts are being considered at JSC are diagnostic systems, control of robot arms, pattern recognition, and image processing. It has become evident, based on the commercial applications of fuzzy technology in Japan and China during the last few years, that this technology should be exploited by the government as well as private industry for energy savings.

  1. Experiments on neural network architectures for fuzzy logic

    NASA Technical Reports Server (NTRS)

    Keller, James M.

    1991-01-01

    The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

  2. Completed Optimised Structure of Threonine Molecule by Fuzzy Logic Modelling

    NASA Astrophysics Data System (ADS)

    Sahiner, Ahmet; Ucun, Fatih; Kapusuz, Gulden; Yilmaz, Nurullah

    2016-04-01

    In this study we applied the fuzzy logic approach in order to model the energy depending on the two torsion angles for the threonine (C4H9NO3) molecule. The model is set up according to theoretical results obtained by the density functional theory (B3LYP) with a 6-31 G(d) basic set on a Gausian program. We aimed to determine the best torsion angle values providing the energy of the molecule minimum by a fuzzy logic approach and to compare them with the density functional theory results. It was concluded that the fuzzy logic approach gives information about the untested data and its best value which are expensive and time-consuming to obtain by other methods and experimentation.

  3. Design and performance comparison of fuzzy logic based tracking controllers

    NASA Technical Reports Server (NTRS)

    Lea, Robert N.; Jani, Yashvant

    1992-01-01

    Several camera tracking controllers based on fuzzy logic principles have been designed and tested in software simulation in the software technology branch at the Johnson Space Center. The fuzzy logic based controllers utilize range measurement and pixel positions from the image as input parameters and provide pan and tilt gimble rate commands as output. Two designs of the rulebase and tuning process applied to the membership functions are discussed in light of optimizing performance. Seven test cases have been designed to test the performance of the controllers for proximity operations where approaches like v-bar, fly-around and station keeping are performed. The controllers are compared in terms of responsiveness, and ability to maintain the object in the field-of-view of the camera. Advantages of the fuzzy logic approach with respect to the conventional approach have been discussed in terms of simplicity and robustness.

  4. Applied intelligent systems: blending fuzzy logic with conventional control

    NASA Astrophysics Data System (ADS)

    Filev, Dimitar; Syed, Fazal U.

    2010-05-01

    The aim of this paper is to show that design of applied intelligent control systems requires different types of blending between fuzzy logic and conventional control systems. Two alternative automotive applications - a manufacturing process control problem and an advisory system for fuel efficient driving - that benefit from both fuzzy and control theories are reviewed and different levels of prioritisations of both approaches are discussed based on the specificity of the applications.

  5. Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories

    NASA Technical Reports Server (NTRS)

    Burchett, Bradley T.

    2003-01-01

    The problem of designing and flying a trajectory for successful recovery of a reusable launch vehicle is tackled using fuzzy logic control with genetic algorithm optimization. The plant is approximated by a simplified three degree of freedom non-linear model. A baseline trajectory design and guidance algorithm consisting of several Mamdani type fuzzy controllers is tuned using a simple genetic algorithm. Preliminary results show that the performance of the overall system is shown to improve with genetic algorithm tuning.

  6. An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems.

    PubMed

    Uzoka, Faith-Michael Emeka; Obot, Okure; Barker, Ken; Osuji, J

    2011-07-01

    The task of medical diagnosis is a complex one, considering the level vagueness and uncertainty management, especially when the disease has multiple symptoms. A number of researchers have utilized the fuzzy-analytic hierarchy process (fuzzy-AHP) methodology in handling imprecise data in medical diagnosis and therapy. The fuzzy logic is able to handle vagueness and unstructuredness in decision making, while the AHP has the ability to carry out pairwise comparison of decision elements in order to determine their importance in the decision process. This study attempts to do a case comparison of the fuzzy and AHP methods in the development of medical diagnosis system, which involves basic symptoms elicitation and analysis. The results of the study indicate a non-statistically significant relative superiority of the fuzzy technology over the AHP technology. Data collected from 30 malaria patients were used to diagnose using AHP and fuzzy logic independent of one another. The results were compared and found to covary strongly. It was also discovered from the results of fuzzy logic diagnosis covary a little bit more strongly to the conventional diagnosis results than that of AHP. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

  7. Analysis of land suitability for urban development in Ahwaz County in southwestern Iran using fuzzy logic and analytic network process (ANP).

    PubMed

    Malmir, Maryam; Zarkesh, Mir Masoud Kheirkhah; Monavari, Seyed Masoud; Jozi, Seyed Ali; Sharifi, Esmail

    2016-08-01

    The ever-increasing development of cities due to population growth and migration has led to unplanned constructions and great changes in urban spatial structure, especially the physical development of cities in unsuitable places, which requires conscious guidance and fundamental organization. It is therefore necessary to identify suitable sites for future development of cities and prevent urban sprawl as one of the main concerns of urban managers and planners. In this study, to determine the suitable sites for urban development in the county of Ahwaz, the effective biophysical and socioeconomic criteria (including 27 sub-criteria) were initially determined based on literature review and interviews with certified experts. In the next step, a database of criteria and sub-criteria was prepared. Standardization of values and unification of scales in map layers were done using fuzzy logic. The criteria and sub-criteria were weighted by analytic network process (ANP) in the Super Decision software. Next, the map layers were overlaid using weighted linear combination (WLC) in the GIS software. According to the research findings, the final land suitability map was prepared with five suitability classes of very high (5.86 %), high (31.93 %), medium (38.61 %), low (17.65 %), and very low (5.95 %). Also, in terms of spatial distribution, suitable lands for urban development are mainly located in the central and southern parts of the Ahwaz County. It is expected that integration of fuzzy logic and ANP model will provide a better decision support tool compared with other models. The developed model can also be used in the land suitability analysis of other cities.

  8. Applications of fuzzy logic to control and decision making

    NASA Technical Reports Server (NTRS)

    Lea, Robert N.; Jani, Yashvant

    1991-01-01

    Long range space missions will require high operational efficiency as well as autonomy to enhance the effectivity of performance. Fuzzy logic technology has been shown to be powerful and robust in interpreting imprecise measurements and generating appropriate control decisions for many space operations. Several applications are underway, studying the fuzzy logic approach to solving control and decision making problems. Fuzzy logic algorithms for relative motion and attitude control have been developed and demonstrated for proximity operations. Based on this experience, motion control algorithms that include obstacle avoidance were developed for a Mars Rover prototype for maneuvering during the sample collection process. A concept of an intelligent sensor system that can identify objects and track them continuously and learn from its environment is under development to support traffic management and proximity operations around the Space Station Freedom. For safe and reliable operation of Lunar/Mars based crew quarters, high speed controllers with ability to combine imprecise measurements from several sensors is required. A fuzzy logic approach that uses high speed fuzzy hardware chips is being studied.

  9. Coordinated signal control for arterial intersections using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Kermanian, Davood; Zare, Assef; Balochian, Saeed

    2013-09-01

    Every day growth of the vehicles has become one of the biggest problems of urbanism especially in major cities. This can waste people's time, increase the fuel consumption, air pollution, and increase the density of cars and vehicles. Fuzzy controllers have been widely used in many consumer products and industrial applications with success over the past two decades. This article proposes a comprehensive model of urban traffic network using state space equations and then using Fuzzy Logic Tool Box and SIMULINK Program MATLAB a fuzzy controller in order to optimize and coordinate signal control at two intersections at an arterial road. The fuzzy controller decides to extend, early cut or terminate a signal phase and phase sequence to ensure smooth flow of traffic with minimal waiting time and length of queue. Results show that the performance of the proposed traffic controller at novel fuzzy model is better that of conventional controllers under normal and abnormal traffic conditions.

  10. Performance evaluation of the distance education system with fuzzy logic

    NASA Astrophysics Data System (ADS)

    Armaǧan, Hamit; Yiǧit, Tuncay

    2017-07-01

    Distance education is a kind of education that brought together course advisor, student and educational materials in a different time and place through communicational technologies. In this educational system the success of education is directly related to audio, video and interaction. In this study, a model is created by using fuzzy logic with the success of distance education students and the components of distance education. This study is made by MATLAB fuzzy logic toolbox. Audio, video, educational technology, student achievement are used as parameters in the evaluation. System assessment is carried out depending on parameter.

  11. Fuzzy Logic as a Tool for Assessing Students' Knowledge and Skills

    ERIC Educational Resources Information Center

    Voskoglou, Michael Gr.

    2013-01-01

    Fuzzy logic, which is based on fuzzy sets theory introduced by Zadeh in 1965, provides a rich and meaningful addition to standard logic. The applications which may be generated from or adapted to fuzzy logic are wide-ranging and provide the opportunity for modeling under conditions which are imprecisely defined. In this article we develop a fuzzy…

  12. Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia.

    PubMed

    Mansoor Baig, Mirza; Gholamhosseini, Hamid; Harrison, Michael J

    2013-07-01

    Anaesthesia monitoring involves critical diagnostic tasks carried out amongst lots of distractions. Computers are capable of handling large amounts of data at high speed and therefore decision support systems and expert systems are now capable of processing many signals simultaneously in real time. We have developed two fuzzy logic based anaesthesia monitoring systems; a real time smart anaesthesia alarm system (RT-SAAM) and fuzzy logic monitoring system-2 (FLMS-2), an updated version of FLMS for the detection of absolute hypovolaemia. This paper presents the design aspects of these two systems which employ fuzzy logic techniques to detect absolute hypovolaemia, and compares their performances in terms of usability and acceptability. The interpretation of these two systems of absolute hypovolaemia was compared with clinicians' assessments using Kappa analysis, RT-SAAM K=0.62, FLMS-2 K=0.75; an improvement in performance by FLMS-2. Copyright © 2013 Elsevier Ltd. All rights reserved.

  13. Postoperative vomiting in pediatric oncologic patients: prediction by a fuzzy logic model.

    PubMed

    Bassanezi, Betina S B; de Oliveira-Filho, Antônio G; Jafelice, Rosana S M; Bustorff-Silva, Joaquim M; Udelsmann, Artur

    2013-01-01

    To report a fuzzy logic mathematical model to predict postoperative vomiting (POV) in pediatric oncologic patients and compare with preexisting scores. Although POV has a high incidence in children and may decrease parental satisfaction after surgeries, there is only one specific score that predicts POV in children: the Eberhart's score. In this study, we report a fuzzy model that intends to predict the probability of POV in pediatric oncologic patients. Fuzzy logic is a mathematical theory that recognizes more than simple true and false values and takes into account levels of continuous variables such as age or duration of the surgery. The fuzzy model tries to account for subjectiveness in the variables. Preoperative potential risk factors for POV in 198 children (0-19 year old) with malignancies were collected and analyzed. Data analysis was performed with the chi-square test and logistic regression to evaluate probable risk factors for POV. A system based on fuzzy logic was developed with the risk factors found in the logistic regression, and a computational interface was created to calculate the probability of POV. The model showed a good performance in predicting POV. After the analysis, the model was compared with Eberhart's score in the same population and showed a better performance. The fuzzy score can predict the chance of POV in children with cancer with good accuracy, allowing better planning for postoperative prophylaxis of vomiting. The computational interface is available for free download at the internet and is very easy to use. © 2012 Blackwell Publishing Ltd.

  14. Fuzzy Logic Based Rotor Health Index of Induction Motor

    NASA Astrophysics Data System (ADS)

    Misra, Rajul; Pahuja, G. L.

    2015-10-01

    This paper presents an experimental study on detection and diagnosis of broken rotor bars in Squirrel Cage Induction Motor (SQIM). The proposed scheme is based on Motor Current Signature Analysis (MCSA) which uses amplitude difference of supply frequency to upper and lower side bands. Initially traditional MCSA has been used for rotor fault detection. It provides rotor health index on full load conditions. However in real practice if a fault occurs motor can not run at full load. To overcome the issue of reduced load condition a Fuzzy Logic based MCSA has been designed, implemented, tested and compared with traditional MCSA. A simulation result shows that proposed scheme is not only capable of detecting the severity of rotor fault but also provides remarkable performance at reduced load conditions.

  15. Fuzzy logic for identifying pigments studied by Raman spectroscopy.

    PubMed

    Ramos, Pablo Manuel; Ferré, Joan; Ruisánchez, Itziar; Andrikopoulos, Konstantinos S

    2004-07-01

    Fuzzy logic and linguistic variables are used for the automatic interpretation of Raman spectra obtained from pigments found in cultural heritage art objects. Featured bands are extracted from a Raman spectrum of a reference pigment and the methodology for constructing the library is illustrated. An unknown spectrum is then interpreted automatically and a process for identifying the corresponding pigment is described. A reference library consisting of 32 pigments was built and the effectiveness of the algorithm was tested by the Raman spectroscopic analysis of 10 pigments that are known to have been extensively used in Byzantine hagiography. Binary mixtures of these pigments were also tested. The algorithm's level of identification was good even though extra peaks, noise, and background signals were encountered in the spectra.

  16. Adaptive Process Control with Fuzzy Logic and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision-making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, an analysis element to recognize changes in the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  17. Forest fire autonomous decision system based on fuzzy logic

    NASA Astrophysics Data System (ADS)

    Lei, Z.; Lu, Jianhua

    2010-11-01

    The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.

  18. Forest fire autonomous decision system based on fuzzy logic

    NASA Astrophysics Data System (ADS)

    Lei, Z.; Lu, Jianhua

    2009-09-01

    The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.

  19. Fuzzy-logic-based safety verification framework for nuclear power plants.

    PubMed

    Rastogi, Achint; Gabbar, Hossam A

    2013-06-01

    This article presents a practical implementation of a safety verification framework for nuclear power plants (NPPs) based on fuzzy logic where hazard scenarios are identified in view of safety and control limits in different plant process values. Risk is estimated quantitatively and compared with safety limits in real time so that safety verification can be achieved. Fuzzy logic is used to define safety rules that map hazard condition with required safety protection in view of risk estimate. Case studies are analyzed from NPP to realize the proposed real-time safety verification framework. An automated system is developed to demonstrate the safety limit for different hazard scenarios. © 2012 Society for Risk Analysis.

  20. Computer vision for general purpose visual inspection: a fuzzy logic approach

    NASA Astrophysics Data System (ADS)

    Chen, Y. H.

    In automatic visual industrial inspection, computer vision systems have been widely used. Such systems are often application specific, and therefore require domain knowledge in order to have a successful implementation. Since visual inspection can be viewed as a decision making process, it is argued that the integration of fuzzy logic analysis and computer vision systems provides a practical approach to general purpose visual inspection applications. This paper describes the development of an integrated fuzzy-rule-based automatic visual inspection system. Domain knowledge about a particular application is represented as a set of fuzzy rules. From the status of predefined fuzzy variables, the set of fuzzy rules are defuzzified to give the inspection results. A practical application where IC marks (often in the forms of English characters and a company logo) inspection is demonstrated, which shows a more consistent result as compared to a conventional thresholding method.

  1. Automated sensory nerve conduction testing using fuzzy logic.

    PubMed

    Gitter, A; Lin, V

    1999-01-01

    Nerve conduction studies continue to be an important tool in the evaluation of peripheral nerve disorders but have come under increased scrutiny because of heightened cost control in health care service delivery. In selected clinical settings, automated nerve conduction studies may be a useful clinical tool replacing conventional testing, but existing instruments are limited and have not generally been accepted into clinical practice. Further advancements in nerve conduction automation may be possible by incorporating expert system approaches into nerve conduction measurement and control algorithms. Using fuzzy logic techniques to duplicate the reasoning strategies of experienced electrodiagnostic clinicians, a software controller was developed to automatically perform sensory nerve conduction studies. The fuzzy logic system successfully performed 88% of 97 sensory studies in a mixed group of normal and patient populations. Sensory nerve action potential latency and amplitude measures obtained with automated testing were the same as determined by clinicians. Failures were related to design limitations of the controller, noise, and artifact. The high negative predictive value and sensitivity of fuzzy logic based testing suggest that its utility is in minimizing the need for unnecessary conventional electrodiagnostic studies in patients with normal nerve function. Fuzzy logic appears to be a useful approach to nerve conduction automation that can model expert reasoning and judgment.

  2. Fuzzy logic control of the building structure with CLEMR dampers

    NASA Astrophysics Data System (ADS)

    Zhang, Xiang-Cheng; Xu, Zhao-Dong; Huang, Xing-Huai; Zhu, Jun-Tao

    2013-04-01

    The semi-active control technology has been paid more attention in the field of structural vibration control due to its high controllability, excellent control effect and low power requirement. When semi-active control device are used for vibration control, some challenges must be taken into account, such as the reliability and the control strategy of the device. This study presents a new large tonnage compound lead extrusion magnetorheological (CLEMR) damper, whose mathematical model is introduced to describe the variation of damping force with current and velocity. Then a current controller based on the fuzzy logic control strategy is designed to determine control currents of the CLEMR dampers rapidly. A ten-floor frame structure with CLEMR dampers using the fuzzy logic control strategy is built and calculated by using MATLAB. Calculation results show that CLEMR dampers can reduce the seismic responses of structures effectively. Calculation results of the fuzzy logic control strategy are compared with those of the semi-active limit Hrovat control structure, the passive-off control structure, and the uncontrolled structure. Comparison results show that the fuzzy logic control strategy can determine control currents of CLEMR dampers quickly and can reduce seismic responses of the structures more effectively than the passive-off control strategy and the uncontrolled structure.

  3. Use of fuzzy logic in signal processing and validation

    SciTech Connect

    Heger, A.S.; Alang-Rashid, N.K. ); Holbert, K.E. )

    1993-01-01

    The advent of fuzzy logic technology has afforded another opportunity to reexamine the signal processing and validation process (SPV). The features offered by fuzzy logic can lend themselves to a more reliable and perhaps fault-tolerant approach to SPV. This is particularly attractive to complex system operations, where optimal control for safe operation depends on reliable input data. The reason for the use of fuzzy logic as the tool for SPV is its ability to transform information from the linguistic domain to a mathematical domain for processing and then transformation of its result back into the linguistic domain for presentation. To ensure the safe and optimal operation of a nuclear plant, for example, reliable and valid data must be available to the human and computer operators. Based on these input data, the operators determine the current state of the power plant and project corrective actions for future states. This determination is based on available data and the conceptual and mathematical models for the plant. A fault-tolerant SPV based on fuzzy logic can help the operators meet the objective of effective, efficient, and safe operation of the nuclear power plant. The ultimate product of this project will be a code that will assist plant operators in making informed decisions under uncertain conditions when conflicting signals may be present.

  4. An Innovative Fuzzy-Logic-Based Methodology for Trend Identification

    SciTech Connect

    Wang Xin; Tsoukalas, Lefteri H.; Wei, Thomas Y.C.; Reifman, Jaques

    2001-07-15

    A new fuzzy-logic-based methodology for on-line signal trend identification is introduced. The methodology may be used for detecting the onset of nuclear power plant (NPP) transients at the earliest possible time and could be of great benefit to diagnostic, maintenance, and performance-monitoring programs. Although signal trend identification is complicated by the presence of noise, fuzzy methods can help capture important features of on-line signals, integrate the information included in these features, and classify incoming NPP signals into increasing, decreasing, and steady-state trend categories. A computer program named PROTREN is developed and tested for the purpose of verifying this methodology using NPP and simulation data. The results indicate that the new fuzzy-logic-based methodology is capable of detecting transients accurately, it identifies trends reliably and does not misinterpret a steady-state signal as a transient one.

  5. River suspended sediment modelling using a fuzzy logic approach

    NASA Astrophysics Data System (ADS)

    Kisi, Ozgur; Emin Karahan, M.; En, Zekai

    2006-12-01

    Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating-curve models. Benchmarking was based on a 5-year period of continuous streamflow and sediment concentration data of Quebrada Blanca Station operated by the United States Geological Survey. The benchmark results showed that the fuzzy model was able to produce much better results than rating-curve models. The fuzzy model proposed in the study is site specific and does not simulate the hysteresis effects. Copyright

  6. Approach to Synchronization Control of Magnetic Bearings Using Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Yang, Li-Farn

    1996-01-01

    This paper presents a fuzzy-logic approach to the synthesis of synchronization control for magnetically suspended rotor system. The synchronization control enables a whirling rotor to undergo synchronous motion along the magnetic bearing axes; thereby avoiding the gyroscopic effect that degrade the stability of rotor systems when spinning at high speed. The control system features a fuzzy controller acting on the magnetic bearing device, in which the fuzzy inference system trained through fuzzy rules to minimize the differential errors between four bearing axes so that an error along one bearing axis can affect the overall control loop for the motion synchronization. Numerical simulations of synchronization control for the magnetically suspended rotor system are presented to show the effectiveness of the present approach.

  7. Fuzzy Petri net-based programmable logic controller.

    PubMed

    Andreu, D; Pascal, J C; Valette, R

    1997-01-01

    Programmable logic controllers (PLCs) are able to directly implement control sequences specified by means of standard languages such as Grafcet or formal models such as Petri nets. In the case of simple regulation problems between two steps it could be of great interest to introduce a notion of "fuzzy events" in order to denote a continuous evolution from one state to another. This could result from a linear interpolation between the commands attached to two control steps represented by two Petri net (PN) places. This paper is an attempt to develop fuzzy PN-based PLCs in a similar way as fuzzy controllers (regulators). Our approach is based on a combination of Petri nets with possibility theory (Petri nets with fuzzy markings).

  8. Decision-making with a fuzzy logic inference engine

    NASA Astrophysics Data System (ADS)

    O'Hagan, Nadja K.; O'Hagan, Michael

    1993-12-01

    Each year, the Army National Guard (ARNG) reviews its entire force structure and, based upon changing national requirements, determines which units, in which states, will be inactivated, activated, or reallocated. These decisions are based upon a fairly large set of criteria and include: overall manpower goals, socioeconomic factors, and unit performance characteristics. In order to facilitate these decisions, a fuzzy expert system is currently being built for the ARNG using a fuzzy logic inference engine based upon the fuzzy decision-making methodologies of Bellman & Zadeh as extended by Yager, and the analytical hierarchical method of Saaty, which normally uses paired comparisons. Incorporated within the force realignment decision support system, is a method of capturing decision maker preferences, which does not require paired comparisons and is (1) rapid, and (2) forces transitivity in the resulting fuzzy relative importance measures.

  9. Boolean and fuzzy logic implemented at the molecular level

    NASA Astrophysics Data System (ADS)

    Gentili, Pier Luigi

    2007-07-01

    In this work, it is shown how to implement both hard and soft computing by means of two structurally related heterocyclic compounds: flindersine (FL) and 6(5H)-phenanthridinone (PH). Since FL and PH have a carbonyl group in their molecular skeletons, they exhibit Proximity Effects in their photophysics. In other words, they have an emission power that can be modulated through external inputs such as temperature ( T) and hydrogen-bonding donation (HBD) ability of solvents. This phenomenology can be exploited to implement both crisp and fuzzy logic. Fuzzy Logic Systems (FLSs) wherein the antecedents of the rules are connected through the AND operator, are built by both the Mamdani's and Sugeno's models. Finally, they are adopted as approximators of the proximity effect phenomenon and tested for their prediction capabilities. Moreover, FL as photochromic compound is also a multiply configurable crisp logic molecular element.

  10. Heterogeneous fuzzy logic networks: fundamentals and development studies.

    PubMed

    Pedrycz, Witold

    2004-11-01

    The recent trend in the development of neurofuzzy systems has profoundly emphasized the importance of synergy between the fundamentals of fuzzy sets and neural networks. The resulting frameworks of the neurofuzzy systems took advantage of an array of learning mechanisms primarily originating within the theory of neurocomputing and the use of fuzzy models (predominantly rule-based systems) being well established in the realm of fuzzy sets. Ideally, one can anticipate that neurofuzzy systems should fully exploit the linkages between these two technologies while strongly preserving their evident identities (plasticity or learning abilities to be shared by the transparency and full interpretability of the resulting neurofuzzy constructs). Interestingly, this synergy still becomes a target yet to be satisfied. This study is an attempt to address the fundamental interpretability challenge of neurofuzzy systems. Our underlying conjecture is that the transparency of any neurofuzzy system links directly with the logic fabric of the system so the logic fundamentals of the underlying architecture become of primordial relevance. Having this in mind the development of neurofuzzy models hinges on a collection of logic driven processing units named here fuzzy (logic) neurons. These are conceptually simple logic-oriented elements that come with a well-defined semantics and plasticity. Owing to their diversity, such neurons form essential building blocks of the networks. The study revisits the existing categories of logic neurons, provides with their taxonomy, helps understand their functional features and sheds light on their behavior when being treated as computational components of any neurofuzzy architecture. The two main categories of aggregative and reference neurons are deeply rooted in the fundamental operations encountered in the technology of fuzzy sets (including logic operations, linguistic modifiers, and logic reference operations). The developed heterogeneous networks

  11. Norepinephrine weaning in septic shock patients by closed loop control based on fuzzy logic

    PubMed Central

    Merouani, Mehdi; Guignard, Bruno; Vincent, François; Borron, Stephen W; Karoubi, Philippe; Fosse, Jean-Philippe; Cohen, Yves; Clec'h, Christophe; Vicaut, Eric; Marbeuf-Gueye, Carole; Lapostolle, Frederic; Adnet, Frederic

    2008-01-01

    Introduction The rate of weaning of vasopressors drugs is usually an empirical choice made by the treating in critically ill patients. We applied fuzzy logic principles to modify intravenous norepinephrine (noradrenaline) infusion rates during norepinephrine infusion in septic patients in order to reduce the duration of shock. Methods Septic patients were randomly assigned to norepinephrine infused either at the clinician's discretion (control group) or under closed-loop control based on fuzzy logic (fuzzy group). The infusion rate changed automatically after analysis of mean arterial pressure in the fuzzy group. The primary end-point was time to cessation of norepinephrine. The secondary end-points were 28-day survival, total amount of norepinephine infused and duration of mechanical ventilation. Results Nineteen patients were randomly assigned to fuzzy group and 20 to control group. Weaning of norepinephrine was achieved in 18 of the 20 control patients and in all 19 fuzzy group patients. Median (interquartile range) duration of shock was significantly shorter in the fuzzy group than in the control group (28.5 [20.5 to 42] hours versus 57.5 [43.7 to 117.5] hours; P < 0.0001). There was no significant difference in duration of mechanical ventilation or survival at 28 days between the two groups. The median (interquartile range) total amount of norepinephrine infused during shock was significantly lower in the fuzzy group than in the control group (0.6 [0.2 to 1.0] μg/kg versus 1.4 [0.6 to 2.7] μg/kg; P < 0.01). Conclusions Our study has shown a reduction in norepinephrine weaning duration in septic patients enrolled in the fuzzy group. We attribute this reduction to fuzzy control of norepinephrine infusion. Trial registration Trial registration: Clinicaltrials.gov NCT00763906. PMID:19068113

  12. Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes

    NASA Technical Reports Server (NTRS)

    Duerksen, Noel

    1997-01-01

    It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control different airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control aileron or roll spoiler position. This controller was used to control bank angle for both a piston powered single engine aileron equipped airplane simulation and a business jet simulation which used spoilers for primary roll control. Overspeed, stall and overbank protection were incorporated in the form of expert systems supervisors and weighted fuzzy rules. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic lateral controller could be successfully used on two general aviation aircraft types that have very different characteristics. These controllers worked for both airplanes over their entire flight envelopes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle ]ever travel, etc.). This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.

  13. Fuzzy, crisp, and human logic in e-commerce marketing data mining

    NASA Astrophysics Data System (ADS)

    Hearn, Kelda L.; Zhang, Yanqing

    2001-03-01

    In today's business world there is an abundance of available data and a great need to make good use of it. Many businesses would benefit from examining customer habits and trends and making marketing and product decisions based on that analysis. However, the process of manually examining data and making sound decisions based on that data is time consuming and often impractical. Intelligent systems that can make judgments similar to human judgments are sorely needed. Thus, systems based on fuzzy logic present themselves as an option to be seriously considered. The work described in this paper attempts to make an initial comparison between fuzzy logic and more traditional hard or crisp logic to see which would make a better substitute for human intervention. In this particular case study, customers are classified into categories that indicate how desirable the customer would be as a prospect for marketing. This classification is based on a small set of customer data. The results from these investigations make it clear that fuzzy logic is more able to think for itself and make decisions that more closely match human decision and is therefore significantly closer to human logic than crisp logic.

  14. A Numerical Optimization Approach for Tuning Fuzzy Logic Controllers

    NASA Technical Reports Server (NTRS)

    Woodard, Stanley E.; Garg, Devendra P.

    1998-01-01

    This paper develops a method to tune fuzzy controllers using numerical optimization. The main attribute of this approach is that it allows fuzzy logic controllers to be tuned to achieve global performance requirements. Furthermore, this approach allows design constraints to be implemented during the tuning process. The method tunes the controller by parameterizing the membership functions for error, change-in-error and control output. The resulting parameters form a design vector which is iteratively changed to minimize an objective function. The minimal objective function results in an optimal performance of the system. A spacecraft mounted science instrument line-of-sight pointing control is used to demonstrate results.

  15. Blended Homing Guidance Law Using Fuzzy Logic

    DTIC Science & Technology

    1998-01-01

    Presented at the 1998 AIAA Guidance, Navigation and Control Conference, August 10-12, Boston, MA  Copyright 1998 by Optimal Synthesis Inc. All...K. Menon* and V. R. Iragavarapu+ Optimal Synthesis Inc. 450 San Antonio Road, Suite 46 Palo Alto, CA 94306-4638 Abstract The application of fuzzy...unclassified c. THIS PAGE unclassified Standard Form 298 (Rev. 8-98) Prescribed by ANSI Std Z39-18  Copyright 1998 by Optimal Synthesis Inc. All

  16. Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2

    NASA Technical Reports Server (NTRS)

    Lea, Robert N. (Editor); Villarreal, James A. (Editor)

    1991-01-01

    Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Texas, Houston. Topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

  17. A Fuzzy Logic Optimal Control Law Solution to the CMMCA Tracking Problem

    DTIC Science & Technology

    1993-03-01

    Pussy Sots. The concept of a fuzzy set is fundamental to fuzzy logic. A fuzzy set is any set in which elements may have In- between membership in...logical NOT) is "u(NOT A) - 1 - UAx)M 2.6.4 Pussy Control. With the linguistic sets defined for the j input and output variables of a given system

  18. Ultrasonic Data Display and Analysis System Developed (Including Fuzzy Logic Analysis) for the Windows-Based PC

    NASA Technical Reports Server (NTRS)

    Lovelace, Jeffrey J.; Cios, Kryzsztof J.; Roth, Don J.; cAO, wEI n.

    2001-01-01

    Post-Scan Interactive Data Display (PSIDD) III is a user-oriented Windows-based system that facilitates the display and comparison of ultrasonic contact measurement data obtained at NASA Glenn Research Center's Ultrasonic Nondestructive Evaluation measurement facility. The system is optimized to compare ultrasonic measurements made at different locations within a material or at different stages of material degradation. PSIDD III provides complete analysis of the primary waveforms in the time and frequency domains along with the calculation of several frequency-dependent properties including phase velocity and attenuation coefficient and several frequency-independent properties, like the cross correlation velocity. The system allows image generation on all the frequency-dependent properties at any available frequency (limited by the bandwidth used in the scans) and on any of the frequency-independent properties. From ultrasonic contact scans, areas of interest on an image can be studied with regard to underlying raw waveforms and derived ultrasonic properties by simply selecting the point on the image. The system offers various modes of indepth comparison between scan points. Up to five scan points can be selected for comparative analysis at once. The system was developed with Borland Delphi software (Visual Pascal) and is based on an SQL data base. It is ideal for the classification of material properties or the location of microstructure variations in materials. Along with the ultrasonic contact measurement software that it is partnered with, this system is technology ready and can be transferred to users worldwide.

  19. Intelligent control based on fuzzy logic and neural net theory

    NASA Technical Reports Server (NTRS)

    Lee, Chuen-Chien

    1991-01-01

    In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment.

  20. Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model

    PubMed Central

    Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

    2014-01-01

    Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models. PMID:25431586

  1. Use of Fuzzy Logic Systems for Assessment of Primary Faults

    NASA Astrophysics Data System (ADS)

    Petrović, Ivica; Jozsa, Lajos; Baus, Zoran

    2015-09-01

    In electric power systems, grid elements are often subjected to very complex and demanding disturbances or dangerous operating conditions. Determining initial fault or cause of those states is a difficult task. When fault occurs, often it is an imperative to disconnect affected grid element from the grid. This paper contains an overview of possibilities for using fuzzy logic in an assessment of primary faults in the transmission grid. The tool for this task is SCADA system, which is based on information of currents, voltages, events of protection devices and status of circuit breakers in the grid. The function model described with the membership function and fuzzy logic systems will be presented in the paper. For input data, diagnostics system uses information of protection devices tripping, states of circuit breakers and measurements of currents and voltages before and after faults.

  2. INJECTION PAINTING OPTIMIZATION WITH FUZZY LOGIC EXPERT SYSTEM.

    SciTech Connect

    BEEBE-WANG,J.; TANG,J.

    2001-06-18

    Optimizing transverse particle distributions in the accumulator ring is one of most important factors to the future performance of the Spallation Neutron Source (SNS) [l]. This can only be achieved by optimizing the injection bumps that paint the beam in phase space. The process is complex due to the vague distribution inputs and the multiple optimization goals. Furthermore, the priority of the optimization criteria could change at different operational stages. We propose optimizing transverse phase space painting with fuzzy logic and present our initial studies toward that end. The focus of this paper is on how the problem can be solved with a Fuzzy Logic (FL) expert system through the creation of a set of rules that can be applied by the system. Various particle distributions, from computer simulations, are analyzed with FL and the results are compared and discussed. Finally, a run-time optimization control system is proposed.

  3. Control of a flexible beam using fuzzy logic

    NASA Technical Reports Server (NTRS)

    Mccullough, Claire L.

    1991-01-01

    The goal of this project, funded under the NASA Summer Faculty Fellowship program, was to evaluate control methods utilizing fuzzy logic for applicability to control of flexible structures. This was done by applying these methods to control of the Control Structures Interaction Suitcase Demonstrator developed at Marshall Space Flight Center. The CSI Suitcase Demonstrator is a flexible beam, mounted at one end with springs and bearing, and with a single actuator capable of rotating the beam about a pin at the fixed end. The control objective is to return the tip of the free end to a zero error position (from a nonzero initial condition). It is neither completely controllable nor completely observable. Fuzzy logic control was demonstrated to successfully control the system and to exhibit desirable robustness properties compared to conventional control.

  4. Fuzzy temporal logic based railway passenger flow forecast model.

    PubMed

    Dou, Fei; Jia, Limin; Wang, Li; Xu, Jie; Huang, Yakun

    2014-01-01

    Passenger flow forecast is of essential importance to the organization of railway transportation and is one of the most important basics for the decision-making on transportation pattern and train operation planning. Passenger flow of high-speed railway features the quasi-periodic variations in a short time and complex nonlinear fluctuation because of existence of many influencing factors. In this study, a fuzzy temporal logic based passenger flow forecast model (FTLPFFM) is presented based on fuzzy logic relationship recognition techniques that predicts the short-term passenger flow for high-speed railway, and the forecast accuracy is also significantly improved. An applied case that uses the real-world data illustrates the precision and accuracy of FTLPFFM. For this applied case, the proposed model performs better than the k-nearest neighbor (KNN) and autoregressive integrated moving average (ARIMA) models.

  5. An architecture for designing fuzzy logic controllers using neural networks

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1991-01-01

    Described here is an architecture for designing fuzzy controllers through a hierarchical process of control rule acquisition and by using special classes of neural network learning techniques. A new method for learning to refine a fuzzy logic controller is introduced. A reinforcement learning technique is used in conjunction with a multi-layer neural network model of a fuzzy controller. The model learns by updating its prediction of the plant's behavior and is related to the Sutton's Temporal Difference (TD) method. The method proposed here has the advantage of using the control knowledge of an experienced operator and fine-tuning it through the process of learning. The approach is applied to a cart-pole balancing system.

  6. Rule based fuzzy logic approach for classification of fibromyalgia syndrome.

    PubMed

    Arslan, Evren; Yildiz, Sedat; Albayrak, Yalcin; Koklukaya, Etem

    2016-06-01

    Fibromyalgia syndrome (FMS) is a chronic muscle and skeletal system disease observed generally in women, manifesting itself with a widespread pain and impairing the individual's quality of life. FMS diagnosis is made based on the American College of Rheumatology (ACR) criteria. However, recently the employability and sufficiency of ACR criteria are under debate. In this context, several evaluation methods, including clinical evaluation methods were proposed by researchers. Accordingly, ACR had to update their criteria announced back in 1990, 2010 and 2011. Proposed rule based fuzzy logic method aims to evaluate FMS at a different angle as well. This method contains a rule base derived from the 1990 ACR criteria and the individual experiences of specialists. The study was conducted using the data collected from 60 inpatient and 30 healthy volunteers. Several tests and physical examination were administered to the participants. The fuzzy logic rule base was structured using the parameters of tender point count, chronic widespread pain period, pain severity, fatigue severity and sleep disturbance level, which were deemed important in FMS diagnosis. It has been observed that generally fuzzy predictor was 95.56 % consistent with at least of the specialists, who are not a creator of the fuzzy rule base. Thus, in diagnosis classification where the severity of FMS was classified as well, consistent findings were obtained from the comparison of interpretations and experiences of specialists and the fuzzy logic approach. The study proposes a rule base, which could eliminate the shortcomings of 1990 ACR criteria during the FMS evaluation process. Furthermore, the proposed method presents a classification on the severity of the disease, which was not available with the ACR criteria. The study was not limited to only disease classification but at the same time the probability of occurrence and severity was classified. In addition, those who were not suffering from FMS were

  7. Fuzzy logic applied to prospecting for areas for installation of wood panel industries.

    PubMed

    Dos Santos, Alexandre Rosa; Paterlini, Ewerthon Mattos; Fiedler, Nilton Cesar; Ribeiro, Carlos Antonio Alvares Soares; Lorenzon, Alexandre Simões; Domingues, Getulio Fonseca; Marcatti, Gustavo Eduardo; de Castro, Nero Lemos Martins; Teixeira, Thaisa Ribeiro; Dos Santos, Gleissy Mary Amaral Dino Alves; Juvanhol, Ronie Silva; Branco, Elvis Ricardo Figueira; Mota, Pedro Henrique Santos; da Silva, Lilianne Gomes; Pirovani, Daiani Bernardo; de Jesus, Waldir Cintra; Santos, Ana Carolina de Albuquerque; Leite, Helio Garcia; Iwakiri, Setsuo

    2017-05-15

    Prospecting for suitable areas for forestry operations, where the objective is a reduction in production and transportation costs, as well as the maximization of profits and available resources, constitutes an optimization problem. However, fuzzy logic is an alternative method for solving this problem. In the context of prospecting for suitable areas for the installation of wood panel industries, we propose applying fuzzy logic analysis for simulating the planting of different species and eucalyptus hybrids in Espírito Santo State, Brazil. The necessary methodological steps for this study are as follows: a) agriclimatological zoning of different species and eucalyptus hybrids; b) the selection of the vector variables; c) the application of the Euclidean distance to the vector variables; d) the application of fuzzy logic to matrix variables of the Euclidean distance; and e) the application of overlap fuzzy logic to locate areas for installation of wood panel industries. Among all the species and hybrids, Corymbia citriodora showed the highest percentage values for the combined very good and good classes, with 8.60%, followed by Eucalyptus grandis with 8.52%, Eucalyptus urophylla with 8.35% and Urograndis with 8.34%. The fuzzy logic analysis afforded flexibility in prospecting for suitable areas for the installation of wood panel industries in the Espírito Santo State can bring great economic and social benefits to the local population with the generation of jobs, income, tax revenues and GDP increase for the State and municipalities involved. The proposed methodology can be adapted to other areas and agricultural crops. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic

    DTIC Science & Technology

    2005-06-01

    Sensor Network, Energy Efficiency, Fuzzy Logic. 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 18. NUMBER 19a. NAME OF RESPONSIBLE PERSON a...are well defined, including detection of false alarms or misses, classification errors, and track quality. 3 Spectrum Efficient Coding Scheme for...tracking, and classification . Therefore, compared to ad-hoc networks, WSN should be event-centric. In this paper, we propose an event forecasting scheme for

  9. Incorporating qualitative knowledge in enzyme kinetic models using fuzzy logic.

    PubMed

    Lee, B; Yen, J; Yang, L; Liao, J C

    1999-03-20

    Modeling of metabolic pathway dynamics requires detailed kinetic equations at the enzyme level. In particular, the kinetic equations must account for metabolite effectors that contribute significantly to the pathway regulation in vivo. Unfortunately, most kinetic rate laws available in the literature do not consider all the effectors simultaneously, and much kinetic information exists in a qualitative or semiquantitative form. In this article, we present a strategy to incorporate such information into the kinetic equation. This strategy uses fuzzy logic-based factors to modify algebraic rate laws that account for partial kinetic characteristics. The parameters introduced by the fuzzy factors are then optimized by use of a hybrid of simplex and genetic algorithms. The resulting model provides a flexible form that can simulate various kinetic behaviors. Such kinetic models are suitable for pathway modeling without complete enzyme mechanisms. Three enzymes in Escherichia coli central metabolism are used as examples: phosphoenolpyruvate carboxylase; phosphoenolpyruvate carboxykinase; and pyruvate kinase I. Results show that, with fuzzy logic-augmented models, the kinetic data can be much better described. In particular, complex behavior, such as allosteric inhibition, can be captured using fuzzy rules. The resulting models, even though they do not provide additional physical meaning in enzyme mechanisms, allow the model to incorporate semiquantitative information in metabolic pathway models.

  10. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  11. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    This paper presents a new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system. In particular, our generalized approximate reasoning-based intelligent control (GARIC) architecture (1) learns and tunes a fuzzy logic controller even when only weak reinforcement, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward neural network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto et al. (1983) to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and demonstrates significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  12. Learning and tuning fuzzy logic controllers through reinforcements

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap

    1992-01-01

    A new method for learning and tuning a fuzzy logic controller based on reinforcements from a dynamic system is presented. In particular, our Generalized Approximate Reasoning-based Intelligent Control (GARIC) architecture: (1) learns and tunes a fuzzy logic controller even when only weak reinforcements, such as a binary failure signal, is available; (2) introduces a new conjunction operator in computing the rule strengths of fuzzy control rules; (3) introduces a new localized mean of maximum (LMOM) method in combining the conclusions of several firing control rules; and (4) learns to produce real-valued control actions. Learning is achieved by integrating fuzzy inference into a feedforward network, which can then adaptively improve performance by using gradient descent methods. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. The GARIC architecture is applied to a cart-pole balancing system and has demonstrated significant improvements in terms of the speed of learning and robustness to changes in the dynamic system's parameters over previous schemes for cart-pole balancing.

  13. Modelling of Reservoir Operations using Fuzzy Logic and ANNs

    NASA Astrophysics Data System (ADS)

    Van De Giesen, N.; Coerver, B.; Rutten, M.

    2015-12-01

    Today, almost 40.000 large reservoirs, containing approximately 6.000 km3 of water and inundating an area of almost 400.000 km2, can be found on earth. Since these reservoirs have a storage capacity of almost one-sixth of the global annual river discharge they have a large impact on the timing, volume and peaks of river discharges. Global Hydrological Models (GHM) are thus significantly influenced by these anthropogenic changes in river flows. We developed a parametrically parsimonious method to extract operational rules based on historical reservoir storage and inflow time-series. Managing a reservoir is an imprecise and vague undertaking. Operators always face uncertainties about inflows, evaporation, seepage losses and various water demands to be met. They often base their decisions on experience and on available information, like reservoir storage and the previous periods inflow. We modeled this decision-making process through a combination of fuzzy logic and artificial neural networks in an Adaptive-Network-based Fuzzy Inference System (ANFIS). In a sensitivity analysis, we compared results for reservoirs in Vietnam, Central Asia and the USA. ANFIS can indeed capture reservoirs operations adequately when fed with a historical monthly time-series of inflows and storage. It was shown that using ANFIS, operational rules of existing reservoirs can be derived without much prior knowledge about the reservoirs. Their validity was tested by comparing actual and simulated releases with each other. For the eleven reservoirs modelled, the normalised outflow, <0,1>, was predicted with a MSE of 0.002 to 0.044. The rules can be incorporated into GHMs. After a network for a specific reservoir has been trained, the inflow calculated by the hydrological model can be combined with the release and initial storage to calculate the storage for the next time-step using a mass balance. Subsequently, the release can be predicted one time-step ahead using the inflow and storage.

  14. [Purposefulness of using fuzzy logic approaches in the rare disease clinical trials].

    PubMed

    Kiseleva, T S; Pagava, K I; Korinteli, I A; Korinteli, M G; Shonvadze, D N

    2011-04-01

    Different methods being used in the rare diseases clinical trials are examined. There is shown the purposefulness of using fuzzy approaches for such studies. Some advantages of Fuzzy logic methods in comparison with Baysian approach are substantiated.

  15. Control Law for Automatic Landing Using Fuzzy Logic Control

    NASA Astrophysics Data System (ADS)

    Kato, Akio; Inagaki, Yoshiki

    The effectiveness of fuzzy logic control law for automatic landing of aircraft, which cover both of control to lead aircraft from level flight at an altitude of 500m to the flight on the glide-path course near the runway and control for the aircraft to land smoothly on a runway, was studied. The control law of the automatic landing was designed to match the design goals of leading from the horizontal flight to the flight on the glide-path course quickly and smoothly and of landing smoothly on a runway. Because there is the ground effect at landing, design of control law and evaluation of control performance were done in consideration of the ground effect. As a result, it was confirmed that the design objective was achieved. Even if the characteristics of the plant changes greatly, this control law was able to maintain the control performance. Moreover, it was confirmed to be able to land safely when there was air turbulence. This paper shows that fuzzy logic control is an effective and flexible method when applied to control law for automatic landing and the design method of control law using fuzzy logic control was obtained.

  16. Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes

    NASA Technical Reports Server (NTRS)

    Duerksen, Noel

    1996-01-01

    It has been hypothesized that a human pilot uses the same set of generic skills to control a wide variety of aircraft. If this is true, then it should be possible to construct an electronic controller which embodies this generic skill set such that it can successfully control difference airplanes without being matched to a specific airplane. In an attempt to create such a system, a fuzzy logic controller was devised to control throttle position and another to control elevator position. These two controllers were used to control flight path angle and airspeed for both a piston powered single engine airplane simulation and a business jet simulation. Overspeed protection and stall protection were incorporated in the form of expert systems supervisors. It was found that by using the artificial intelligence techniques of fuzzy logic and expert systems, a generic longitudinal controller could be successfully used on two general aviation aircraft types that have very difference characteristics. These controllers worked for both airplanes over their entire flight envelopes including configuration changes. The controllers for both airplanes were identical except for airplane specific limits (maximum allowable airspeed, throttle lever travel, etc.). The controllers also handled configuration changes without mode switching or knowledge of the current configuration. This research validated the fact that the same fuzzy logic based controller can control two very different general aviation airplanes. It also developed the basic controller architecture and specific control parameters required for such a general controller.

  17. Regression Models and Fuzzy Logic Prediction of TBM Penetration Rate

    NASA Astrophysics Data System (ADS)

    Minh, Vu Trieu; Katushin, Dmitri; Antonov, Maksim; Veinthal, Renno

    2017-03-01

    This paper presents statistical analyses of rock engineering properties and the measured penetration rate of tunnel boring machine (TBM) based on the data of an actual project. The aim of this study is to analyze the influence of rock engineering properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), rock brittleness index (BI), the distance between planes of weakness (DPW), and the alpha angle (Alpha) between the tunnel axis and the planes of weakness on the TBM rate of penetration (ROP). Four (4) statistical regression models (two linear and two nonlinear) are built to predict the ROP of TBM. Finally a fuzzy logic model is developed as an alternative method and compared to the four statistical regression models. Results show that the fuzzy logic model provides better estimations and can be applied to predict the TBM performance. The R-squared value (R2) of the fuzzy logic model scores the highest value of 0.714 over the second runner-up of 0.667 from the multiple variables nonlinear regression model.

  18. Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

    NASA Astrophysics Data System (ADS)

    Girola Schneider, R.

    2017-07-01

    The fuzzy logic is a branch of the artificial intelligence founded on the concept that everything is a matter of degree. It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters. Fuzzy logic enables the researcher to work with "imprecise" information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic's techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

  19. Fuzzy-logic modeling of Fenton's oxidation of anaerobically pretreated poultry manure wastewater.

    PubMed

    Yetilmezsoy, Kaan

    2012-07-01

    A multiple inputs and multiple outputs (MIMO) fuzzy-logic-based model was proposed to estimate color and chemical oxygen demand (COD) removal efficiencies in the post-treatment of anaerobically pretreated poultry manure wastewater effluent using Fenton's oxidation process. Three main input variables including initial pH, Fe+2, and H2O2 dosages were fuzzified in a new numerical modeling scheme by the use of an artificial intelligence-based approach. Trapezoidal membership functions with eight levels were conducted for the fuzzy subsets, and a Mamdani-type fuzzy inference system was used to implement a total of 70 rules in the IF-THEN format. The product (prod) and the center of gravity (centroid) methods were applied as the inference operator and defuzzification methods, respectively. Fuzzy-logic predicted results were compared with the outputs of two first-order polynomial regression models derived in the scope of this study. Estimated results were also compared to the multiple regression approach by means of various descriptive statistical indicators, such as root mean-squared error, index of agreement, fractional variance, proportion of systematic error, etc. Results of the statistical analysis clearly revealed that, compared to conventional regression models, the proposed MIMO fuzzy-logic model produced very smaller deviations and demonstrated a superior predictive performance on forecasting of color and COD removal efficiencies with satisfactory determination coefficients over 0.98. Due to high capability of the fuzzy-logic methodology in capturing the non-linear interactions, it was demonstrated that a complex dynamic system, such as Fenton's oxidation, could be easily modeled.

  20. Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators.

    PubMed

    Bárdossy, András; Blinowska, Aleksandra; Kuzmicz, Wieslaw; Ollitrault, Jacky; Lewandowski, Michał; Przybylski, Andrzej; Jaworski, Zbigniew

    2014-02-01

    The paper presents a diagnostic algorithm for classifying cardiac tachyarrhythmias for implantable cardioverter defibrillators (ICDs). The main aim was to develop an algorithm that could reduce the rate of occurrence of inappropriate therapies, which are often observed in existing ICDs. To achieve low energy consumption, which is a critical factor for implantable medical devices, very low computational complexity of the algorithm was crucial. The study describes and validates such an algorithm and estimates its clinical value. The algorithm was based on the heart rate variability (HRV) analysis. The input data for our algorithm were: RR-interval (I), as extracted from raw intracardiac electrogram (EGM), and in addition two other features of HRV called here onset (ONS) and instability (INST). 6 diagnostic categories were considered: ventricular fibrillation (VF), ventricular tachycardia (VT), sinus tachycardia (ST), detection artifacts and irregularities (including extrasystoles) (DAI), atrial tachyarrhythmias (ATF) and no tachycardia (i.e. normal sinus rhythm) (NT). The initial set of fuzzy rules based on the distributions of I, ONS and INST in the 6 categories was optimized by means of a software tool for automatic rule assessment using simulated annealing. A training data set with 74 EGM recordings was used during optimization, and the algorithm was validated with a validation data set with 58 EGM recordings. Real life recordings stored in defibrillator memories were used. Additionally the algorithm was tested on 2 sets of recordings from the PhysioBank databases: MIT-BIH Arrhythmia Database and MIT-BIH Supraventricular Arrhythmia Database. A custom CMOS integrated circuit implementing the diagnostic algorithm was designed in order to estimate the power consumption. A dedicated Web site, which provides public online access to the algorithm, has been created and is available for testing it. The total number of events in our training and validation sets was 132. In

  1. Distributed traffic signal control using fuzzy logic

    NASA Technical Reports Server (NTRS)

    Chiu, Stephen

    1992-01-01

    We present a distributed approach to traffic signal control, where the signal timing parameters at a given intersection are adjusted as functions of the local traffic condition and of the signal timing parameters at adjacent intersections. Thus, the signal timing parameters evolve dynamically using only local information to improve traffic flow. This distributed approach provides for a fault-tolerant, highly responsive traffic management system. The signal timing at an intersection is defined by three parameters: cycle time, phase split, and offset. We use fuzzy decision rules to adjust these three parameters based only on local information. The amount of change in the timing parameters during each cycle is limited to a small fraction of the current parameters to ensure smooth transition. We show the effectiveness of this method through simulation of the traffic flow in a network of controlled intersections.

  2. The research on high speed underwater target recognition based on fuzzy logic inference

    NASA Astrophysics Data System (ADS)

    Jiang, Xiang-Dong; Yang, De-Sen; Shi, Sheng-Guo; Li, Si-Chun

    2006-06-01

    The underwater target recognition is a key technology in acoustic confrontation and underwater defence. In this article, a recognition system based of fuzzy logic inference (FLI) is set up. This system is mainly composed of three parts: the fuzzy input module, the fuzzy logic inference module with a set of inference rules and the de-fuzzy output module. The inference result shows the recognition system is effective in most conditions.

  3. An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream

    PubMed Central

    Marakhimov, A. R.; Igamberdiev, H. Z.; Umarov, Sh. X.

    2016-01-01

    This paper considers the problem of building adaptive fuzzy-logic traffic control systems (AFLTCS) to deal with information fuzziness and uncertainty in case of heavy traffic streams. Methods of formal description of traffic control on the crossroads based on fuzzy sets and fuzzy logic are proposed. This paper also provides efficient algorithms for implementing AFLTCS and develops the appropriate simulation models to test the efficiency of suggested approach. PMID:27517081

  4. Robustness of fuzzy logic power system stabilizers applied to multimachine power system

    SciTech Connect

    Hiyama, Takashi . Dept. of Electrical Engineering and Computer Science)

    1994-09-01

    This paper investigates the robustness of fuzzy logic stabilizers using the information of speed and acceleration states of a study unit. The input signals are the real power output and/or the speed of the study unit. Non-linear simulations show the robustness of the fuzzy logic power system stabilizers. Experiments are also performed by using a micro-machine system. The results show the feasibility of proposed fuzzy logic stabilizer.

  5. Fuzzy knowledge base construction through belief networks based on Lukasiewicz logic

    NASA Technical Reports Server (NTRS)

    Lara-Rosano, Felipe

    1992-01-01

    In this paper, a procedure is proposed to build a fuzzy knowledge base founded on fuzzy belief networks and Lukasiewicz logic. Fuzzy procedures are developed to do the following: to assess the belief values of a consequent, in terms of the belief values of its logical antecedents and the belief value of the corresponding logical function; and to update belief values when new evidence is available.

  6. Application of fuzzy logic and fuzzy AHP to mineral prospectivity mapping of porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang, NW China

    NASA Astrophysics Data System (ADS)

    Zhang, Nannan; Zhou, Kefa; Du, Xishihui

    2017-04-01

    Mineral prospectivity mapping (MPM) is a multi-step process that ranks promising target areas for further exploration. Fuzzy logic and fuzzy analytical hierarchy process (AHP) are knowledge-driven MPM approaches. In this study, both approaches were used for data processing, based on which MPM was performed for porphyry and hydrothermal vein copper deposits in the Dananhu-Tousuquan island arc, Xinjiang. The results of the two methods were then compared. The two methods combined expert experience and the Studentized contrast (S(C)) values of the weights-of-evidence approach to calculate the weights of 15 layers, and these layers were then integrated by the gamma operator (γ). Through prediction-area (P-A) plot analysis, the optimal γ for fuzzy logic and fuzzy AHP was determined as 0.95 and 0.93, respectively. The thresholds corresponding to different levels of metallogenic probability were defined via concentration-area (C-A) fractal analysis. The prediction performances of the two methods were compared on this basis. The results showed that in MPM based on fuzzy logic, the area under the receiver operating characteristic (ROC) curve was 0.806 and 81.48% of the known deposits were predicted, whereas in MPM based on fuzzy AHP, the area under the ROC curve was 0.862 and 92.59% of the known deposits were predicted. Therefore, prediction based on fuzzy AHP is more accurate and can provide directions for future prospecting.

  7. Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states

    NASA Astrophysics Data System (ADS)

    Kish, Laszlo B.

    2009-03-01

    A new type of deterministic (non-probabilistic) computer logic system inspired by the stochasticity of brain signals is shown. The distinct values are represented by independent stochastic processes: independent voltage (or current) noises. The orthogonality of these processes provides a natural way to construct binary or multi-valued logic circuitry with arbitrary number N of logic values by using analog circuitry. Moreover, the logic values on a single wire can be made a (weighted) superposition of the N distinct logic values. Fuzzy logic is also naturally represented by a two-component superposition within the binary case ( N=2). Error propagation and accumulation are suppressed. Other relevant advantages are reduced energy dissipation and leakage current problems, and robustness against circuit noise and background noises such as 1/f, Johnson, shot and crosstalk noise. Variability problems are also non-existent because the logic value is an AC signal. A similar logic system can be built with orthogonal sinusoidal signals (different frequency or orthogonal phase) however that has an extra 1/N type slowdown compared to the noise-based logic system with increasing number of N furthermore it is less robust against time delay effects than the noise-based counterpart.

  8. Quantifying favorableness for occurrence of a mineral deposit type using fuzzy logic; an example from Arizona

    USGS Publications Warehouse

    Gettings, Mark E.; Bultman, Mark W.

    1993-01-01

    An application of possibility theory from fuzzy logic to the quantification of favorableness for quartz-carbonate vein deposits in the southern Santa Rita Mountains of southeastern Arizona is described. Three necessary but probably not sufficient conditions for the formation of these deposits were defined as the occurrence of carbonate berain rocks within hypabyssal depths, significant fracturing of the rocks, and proximity to a felsic intrusive. The quality of data available to evaluate these conditions is variable over the study area. The possibility of each condition was represented as a fuzzy set enumerated over the area. The intersection of the sets measures the degree of simultaneous occurrence of hte necessary factors and provides a measure of the possibility of deposit occurrence. Using fuzzy set technicques, the effect of one or more fuzzy sets relative to the others in the intersection can be controlled and logical combinations of the sets can be used to impose a time sequential constraint on the necessary conditions. Other necessary conditions, and supplementary conditions such as variable data quality or intensity of exploration can be included in the analysis by their proper representation as fuzzy sets.

  9. A few remarks on efficiency of embedding of a classical mathematical problem into fuzzy logical environment

    NASA Astrophysics Data System (ADS)

    Včelař, František; Pátíková, Zuzana

    2017-07-01

    For the case of classical Tarski's theorem on fixed points of isotone maps we show that embedding of this statement into fuzzy logical environment leads to surprising results, which cannot be easily seen and awaited in classical logical environment.

  10. On-line fuzzy logic control of tube bending

    NASA Astrophysics Data System (ADS)

    Lieh, Junghsen; Li, Wei Jie

    2005-11-01

    This paper describes the simulation and on-line fuzzy logic control of tube bending. By combining elasticity and plasticity theories, a conventional model was developed. The results from simulation were compared with those obtained from testing. The experimental data reveal that there exists certain level of uncertainty and nonlinearity in tube bending, and its variation could be significant. To overcome this, a on-line fuzzy logic controller with self-tuning capabilities was designed. The advantages of this on-line system are (1) its computational requirement is simple in comparison with more algorithmic-based controllers, and (2) the system does not need prior knowledge of material characteristics. The device includes an AC motor, a servo controller, a forming mechanism, a 3D optical sensor, and a microprocessor. This automated bending machine adopts primary and secondary errors between the actual response and desired output to conduct on-line rule reasoning. Results from testing show that the spring back angle can be effectively compensated by the self- tuning fuzzy system in a real-time fashion.

  11. Motion Control of the Soccer Robot Based on Fuzzy Logic

    NASA Astrophysics Data System (ADS)

    Coman, Daniela; Ionescu, Adela

    2009-08-01

    Robot soccer is a challenging platform for multi-agent research, involving topics such as real-time image processing and control, robot path planning, obstacle avoidance and machine learning. The conventional robot control consists of methods for path generation and path following. When a robot moves away the estimated path, it must return immediately, and while doing so, the obstacle avoidance behavior and the effectiveness of such a path are not guaranteed. So, motion control is a difficult task, especially in real time and high speed control. This paper describes the use of fuzzy logic control for the low level motion of a soccer robot. Firstly, the modelling of the soccer robot is presented. The soccer robot based on MiroSoT Small Size league is a differential-drive mobile robot with non-slipping and pure-rolling. Then, the design of fuzzy controller is describes. Finally, the computer simulations in MATLAB Simulink show that proposed fuzzy logic controller works well.

  12. Integration of fuzzy logic and image analysis for the detection of gullies in the Calhoun critical zone observatory using airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Bastola, S.; Noto, L. V.; Dialynas, Y. G.; Bras, R. L.

    2015-12-01

    The entire Piedmont of the Southeastern United States, where the Calhoun Critical Zone Observatory (CCZO) is located, experienced one of the most severe erosive events in the United States during last two centuries. Forested areas were cleared to cultivate cotton, tobacco and other crops during the nineteenth and early twentieth century and these land use change, together with intense rainfalls, initiated deep gullying. An accurate mapping of these landforms is important since, despite some gully stabilization and reforestation efforts, gullies are still major contributors of sediment to streams. Mapping gullies in the CCZO area is hindered by the presence of dense canopy which precludes the identification through aerial photogrammetry and other traditional remote sensing methods. Moreover, the wide spatial extent of the gullies makes detailed field surveys, for the identification and characterization of entire gullies, a very large and expensive proposition. This work aims to develop and assess an automated set of algorithms to detect and map gullies using morphological characteristics retrieved by very high resolution imagery (VHRI). A one-meter resolution LiDAR DEM is used to derive different morphometric indices whose combination, carried out using spatial analysis methods and fuzzy logic rules, are a tool to identify gullies. This spatial model has been calibrated using the reference perimeters of two gullies that we measured during a recent field survey. The entire procedure attempts to provide estimates of gully erosion patterns, which characterize the entire Calhoun CZO area and to develop and evaluate a method to measure characteristic features of gullies (i.e. depth and volume).

  13. Integration of fuzzy logic and image analysis for the detection of gullies in the Calhoun Critical Zone Observatory using airborne LiDAR data

    NASA Astrophysics Data System (ADS)

    Noto, Leonardo V.; Bastola, Satish; Dialynas, Yannis G.; Arnone, Elisa; Bras, Rafael L.

    2017-04-01

    The entire Piedmont of the Southeastern United States, where the Calhoun Critical Zone Observatory (CCZO) is located, experienced one of the most severe erosive events of the last two centuries. Forested areas were cleared to cultivate cotton, tobacco, and other crops during the nineteenth and early twentieth century and these land use changes, together with intense rainfalls, initiated deep gullying. An accurate mapping of these landforms is important since, despite some gully stabilization and reforestation efforts, gullies are still major contributors of sediment to streams. Mapping gullies in the CCZO area is hindered by the presence of dense canopy, which precludes the identification through aerial photogrammetry and other traditional remote sensing methods. Moreover, the wide spatial extent of the gullies makes the identification and characterization of entire gullies through field surveys a very large and expensive proposition. This work aims to develop a methodology to automatically detect and map gullies based on a set of algorithms and morphological characteristics retrieved by very high resolution (VHR) imagery. A one-meter resolution LiDAR Digital Elevation Model (DEM) is used to derive different morphometric indices, which are combined by using spatial analysis methods and fuzzy logic rules, building up a tool able to automatically identify gullies. This spatial model has been calibrated using, as reference, the perimeters of two relatively large gullies that have been measured during a recent field survey. The entire procedure aims to provide estimates of gully erosion patterns, which characterize the entire CCZO area, and to develop an objective method to measure characteristic features of gullies (i.e., depth and volume).

  14. Segmentation method of eye region based on fuzzy logic system for classifying open and closed eyes

    NASA Astrophysics Data System (ADS)

    Kim, Ki Wan; Lee, Won Oh; Kim, Yeong Gon; Hong, Hyung Gil; Lee, Eui Chul; Park, Kang Ryoung

    2015-03-01

    The classification of eye openness and closure has been researched in various fields, e.g., driver drowsiness detection, physiological status analysis, and eye fatigue measurement. For a classification with high accuracy, accurate segmentation of the eye region is required. Most previous research used the segmentation method by image binarization on the basis that the eyeball is darker than skin, but the performance of this approach is frequently affected by thick eyelashes or shadows around the eye. Thus, we propose a fuzzy-based method for classifying eye openness and closure. First, the proposed method uses I and K color information from the HSI and CMYK color spaces, respectively, for eye segmentation. Second, the eye region is binarized using the fuzzy logic system based on I and K inputs, which is less affected by eyelashes and shadows around the eye. The combined image of I and K pixels is obtained through the fuzzy logic system. Third, in order to reflect the effect by all the inference values on calculating the output score of the fuzzy system, we use the revised weighted average method, where all the rectangular regions by all the inference values are considered for calculating the output score. Fourth, the classification of eye openness or closure is successfully made by the proposed fuzzy-based method with eye images of low resolution which are captured in the environment of people watching TV at a distance. By using the fuzzy logic system, our method does not require the additional procedure of training irrespective of the chosen database. Experimental results with two databases of eye images show that our method is superior to previous approaches.

  15. Challenges And Results of the Applications of Fuzzy Logic in the Classification of Rich Galaxy Clusters

    NASA Astrophysics Data System (ADS)

    Santiago Girola Schneider, Rafael

    2015-08-01

    The fuzzy logic is a branch of the artificial intelligence founded on the concept that 'everything is a matter of degree.' It intends to create mathematical approximations on the resolution of certain types of problems. In addition, it aims to produce exact results obtained from imprecise data, for which it is particularly useful for electronic and computer applications. This enables it to handle vague or unspecific information when certain parts of a system are unknown or ambiguous and, therefore, they cannot be measured in a reliable manner. Also, when the variation of a variable can produce an alteration on the others.The main focus of this paper is to prove the importance of these techniques formulated from a theoretical analysis on its application on ambiguous situations in the field of the rich clusters of galaxies. The purpose is to show its applicability in the several classification systems proposed for the rich clusters, which are based on criteria such as the level of richness of the cluster, the distribution of the brightest galaxies, whether there are signs of type-cD galaxies or not or the existence of sub-clusters.Fuzzy logic enables the researcher to work with “imprecise” information implementing fuzzy sets and combining rules to define actions. The control systems based on fuzzy logic join input variables that are defined in terms of fuzzy sets through rule groups that produce one or several output values of the system under study. From this context, the application of the fuzzy logic’s techniques approximates the solution of the mathematical models in abstractions about the rich galaxy cluster classification of physical properties in order to solve the obscurities that must be confronted by an investigation group in order to make a decision.

  16. Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation.

    PubMed

    Omrane, Hajer; Masmoudi, Mohamed Slim; Masmoudi, Mohamed

    This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path.

  17. Fuzzy Logic Based Control for Autonomous Mobile Robot Navigation

    PubMed Central

    Masmoudi, Mohamed Slim; Masmoudi, Mohamed

    2016-01-01

    This paper describes the design and the implementation of a trajectory tracking controller using fuzzy logic for mobile robot to navigate in indoor environments. Most of the previous works used two independent controllers for navigation and avoiding obstacles. The main contribution of the paper can be summarized in the fact that we use only one fuzzy controller for navigation and obstacle avoidance. The used mobile robot is equipped with DC motor, nine infrared range (IR) sensors to measure the distance to obstacles, and two optical encoders to provide the actual position and speeds. To evaluate the performances of the intelligent navigation algorithms, different trajectories are used and simulated using MATLAB software and SIMIAM navigation platform. Simulation results show the performances of the intelligent navigation algorithms in terms of simulation times and travelled path. PMID:27688748

  18. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

  19. Intelligent Paging Based Mobile User Tracking Using Fuzzy Logic

    NASA Astrophysics Data System (ADS)

    Saha, Sajal; Dutta, Raju; Debnath, Soumen; Mukhopadhyay, Asish K.

    2010-11-01

    In general, a mobile user travels in a predefined path that depends mostly on the user's characteristics. Thus, tracking the locations of a mobile user is one of the challenges for location management. In this paper, we introduce a movement pattern learning strategy system to track the user's movements using adaptive fuzzy logic. Our fuzzy inference system extracts patterns from the historical data record of the cell numbers along with the date and time stamp of the users occupying the cell. Implementation of this strategy has been evaluated with the real time user data which proves the efficiency and accuracy of the model. This mechanism not only reduces user location tracking costs, but also significantly decreases the call-loss rates and average paging delays.

  20. A reinforcement learning-based architecture for fuzzy logic control

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.

    1992-01-01

    This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.

  1. A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management

    NASA Technical Reports Server (NTRS)

    Wu, G. Gordon

    1995-01-01

    Due to the complexity of future space missions and the large amount of data involved, greater autonomy in data processing is demanded for mission operations, training, and vehicle health management. In this paper, we develop a fuzzy logic intelligent diagnostic system to perform data reduction, data analysis, and fault diagnosis for spacecraft vehicle health management applications. The diagnostic system contains a data filter and an inference engine. The data filter is designed to intelligently select only the necessary data for analysis, while the inference engine is designed for failure detection, warning, and decision on corrective actions using fuzzy logic synthesis. Due to its adaptive nature and on-line learning ability, the diagnostic system is capable of dealing with environmental noise, uncertainties, conflict information, and sensor faults.

  2. Fuzzy Logic Determination of Lithologies from Well Log Data: Application to the KTB Project Data set (Germany)

    NASA Astrophysics Data System (ADS)

    Bosch, David; Ledo, Juanjo; Queralt, Pilar

    2013-07-01

    Fuzzy logic has been used for lithology prediction with remarkable success. Several techniques such as fuzzy clustering or linguistic reasoning have proven to be useful for lithofacies determination. In this paper, a fuzzy inference methodology has been implemented as a MATLAB routine and applied for the first time to well log data from the German Continental Deep Drilling Program (KTB). The training of the fuzzy inference system is based on the analysis of the multi-class Matthews correlation coefficient computed for the classification matrix. For this particular data set, we have found that the best suited membership function type is the piecewise linear interpolation of the normalized histograms; that the best combination operator for obtaining the final lithology degrees of membership is the fuzzy gamma operator; and that all the available properties are relevant in the classification process. Results show that this fuzzy logic-based method is suited for rapidly and reasonably suggesting a lithology column from well log data, neatly identifying the main units and in some cases refining the classification, which can lead to a better interpretation. We have tested the trained system with synthetic data generated from property value distributions of the training data set to find that the differences in data distributions between both wells are significant enough to misdirect the inference process. However, a cross-validation analysis has revealed that, even with differences between data distributions and missing lithologies in the training data set, this fuzzy logic inference system is able to output a coherent classification.

  3. Development of Fuzzy Logic and Soft Computing Methodologies

    NASA Technical Reports Server (NTRS)

    Zadeh, L. A.; Yager, R.

    1999-01-01

    Our earlier research on computing with words (CW) has led to a new direction in fuzzy logic which points to a major enlargement of the role of natural languages in information processing, decision analysis and control. This direction is based on the methodology of computing with words and embodies a new theory which is referred to as the computational theory of perceptions (CTP). An important feature of this theory is that it can be added to any existing theory - especially to probability theory, decision analysis, and control - and enhance the ability of the theory to deal with real-world problems in which the decision-relevant information is a mixture of measurements and perceptions. The new direction is centered on an old concept - the concept of a perception - a concept which plays a central role in human cognition. The ability to reason with perceptions perceptions of time, distance, force, direction, shape, intent, likelihood, truth and other attributes of physical and mental objects - underlies the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Everyday examples of such tasks are parking a car, driving in city traffic, cooking a meal, playing golf and summarizing a story. Perceptions are intrinsically imprecise. Imprecision of perceptions reflects the finite ability of sensory organs and ultimately, the brain, to resolve detail and store information. More concretely, perceptions are both fuzzy and granular, or, for short, f-granular. Perceptions are f-granular in the sense that: (a) the boundaries of perceived classes are not sharply defined; and (b) the elements of classes are grouped into granules, with a granule being a clump of elements drawn together by indistinguishability, similarity. proximity or functionality. F-granularity of perceptions may be viewed as a human way of achieving data compression. In large measure, scientific progress has been, and continues to be

  4. Advances In Infection Surveillance and Clinical Decision Support With Fuzzy Sets and Fuzzy Logic.

    PubMed

    Koller, Walter; de Bruin, Jeroen S; Rappelsberger, Andrea; Adlassnig, Klaus-Peter

    2015-01-01

    By the use of extended intelligent information technology tools for fully automated healthcare-associated infection (HAI) surveillance, clinicians can be informed and alerted about the emergence of infection-related conditions in their patients. Moni--a system for monitoring nosocomial infections in intensive care units for adult and neonatal patients--employs knowledge bases that were written with extensive use of fuzzy sets and fuzzy logic, allowing the inherent un-sharpness of clinical terms and the inherent uncertainty of clinical conclusions to be a part of Moni's output. Thus, linguistic as well as propositional uncertainty became a part of Moni, which can now report retrospectively on HAIs according to traditional crisp HAI surveillance definitions, as well as support clinical bedside work by more complex crisp and fuzzy alerts and reminders. This improved approach can bridge the gap between classical retrospective surveillance of HAIs and ongoing prospective clinical-decision-oriented HAI support.

  5. The Application of Fuzzy Logic to SAFOR in SIMNET

    DTIC Science & Technology

    1994-02-01

    official position of ay Go@ met Agncy P,*bc 6e.ent burde for two 00oh of w4off.oon to .. otemed Wo sego. I how pt sreotpons. idnci g "Wne s . o. womoig...1993 4. TITE AND SUBTITLE 5. FUNDING NUMBERS The Application of Fuzzy Logic to SAFOR in SIMNET IDA Central Research Project 6. AUTHOR( S ) Irvin W. Kay...Bohdan Balko 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) S . PERFORMING ORGANIZATION Institute for Defense AnalysesREOTNM R 1801 N. Beauregard St

  6. FUZZY LOGIC CONTROL OF ELECTRIC MOTORS AND MOTOR DRIVES: FEASIBILITY STUDY

    EPA Science Inventory

    The report gives results of a study (part 1) of fuzzy logic motor control (FLMC). The study included: 1) reviews of existing applications of fuzzy logic, of motor operation, and of motor control; 2) a description of motor control schemes that can utilize FLMC; 3) selection of a m...

  7. FUZZY LOGIC CONTROL OF ELECTRIC MOTORS AND MOTOR DRIVES: FEASIBILITY STUDY

    EPA Science Inventory

    The report gives results of a study (part 1) of fuzzy logic motor control (FLMC). The study included: 1) reviews of existing applications of fuzzy logic, of motor operation, and of motor control; 2) a description of motor control schemes that can utilize FLMC; 3) selection of a m...

  8. Fuzzy logic application for modeling man-in-the-loop space shuttle proximity operations. M.S. Thesis - MIT

    NASA Technical Reports Server (NTRS)

    Brown, Robert B.

    1994-01-01

    A software pilot model for Space Shuttle proximity operations is developed, utilizing fuzzy logic. The model is designed to emulate a human pilot during the terminal phase of a Space Shuttle approach to the Space Station. The model uses the same sensory information available to a human pilot and is based upon existing piloting rules and techniques determined from analysis of human pilot performance. Such a model is needed to generate numerous rendezvous simulations to various Space Station assembly stages for analysis of current NASA procedures and plume impingement loads on the Space Station. The advantages of a fuzzy logic pilot model are demonstrated by comparing its performance with NASA's man-in-the-loop simulations and with a similar model based upon traditional Boolean logic. The fuzzy model is shown to respond well from a number of initial conditions, with results typical of an average human. In addition, the ability to model different individual piloting techniques and new piloting rules is demonstrated.

  9. Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations.

    PubMed

    Trnka, Hjalte; Wu, Jian X; Van De Weert, Marco; Grohganz, Holger; Rantanen, Jukka

    2013-12-01

    Freeze-drying of peptide and protein-based pharmaceuticals is an increasingly important field of research. The diverse nature of these compounds, limited understanding of excipient functionality, and difficult-to-analyze quality attributes together with the increasing importance of the biosimilarity concept complicate the development phase of safe and cost-effective drug products. To streamline the development phase and to make high-throughput formulation screening possible, efficient solutions for analyzing critical quality attributes such as cake quality with minimal material consumption are needed. The aim of this study was to develop a fuzzy logic system based on image analysis (IA) for analyzing cake quality. Freeze-dried samples with different visual quality attributes were prepared in well plates. Imaging solutions together with image analytical routines were developed for extracting critical visual features such as the degree of cake collapse, glassiness, and color uniformity. On the basis of the IA outputs, a fuzzy logic system for analysis of these freeze-dried cakes was constructed. After this development phase, the system was tested with a new screening well plate. The developed fuzzy logic-based system was found to give comparable quality scores with visual evaluation, making high-throughput classification of cake quality possible. © 2013 Wiley Periodicals, Inc. and the American Pharmacists Association.

  10. Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 1

    NASA Technical Reports Server (NTRS)

    Lea, Robert N. (Editor); Villarreal, James (Editor)

    1991-01-01

    Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by NASA and the University of Houston, Clear Lake. The workshop was held April 11 to 13 at the Johnson Space Flight Center. Technical topics addressed included adaptive systems, learning algorithms, network architectures, vision, robotics, neurobiological connections, speech recognition and synthesis, fuzzy set theory and application, control and dynamics processing, space applications, fuzzy logic and neural network computers, approximate reasoning, and multiobject decision making.

  11. Estimating outcomes in newborn infants using fuzzy logic

    PubMed Central

    Chaves, Luciano Eustáquio; Nascimento, Luiz Fernando C.

    2014-01-01

    OBJECTIVE: To build a linguistic model using the properties of fuzzy logic to estimate the risk of death of neonates admitted to a Neonatal Intensive Care Unit. METHODS: Computational model using fuzzy logic. The input variables of the model were birth weight, gestational age, 5th-minute Apgar score and inspired fraction of oxygen in newborn infants admitted to a Neonatal Intensive Care Unit of Taubaté, Southeast Brazil. The output variable was the risk of death, estimated as a percentage. Three membership functions related to birth weight, gestational age and 5th-minute Apgar score were built, as well as two functions related to the inspired fraction of oxygen; the risk presented five membership functions. The model was developed using the Mandani inference by means of Matlab(r) software. The model values were compared with those provided by experts and their performance was estimated by ROC curve. RESULTS: 100 newborns were included, and eight of them died. The model estimated an average possibility of death of 49.7±29.3%, and the possibility of hospital discharge was 24±17.5%. These values are different when compared by Student's t-test (p<0.001). The correlation test revealed r=0.80 and the performance of the model was 81.9%. CONCLUSIONS: This predictive, non-invasive and low cost model showed a good accuracy and can be applied in neonatal care, given the easiness of its use. PMID:25119746

  12. Automated maneuver planning using a fuzzy logic algorithm

    NASA Technical Reports Server (NTRS)

    Conway, D.; Sperling, R.; Folta, D.; Richon, K.; Defazio, R.

    1994-01-01

    Spacecraft orbital control requires intensive interaction between the analyst and the system used to model the spacecraft trajectory. For orbits with right mission constraints and a large number of maneuvers, this interaction is difficult or expensive to accomplish in a timely manner. Some automation of maneuver planning can reduce these difficulties for maneuver-intensive missions. One approach to this automation is to use fuzzy logic in the control mechanism. Such a prototype system currently under development is discussed. The Tropical Rainfall Measurement Mission (TRMM) is one of several missions that could benefit from automated maneuver planning. TRMM is scheduled for launch in August 1997. The spacecraft is to be maintained in a 350-km circular orbit throughout the 3-year lifetime of the mission, with very small variations in this orbit allowed. Since solar maximum will occur as early as 1999, the solar activity during the TRMM mission will be increasing. The increasing solar activity will result in orbital maneuvers being performed as often as every other day. The results of automated maneuver planning for the TRMM mission will be presented to demonstrate the prototype of the fuzzy logic tool.

  13. Fuzzy logic anti-skid control for commercial trucks

    NASA Astrophysics Data System (ADS)

    Akey, Mark L.

    1995-06-01

    A fuzzy logic (FL) anti-skid brake controller (ABS) is proposed as the next generation ABS replacing current generation finite state (FS) control. The FL controller is part of a commercial truck braking system, encompassing reverse front-back braking proportions on an articulated vehicle as compared to that found on fixed, passenger car systems. In this early research, the FL controller must satisfy three goals. The first goal is to produce superior braking distances over that of the finite state controller, specifically under low (mu) conditions. The second goal is to provide superior braking under varying system conditions (road surface conditions, physical brake parameters, wheel velocity sensor parameters). The third goal is to provide a convenient, flexible, and tractable ABS solution which is amenable to redevelopemnt to different vehicular platforms. Monte Carlo simulation results illustrate stopping distance improvements of 5 to 10 % averaged over all (mu) surfaces for varying wheel loads. On low (mu) surfaces, the improvement increases to 15% (up to a full tractor-trailer length). These results are obtained while varying other system parameters demonstrating robustness. Finally, the fuzzy logic rule sets and the overall configuration illustrate a straight-forward design and maturation process for the rule sets.

  14. A Grey Fuzzy Logic Approach for Cotton Fibre Selection

    NASA Astrophysics Data System (ADS)

    Chakraborty, Shankar; Das, Partha Protim; Kumar, Vidyapati

    2017-06-01

    It is a well known fact that the quality of ring spun yarn predominantly depends on various physical properties of cotton fibre. Any variation in these fibre properties may affect the strength and unevenness of the final yarn. Thus, so as to achieve the desired yarn quality and characteristics, it becomes imperative for the spinning industry personnel to identify the most suitable cotton fibre from a set of feasible alternatives in presence of several conflicting properties/attributes. This cotton fibre selection process can be modelled as a Multi-Criteria Decision Making (MCDM) problem. In this paper, a grey fuzzy logic-based approach is proposed for selection of the most apposite cotton fibre from 17 alternatives evaluated based on six important fibre properties. It is observed that the preference order of the top-ranked cotton fibres derived using the grey fuzzy logic approach closely matches with that attained by the past researchers which proves the application potentiality of this method in solving varying MCDM problems in textile industries.

  15. Estimating outcomes in newborn infants using fuzzy logic.

    PubMed

    Chaves, Luciano Eustáquio; Nascimento, Luiz Fernando C

    2014-06-01

    To build a linguistic model using the properties of fuzzy logic to estimate the risk of death of neonates admitted to a Neonatal Intensive Care Unit. Computational model using fuzzy logic. The input variables of the model were birth weight, gestational age, 5th-minute Apgar score and inspired fraction of oxygen in newborn infants admitted to a Neonatal Intensive Care Unit of Taubaté, Southeast Brazil. The output variable was the risk of death, estimated as a percentage. Three membership functions related to birth weight, gestational age and 5th-minute Apgar score were built, as well as two functions related to the inspired fraction of oxygen; the risk presented five membership functions. The model was developed using the Mandani inference by means of Matlab(r) software. The model values were compared with those provided by experts and their performance was estimated by ROC curve. 100 newborns were included, and eight of them died. The model estimated an average possibility of death of 49.7±29.3%, and the possibility of hospital discharge was 24±17.5%. These values are different when compared by Student's t-test (p<0.001). The correlation test revealed r=0.80 and the performance of the model was 81.9%. This predictive, non-invasive and low cost model showed a good accuracy and can be applied in neonatal care, given the easiness of its use.

  16. Toward a fuzzy logic control of the infant incubator.

    PubMed

    Reddy, Narender P; Mathur, Garima; Hariharan, S I

    2009-10-01

    Premature birth is a world wide problem. Thermo regulation is a major problem in premature infants. Premature infants are often kept in infant incubators providing convective heating. Currently either the incubator air temperature is sensed and used to control the heat flow, or infant's skin temperature is sensed and used in the close loop control. Skin control often leads to large fluctuations in the incubator air temperature. Air control also leads to skin temperature fluctuations. The question remains if both the infant's skin temperature and the incubator air temperature can be simultaneously used in the control. The purpose of the present study was to address this question by developing a fuzzy logic control which incorporates both incubator air temperature and infant's skin temperature to control the heating. The control was evaluated using a lumped parameter mathematical model of infant-incubator system (Simon, B. N., N. P. Reddy, and A. Kantak, J. Biomech. Eng. 116:263-266, 1994). Simulation results confirmed previous experimental results that the on-off skin control could lead to fluctuations in the incubator air temperature, and the air control could lead to too slow rise time in the core temperature. The fuzzy logic provides a smooth control with the desired rise time.

  17. Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering

    NASA Astrophysics Data System (ADS)

    Panomruttanarug, Benjamas; Higuchi, Kohji

    This paper presents an emulation of fuzzy logic control schemes for an autonomous parallel parking system in a backward maneuver. There are four infrared sensors sending the distance data to a microcontroller for generating an obstacle-free parking path. Two of them mounted on the front and rear wheels on the parking side are used as the inputs to the fuzzy rules to calculate a proper steering angle while backing. The other two attached to the front and rear ends serve for avoiding collision with other cars along the parking space. At the end of parking processes, the vehicle will be in line with other parked cars and positioned in the middle of the free space. Fuzzy rules are designed based upon a wall following process. Performance of the infrared sensors is improved using Kalman filtering. The design method needs extra information from ultrasonic sensors. Starting from modeling the ultrasonic sensor in 1-D state space forms, one makes use of the infrared sensor as a measurement to update the predicted values. Experimental results demonstrate the effectiveness of sensor improvement.

  18. Fuzzy Logic Model for Multi-Reservoir Operation

    NASA Astrophysics Data System (ADS)

    Mohan, S.; Prasad, M. Anjaneya

    Reservoirs are built usually to serve multiple purposes, viz. irrigation, municipal and industrial water supply, hydro-power and flood control. Because of high variability of annual rainfall and conflicting demands on scarce water resources, the study and operation of reservoir systems has assumed great significance to meet the short and long-term requirements. The reservoir managers do not find previous techniques of complex optimization models are difficult to adopt practically. New methods have to be developed, which are simple to understand and can be effectively adopted for the existing systems. Artificial intelligent tools like Genetic programming, Neural Networks and fuzzy logic methods are increasingly becoming popular in Water resources applications. The fuzzy-rule-based systems are very much suitable for inferring developed operating policies. In the present work a rule-based Fuzzy model is attempted for long-term operation a multi-reservoir system. The model was developed on monthly basis for operation and the model was demonstrated with a case of two serial reservoirs on River Godavari sub-system located in South India. The uniqueness of the present paper is that the model was developed based on the historical operation so that the model may be acceptable to reservoir managers, since the departmental expertise was the basis for the model development. The performance of the model was tested with both calibrated and validation periods. The performance of model during two crop seasons of year was reported.

  19. Analysis and prediction of aperiodic hydrodynamic oscillatory time series by feed-forward neural networks, fuzzy logic, and a local nonlinear predictor

    SciTech Connect

    Gentili, Pier Luigi; Gotoda, Hiroshi; Dolnik, Milos; Epstein, Irving R.

    2015-01-15

    Forecasting of aperiodic time series is a compelling challenge for science. In this work, we analyze aperiodic spectrophotometric data, proportional to the concentrations of two forms of a thermoreversible photochromic spiro-oxazine, that are generated when a cuvette containing a solution of the spiro-oxazine undergoes photoreaction and convection due to localized ultraviolet illumination. We construct the phase space for the system using Takens' theorem and we calculate the Lyapunov exponents and the correlation dimensions to ascertain the chaotic character of the time series. Finally, we predict the time series using three distinct methods: a feed-forward neural network, fuzzy logic, and a local nonlinear predictor. We compare the performances of these three methods.

  20. Utility of Arden Syntax for Representation of Fuzzy Logic in Clinical Quality Measures.

    PubMed

    Jenders, Robert A

    2015-01-01

    Prior work has established that fuzzy logic is prevalent in clinical practice guidelines and that Arden Syntax is suitable for representing clinical quality measures (CQMs). Approved since then, Arden Syntax v2.9 (2012) has formal constructs for fuzzy logic even as new formalisms are proposed to represent quality logic. Determine the prevalence of fuzzy logic in CQMs and assess the utility of a contemporary version of Arden Syntax for representing them. Linguistic variables were tabulated in the 329 Assessing Care of the Vulnerable Elderly (ACOVE-3) CQMs, and these logic statements were encoded in Arden Syntax. In a total of 392 CQMs, linguistic variables occurred in 30.6%, and Arden Syntax could be used to represent these formally. Fuzzy logic occurs commonly in CQMs, and Arden Syntax offers particular utility for the representations of these constructs.

  1. Application of the removal of pollutants from textile industry wastewater in constructed wetlands using fuzzy logic.

    PubMed

    Dogdu, Gamze; Yalcuk, Arda; Postalcioglu, Seda

    2017-02-01

    There are more than a hundred textile industries in Turkey that discharge large quantities of dye-rich wastewater, resulting in water pollution. Such effluents must be treated to meet discharge limits imposed by the Water Framework Directive in Turkey. Industrial treatment facilities must be required to monitor operations, keep them cost-effective, prevent operational faults, discharge-limit infringements, and water pollution. This paper proposes the treatment of actual textile wastewater by vertical flow constructed wetland (VFCW) systems operation and monitoring effluent wastewater quality using fuzzy logic with a graphical user interface. The treatment performance of VFCW is investigated in terms of chemical oxygen demand and ammonium nitrogen (NH4-N) content, color, and pH parameters during a 75-day period of operation. A computer program was developed with a fuzzy logic system (a decision- making tool) to graphically present (via a status analysis chart) the quality of treated textile effluent in relation to the Turkish Water Pollution Control Regulation. Fuzzy logic is used in the evaluation of data obtained from the VFCW systems and for notification of critical states exceeding the discharge limits. This creates a warning chart that reports any errors encountered in a reactor during the collection of any sample to the concerned party.

  2. PSIDD3: Post-Scan Ultrasonic Data Display System for the Windows-Based PC Including Fuzzy Logic Analysis

    NASA Technical Reports Server (NTRS)

    Lovelace, Jeffrey J.; Cios, Krzysztof J.; Roth, Don J.; Cao, Wei

    2000-01-01

    Post-Scan Interactive Data Display (PSIDD) III is a user-oriented Windows-based system that facilitates the display and comparison of ultrasonic contact data. The system is optimized to compare ultrasonic measurements made at different locations within a material or at different stages of material degradation. PSIDD III provides complete analysis of the primary wave forms in the time and frequency domains along with the calculation of several frequency dependent properties including Phase Velocity and Attenuation Coefficient and several frequency independent properties, like the Cross Correlation Velocity. The system allows image generation on all of the frequency dependent properties at any available frequency (limited by the bandwidth used in the scans) and on any of the frequency independent properties. From ultrasonic contact scans, areas of interest on an image can be studied with regard to underlying raw waveforms and derived ultrasonic properties by simply selecting the point on the image. The system offers various modes of in-depth comparison between scan points. Up to five scan points can be selected for comparative analysis at once. The system was developed with Borland Delphi software (Visual Pascal) and is based on a SQL database. It is ideal for classification of material properties, or location of microstructure variations in materials.

  3. Landslide Susceptibility Assessment Through Fuzzy Logic Inference System (flis)

    NASA Astrophysics Data System (ADS)

    Bibi, T.; Gul, Y.; Rahman, A. Abdul; Riaz, M.

    2016-09-01

    Landslide is among one of the most important natural hazards that lead to modification of the environment. It is a regular feature of a rapidly growing district Mansehra, Pakistan. This caused extensive loss of life and property in the district located at the foothills of Himalaya. Keeping in view the situation it is concluded that besides structural approaches the non-structural approaches such as hazard and risk assessment maps are effective tools to reduce the intensity of damage. A landslide susceptibility map is base for engineering geologists and geomorphologists. However, it is not easy to produce a reliable susceptibility map due to complex nature of landslides. Since 1980s, several mathematical models have been developed to map landslide susceptibility and hazard. Among various models this paper is discussing the effectiveness of fuzzy logic approach for landslide susceptibility mapping in District Mansehra, Pakistan. The factor maps were modified as landslide susceptibility and fuzzy membership functions were assessed for each class. Likelihood ratios are obtained for each class of contributing factors by considering the expert opinion. The fuzzy operators are applied to generate landslide susceptibility maps. According to this map, 17% of the study area is classified as high susceptibility, 32% as moderate susceptibility, 51% as low susceptibility and areas. From the results it is found that the fuzzy model can integrate effectively with various spatial data for landslide hazard mapping, suggestions in this study are hope to be helpful to improve the applications including interpretation, and integration phases in order to obtain an accurate decision supporting layer.

  4. Contrast of evolution models for agricultural contaminants in ground waters by means of fuzzy logic and data mining

    NASA Astrophysics Data System (ADS)

    Andujar, J. M.; Aroba, J.; de Torre, M. L. La; Grande, J. A.

    2006-01-01

    This work aims at contrasting, by means of a set of fuzzy logic- and data mining-based algorithms, the functioning model of a detritic aquifer undergoing overexploitation and nitrate excess input coming from strawberry and citrus intensive crops in its recharge zone. To provide researchers unskilled in data mining techniques with an easy and intuitive interpretation, the authors have developed a computer tool based on fuzzy logic that allows immediate qualitative analysis of the data contained in a data mass from the water chemical analyses, and serves as a contrast to functioning models previously proposed with classical statistics.

  5. Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions

    NASA Technical Reports Server (NTRS)

    Zadeh, L. A.

    1985-01-01

    A fuzzy syllogism in fuzzy logic is defined to be an inference schema in which the major premise, the minor premise and the conclusion are propositions containing fuzzy quantifiers. A basic fuzzy syllogism in fuzzy logic is the intersection/product syllogism. Several other basic syllogisms are developed that may be employed as rules of combination of evidence in expert systems. Among these is the consequent conjunction syllogism. Furthermore, it is shown that syllogistic reasoning in fuzzy logic provides a basis for reasoning with dispositions; that is, with propositions that are preponderantly but not necessarily always true. It is also shown that the concept of dispositionality is closely related to the notion of usuality and serves as a basis for what might be called a theory of usuality - a theory which may eventually provide a computational framework for commonsense reasoning.

  6. Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions

    NASA Technical Reports Server (NTRS)

    Zadeh, L. A.

    1985-01-01

    A fuzzy syllogism in fuzzy logic is defined to be an inference schema in which the major premise, the minor premise and the conclusion are propositions containing fuzzy quantifiers. A basic fuzzy syllogism in fuzzy logic is the intersection/product syllogism. Several other basic syllogisms are developed that may be employed as rules of combination of evidence in expert systems. Among these is the consequent conjunction syllogism. Furthermore, it is shown that syllogistic reasoning in fuzzy logic provides a basis for reasoning with dispositions; that is, with propositions that are preponderantly but not necessarily always true. It is also shown that the concept of dispositionality is closely related to the notion of usuality and serves as a basis for what might be called a theory of usuality - a theory which may eventually provide a computational framework for commonsense reasoning.

  7. Qualitative information modeling: The role of fuzzy logic in project economic evaluations

    SciTech Connect

    Warnken, P.G.

    1995-12-31

    Conventional models rely on a precise mathematical formalism to express the quantitative essentials of the system being modeled. In contrast, decisionmakers in the real world employ cognitive skills to process information and arrive at decisions based on judgement and experience. Bridging the gap between the two analytic approaches -- that is, formulating intelligent models -- has met with very limited success using traditional computational methods. The difficulty stems from two problems. First, imprecision, which is the distinguishing feature of qualitative factors, is an information attribute that is not easily computable using the rules of traditional set theory and Boolean (bivalent) logic. Second, cognitive information processing is cumbersome using the numerical rule-based approaches common in today`s expert systems. Fuzzy models overcome these problems by employing new mathematical rules for expressing and processing knowledge. These rules are based on fuzzy logic. Fuzzy logic is the formal symbolic language used to represent linguistic terms and verbal rules for computational and modeling purposes. This language provides model builders with the means to incorporate subjective judgements, imprecise information, and human reasoning capabilities as part of a model`s framework. This paper outlines the concepts needed to understand fuzzy modeling systems. The key concepts discussed include fuzzy sets, fuzzy logical operators, linguistic variables, and verbal rules. A simple fuzzy economic rating model for project investments is presented to demonstrate the fuzzy modeling technique. Finally, the paper discusses the role of fuzzy logic in the economic modeling process.

  8. Multi-objective decision-making under uncertainty: Fuzzy logic methods

    NASA Technical Reports Server (NTRS)

    Hardy, Terry L.

    1995-01-01

    Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

  9. A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology.

    PubMed

    Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali

    2015-01-01

    The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology.

  10. A Modern Syllogistic Method in Intuitionistic Fuzzy Logic with Realistic Tautology

    PubMed Central

    Rushdi, Ali Muhammad; Zarouan, Mohamed; Alshehri, Taleb Mansour; Rushdi, Muhammad Ali

    2015-01-01

    The Modern Syllogistic Method (MSM) of propositional logic ferrets out from a set of premises all that can be concluded from it in the most compact form. The MSM combines the premises into a single function equated to 1 and then produces the complete product of this function. Two fuzzy versions of MSM are developed in Ordinary Fuzzy Logic (OFL) and in Intuitionistic Fuzzy Logic (IFL) with these logics augmented by the concept of Realistic Fuzzy Tautology (RFT) which is a variable whose truth exceeds 0.5. The paper formally proves each of the steps needed in the conversion of the ordinary MSM into a fuzzy one. The proofs rely mainly on the successful replacement of logic 1 (or ordinary tautology) by an RFT. An improved version of Blake-Tison algorithm for generating the complete product of a logical function is also presented and shown to be applicable to both crisp and fuzzy versions of the MSM. The fuzzy MSM methodology is illustrated by three specific examples, which delineate differences with the crisp MSM, address the question of validity values of consequences, tackle the problem of inconsistency when it arises, and demonstrate the utility of the concept of Realistic Fuzzy Tautology. PMID:26380357

  11. WARP: Weight Associative Rule Processor. A dedicated VLSI fuzzy logic megacell

    NASA Technical Reports Server (NTRS)

    Pagni, A.; Poluzzi, R.; Rizzotto, G. G.

    1992-01-01

    During the last five years Fuzzy Logic has gained enormous popularity in the academic and industrial worlds. The success of this new methodology has led the microelectronics industry to create a new class of machines, called Fuzzy Machines, to overcome the limitations of traditional computing systems when utilized as Fuzzy Systems. This paper gives an overview of the methods by which Fuzzy Logic data structures are represented in the machines (each with its own advantages and inefficiencies). Next, the paper introduces WARP (Weight Associative Rule Processor) which is a dedicated VLSI megacell allowing the realization of a fuzzy controller suitable for a wide range of applications. WARP represents an innovative approach to VLSI Fuzzy controllers by utilizing different types of data structures for characterizing the membership functions during the various stages of the Fuzzy processing. WARP dedicated architecture has been designed in order to achieve high performance by exploiting the computational advantages offered by the different data representations.

  12. Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization.

    PubMed

    Miranda, Gisele Helena Barboni; Felipe, Joaquim Cezar

    2015-09-01

    Fuzzy logic can help reduce the difficulties faced by computational systems to represent and simulate the reasoning and the style adopted by radiologists in the process of medical image analysis. The study described in this paper consists of a new method that applies fuzzy logic concepts to improve the representation of features related to image description in order to make it semantically more consistent. Specifically, we have developed a computer-aided diagnosis tool for automatic BI-RADS categorization of breast lesions. The user provides parameters such as contour, shape and density and the system gives a suggestion about the BI-RADS classification. Initially, values of malignancy were defined for each image descriptor, according to the BI-RADS standard. When analyzing contour, for example, our method considers the matching of features and linguistic variables. Next, we created the fuzzy inference system. The generation of membership functions was carried out by the Fuzzy Omega algorithm, which is based on the statistical analysis of the dataset. This algorithm maps the distribution of different classes in a set. Images were analyzed by a group of physicians and the resulting evaluations were submitted to the Fuzzy Omega algorithm. The results were compared, achieving an accuracy of 76.67% for nodules and 83.34% for calcifications. The fit of definitions and linguistic rules to numerical models provided by our method can lead to a tighter connection between the specialist and the computer system, yielding more effective and reliable results. Copyright © 2014 Elsevier Ltd. All rights reserved.

  13. Design and implementation of a fuzzy logic yaw controller

    NASA Astrophysics Data System (ADS)

    Wu, Kung C.; Swift, Andrew H.; Craver, W. Lionel, Jr.; Chang, Yi-Chieh

    1993-08-01

    This paper describes a fuzzy logic controller (FLC) designed and implemented to control the yaw angle of a 10 kW fixed speed teetered-rotor wind turbine presently being commissioned at the University of Texas at El Paso. The technical challenge of this project is that the wind turbine represents a highly stochastic nonlinear system. The problems associated with the wind turbine yaw control are of a similar nature as those experienced with position control of high inertia equipment like tracking antenna, gun turrets, and overhead cranes. Furthermore, the wind turbine yaw controller must be extremely cost-effective and highly reliable in order to be economically viable compared to the fossil fueled power generators.

  14. Adaptive process control using fuzzy logic and genetic algorithms

    NASA Technical Reports Server (NTRS)

    Karr, C. L.

    1993-01-01

    Researchers at the U.S. Bureau of Mines have developed adaptive process control systems in which genetic algorithms (GA's) are used to augment fuzzy logic controllers (FLC's). GA's are search algorithms that rapidly locate near-optimum solutions to a wide spectrum of problems by modeling the search procedures of natural genetics. FLC's are rule based systems that efficiently manipulate a problem environment by modeling the 'rule-of-thumb' strategy used in human decision making. Together, GA's and FLC's possess the capabilities necessary to produce powerful, efficient, and robust adaptive control systems. To perform efficiently, such control systems require a control element to manipulate the problem environment, and a learning element to adjust to the changes in the problem environment. Details of an overall adaptive control system are discussed. A specific laboratory acid-base pH system is used to demonstrate the ideas presented.

  15. A fuzzy logic approach to marine spatial management.

    PubMed

    Teh, Lydia C L; Teh, Louise S L

    2011-04-01

    Marine spatial planning tends to prioritise biological conservation targets over socio-economic considerations, which may incur lower user compliance and ultimately compromise management success. We argue for more inclusion of human dimensions in spatial management, so that outcomes not only fulfill biodiversity and conservation objectives, but are also acceptable to resource users. We propose a fuzzy logic framework that will facilitate this task- The protected area suitability index (PASI) combines fishers' spatial preferences with biological criteria to assess site suitability for protection from fishing. We apply the PASI in a spatial evaluation of a small-scale reef fishery in Sabah, Malaysia. While our results pertain to fishers specifically, the PASI can also be customized to include the interests of other stakeholders and resource users, as well as incorporate varying levels of protection.

  16. Wastewater neutralization control based on fuzzy logic: Experimental results

    SciTech Connect

    Adroer, M.; Alsina, A.; Aumatell, J.; Poch, M.

    1999-07-01

    Many industrial wastes contain acidic or alkaline materials that require neutralization of previous discharge into receiving waters or to chemical and biological treatment plants. The control of the wastewater neutralization process is subjected to several difficulties, such as the highly nonlinear titration curve (with special sensitivity around neutrality), the unknown water composition, the variable buffering capacity of the system, and the changes in input loading. To deal with these problems, this study proposes a fixed fuzzy logic controller (FLC) structure coupled with a tuning factor. The versatility and robustness of this controller has been proved when faced with solutions of variable buffering capacity, with acids that cover a wide pK range and with switches between acids throughout the course of a test. Laboratory experiments and simulation runs using the proposed controller were successful in a wide operational range.

  17. A Fuzzy Logic Approach to Marine Spatial Management

    NASA Astrophysics Data System (ADS)

    Teh, Lydia C. L.; Teh, Louise S. L.

    2011-04-01

    Marine spatial planning tends to prioritise biological conservation targets over socio-economic considerations, which may incur lower user compliance and ultimately compromise management success. We argue for more inclusion of human dimensions in spatial management, so that outcomes not only fulfill biodiversity and conservation objectives, but are also acceptable to resource users. We propose a fuzzy logic framework that will facilitate this task- The protected area suitability index (PASI) combines fishers' spatial preferences with biological criteria to assess site suitability for protection from fishing. We apply the PASI in a spatial evaluation of a small-scale reef fishery in Sabah, Malaysia. While our results pertain to fishers specifically, the PASI can also be customized to include the interests of other stakeholders and resource users, as well as incorporate varying levels of protection.

  18. Controlling of grid connected photovoltaic lighting system with fuzzy logic

    SciTech Connect

    Saglam, Safak; Ekren, Nazmi; Erdal, Hasan

    2010-02-15

    In this study, DC electrical energy produced by photovoltaic panels is converted to AC electrical energy and an indoor area is illuminated using this energy. System is controlled by fuzzy logic algorithm controller designed with 16 rules. Energy is supplied from accumulator which is charged by photovoltaic panels if its energy would be sufficient otherwise it is supplied from grid. During the 1-week usage period at the semester time, 1.968 kWh energy is used from grid but designed system used 0.542 kWh energy from photovoltaic panels at the experiments. Energy saving is determined by calculations and measurements for one education year period (9 months) 70.848 kWh. (author)

  19. A comprehensive approach using fuzzy logic to select fracture fluid systems

    SciTech Connect

    Xiong, H.; Davidson, B.; Holditch, S.A.; Saunders, B.

    1997-01-01

    This system, which consists of several fuzzy logic evaluators, can also be applied to similar problems associated with drilling, completing and working over wells. With formation information, the fuzzy logic system first determines base fluid, viscosifying method and energization method before choosing the 3--5 best combinations of possible fluids. The system then determines polymer type and loading, crosslinker, gas type if necessary, and other additives for the fluid systems. Also using fuzzy logic, this system checks the compatibility of the fluid and additives with formation fluids and composition.

  20. Automated interpretation of LIBS spectra using a fuzzy logic inference engine.

    PubMed

    Hatch, Jeremy J; McJunkin, Timothy R; Hanson, Cynthia; Scott, Jill R

    2012-03-01

    Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. Fuzzy logic inference rules were developed using methodology that includes data mining methods and operator expertise to differentiate between various copper-containing and stainless steel alloys as well as unknowns. Results using the fuzzy logic inference engine indicate a high degree of confidence in spectral assignment.

  1. Severity classification for idiopathic pulmonary fibrosis by using fuzzy logic

    PubMed Central

    Lopes, Agnaldo José; Capone, Domenico; Mogami, Roberto; Lanzillotti, Regina Serrão; de Melo, Pedro Lopes; Jansen, José Manoel

    2011-01-01

    OBJECTIVE: To set out a severity classification for idiopathic pulmonary fibrosis (IPF) based on the interaction of pulmonary function parameters with high resolution computed tomography (CT) findings. INTRODUCTION: Despite the contribution of functional and radiological methods in the study of IPF, there are few classification proposals for the disease based on these examinations. METHODS: A cross-sectional study was carried out, in which 41 non-smoking patients with IPF were evaluated. The following high resolution CT findings were quantified using a semi-quantitative scoring system: reticular abnormality, honeycombing and ground-glass opacity. The functional variables were measured by spirometry, forced oscillation technique, helium dilution method, as well as the single-breath method of diffusing capacity of carbon monoxide. With the interaction between functional indexes and high resolution CT scores through fuzzy logic, a classification for IPF has been built. RESULTS: Out of 41 patients studied, 26 were male and 15 female, with a mean age of 70.8 years. Volume measurements were the variables which showed the best interaction with the disease extension on high resolution CT, while the forced vital capacity showed the lowest estimative errors in comparison to total lung capacity. A classification for IPF was suggested based on the 95% confidence interval of the forced vital capacity %: mild group (≥92.7); moderately mild (76.9–92.6); moderate (64.3–76.8%); moderately severe (47.1–64.2); severe (24.3–47.0); and very severe (<24.3). CONCLUSION: Through fuzzy logic, an IPF classification was built based on forced vital capacity measurement with a simple practical application. PMID:21808868

  2. Rollover prevention for sport utility vehicle using fuzzy logic controller

    NASA Astrophysics Data System (ADS)

    Lee, Yong-hwi; Yi, Seung-Jong

    2005-12-01

    The purpose of this study is to develop the fuzzy logic RSC(Roll Stability Control) system to prevent the rollover for the SUV(sport utility vehicle). The SUV model used in this study is the 8-DOF model considering the longitudinal, lateral, yaw and roll motions. The longitudinal and transversal weight transfers are considered in the computation of the vertical forces acting on a wheel. The engine torque is obtained from the throttle position and the r.p.m. of the engine map. The fuzzy logic controller input consists of the roll angle error and its derivative. The output is the brake torque and the throttle angle. The engine torque controller controls the throttle valve angle. The brake controller independently controls both right and left wheels. When the roll angle is +/-4.5° defined as the critical roll angle, the front inner tire experiences the 1/100 ~ 1/50 of the total vertical forces, and the rollover starts. To prevent the rollover in advance, the target angle +/-4.5° is adopted to control the vehicle stability. The RSC system begins operating at +/-4.5° and stops at 0°. The simulations are conducted to evaluate the controller performance at right turns for the excessive steering angle. When the roll angle error and its derivative exceed the limited point, the RSC system makes the longitudinal velocity of the SUV decrease the brake torque and adjusts the throttle angle. The roll motion of the SUV is then stabilized.

  3. CONTROLLING MECHANICAL VENTILATION IN ARDS WITH FUZZY LOGIC

    PubMed Central

    Nguyen, Binh; Bernstein, David B.; Bates, Jason H.T.

    2014-01-01

    Purpose The current ventilatory care goal for acute respiratory distress syndrome (ARDS), and the only evidence-based approach for managing ARDS, is to ventilate with a tidal volume (VT) of 6 ml/kg predicted body weight (PBW). However, it is not uncommon for some caregivers to feel inclined to deviate from this strategy for one reason or another. To accommodate this inclination in a rationalized manner, we previously developed an algorithm that allows for VT to depart from 6 ml/kg PBW based on physiological criteria. The goal of the present study was to test the feasibility of this algorithm in a small retrospective study. Materials and Methods Current values of peak airway pressure (PAP), positive end-expiratory pressure (PEEP) and arterial oxygen saturation (SaO2) are used in a fuzzy logic algorithm to decide how much VT should differ from 6 ml/kg PBW and how much PEEP should change from its current setting. We retrospectively tested the predictions of the algorithm against 26 cases of decision making in 17 patients with ARDS. Results Differences between algorithm and physician VT decisions were within 2.5 ml/kg PBW except in 1 of 26 cases, and differences between PEEP decisions were within 2.5 cm H2O except in 3 of 26 cases. The algorithm was consistently more conservative than physicians in changing VT, but was slightly less conservative when changing PEEP. Conclusions Within the limits imposed by a small retrospective study, we conclude that our fuzzy logic algorithm makes sensible decisions while at the same time keeping practice close to the current ventilatory care goal. PMID:24721387

  4. A fuzzy logic controller for an autonomous mobile robot

    NASA Technical Reports Server (NTRS)

    Yen, John; Pfluger, Nathan

    1993-01-01

    The ability of a mobile robot system to plan and move intelligently in a dynamic system is needed if robots are to be useful in areas other than controlled environments. An example of a use for this system is to control an autonomous mobile robot in a space station, or other isolated area where it is hard or impossible for human life to exist for long periods of time (e.g., Mars). The system would allow the robot to be programmed to carry out the duties normally accomplished by a human being. Some of the duties that could be accomplished include operating instruments, transporting objects, and maintenance of the environment. The main focus of our early work has been on developing a fuzzy controller that takes a path and adapts it to a given environment. The robot only uses information gathered from the sensors, but retains the ability to avoid dynamically placed obstacles near and along the path. Our fuzzy logic controller is based on the following algorithm: (1) determine the desired direction of travel; (2) determine the allowed direction of travel; and (3) combine the desired and allowed directions in order to determine a direciton that is both desired and allowed. The desired direction of travel is determined by projecting ahead to a point along the path that is closer to the goal. This gives a local direction of travel for the robot and helps to avoid obstacles.

  5. A Fuzzy Logic Framework for Integrating Multiple Learned Models

    SciTech Connect

    Hartog, Bobi Kai Den

    1999-03-01

    The Artificial Intelligence field of Integrating Multiple Learned Models (IMLM) explores ways to combine results from sets of trained programs. Aroclor Interpretation is an ill-conditioned problem in which trained programs must operate in scenarios outside their training ranges because it is intractable to train them completely. Consequently, they fail in ways related to the scenarios. We developed a general-purpose IMLM solution, the Combiner, and applied it to Aroclor Interpretation. The Combiner's first step, Scenario Identification (M), learns rules from very sparse, synthetic training data consisting of results from a suite of trained programs called Methods. S1 produces fuzzy belief weights for each scenario by approximately matching the rules. The Combiner's second step, Aroclor Presence Detection (AP), classifies each of three Aroclors as present or absent in a sample. The third step, Aroclor Quantification (AQ), produces quantitative values for the concentration of each Aroclor in a sample. AP and AQ use automatically learned empirical biases for each of the Methods in each scenario. Through fuzzy logic, AP and AQ combine scenario weights, automatically learned biases for each of the Methods in each scenario, and Methods' results to determine results for a sample.

  6. Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images

    PubMed Central

    Medeiros, Marcos D.; Gonçalves, Luiz Marcos G.; Frery, Alejandro C.

    2010-01-01

    Stereo matching is an open problem in Computer Vision, for which local features are extracted to identify corresponding points in pairs of images. The results are heavily dependent on the initial steps. We apply image decomposition in multiresolution levels, for reducing the search space, computational time, and errors. We propose a solution to the problem of how deep (coarse) should the stereo measures start, trading between error minimization and time consumption, by starting stereo calculation at varying resolution levels, for each pixel, according to fuzzy decisions. Our heuristic enhances the overall execution time since it only employs deeper resolution levels when strictly necessary. It also reduces errors because it measures similarity between windows with enough details. We also compare our algorithm with a very fast multi-resolution approach, and one based on fuzzy logic. Our algorithm performs faster and/or better than all those approaches, becoming, thus, a good candidate for robotic vision applications. We also discuss the system architecture that efficiently implements our solution. PMID:22205859

  7. A fuzzy logic controller for an autonomous mobile robot

    NASA Technical Reports Server (NTRS)

    Yen, John; Pfluger, Nathan

    1993-01-01

    The ability of a mobile robot system to plan and move intelligently in a dynamic system is needed if robots are to be useful in areas other than controlled environments. An example of a use for this system is to control an autonomous mobile robot in a space station, or other isolated area where it is hard or impossible for human life to exist for long periods of time (e.g., Mars). The system would allow the robot to be programmed to carry out the duties normally accomplished by a human being. Some of the duties that could be accomplished include operating instruments, transporting objects, and maintenance of the environment. The main focus of our early work has been on developing a fuzzy controller that takes a path and adapts it to a given environment. The robot only uses information gathered from the sensors, but retains the ability to avoid dynamically placed obstacles near and along the path. Our fuzzy logic controller is based on the following algorithm: (1) determine the desired direction of travel; (2) determine the allowed direction of travel; and (3) combine the desired and allowed directions in order to determine a direciton that is both desired and allowed. The desired direction of travel is determined by projecting ahead to a point along the path that is closer to the goal. This gives a local direction of travel for the robot and helps to avoid obstacles.

  8. A fuzzy-logic based decision-making approach for identification of groundwater quality based on groundwater quality indices.

    PubMed

    Vadiati, M; Asghari-Moghaddam, A; Nakhaei, M; Adamowski, J; Akbarzadeh, A H

    2016-12-15

    Due to inherent uncertainties in measurement and analysis, groundwater quality assessment is a difficult task. Artificial intelligence techniques, specifically fuzzy inference systems, have proven useful in evaluating groundwater quality in uncertain and complex hydrogeological systems. In the present study, a Mamdani fuzzy-logic-based decision-making approach was developed to assess groundwater quality based on relevant indices. In an effort to develop a set of new hybrid fuzzy indices for groundwater quality assessment, a Mamdani fuzzy inference model was developed with widely-accepted groundwater quality indices: the Groundwater Quality Index (GQI), the Water Quality Index (WQI), and the Ground Water Quality Index (GWQI). In an effort to present generalized hybrid fuzzy indices a significant effort was made to employ well-known groundwater quality index acceptability ranges as fuzzy model output ranges rather than employing expert knowledge in the fuzzification of output parameters. The proposed approach was evaluated for its ability to assess the drinking water quality of 49 samples collected seasonally from groundwater resources in Iran's Sarab Plain during 2013-2014. Input membership functions were defined as "desirable", "acceptable" and "unacceptable" based on expert knowledge and the standard and permissible limits prescribed by the World Health Organization. Output data were categorized into multiple categories based on the GQI (5 categories), WQI (5 categories), and GWQI (3 categories). Given the potential of fuzzy models to minimize uncertainties, hybrid fuzzy-based indices produce significantly more accurate assessments of groundwater quality than traditional indices. The developed models' accuracy was assessed and a comparison of the performance indices demonstrated the Fuzzy Groundwater Quality Index model to be more accurate than both the Fuzzy Water Quality Index and Fuzzy Ground Water Quality Index models. This suggests that the new hybrid fuzzy

  9. Autonomous Control of a Quadrotor UAV Using Fuzzy Logic

    NASA Astrophysics Data System (ADS)

    Sureshkumar, Vijaykumar

    UAVs are being increasingly used today than ever before in both military and civil applications. They are heavily preferred in "dull, dirty or dangerous" mission scenarios. Increasingly, UAVs of all kinds are being used in policing, fire-fighting, inspection of structures, pipelines etc. Recently, the FAA gave its permission for UAVs to be used on film sets for motion capture and high definition video recording. The rapid development in MEMS and actuator technology has made possible a plethora of UAVs that are suited for commercial applications in an increasingly cost effective manner. An emerging popular rotary wing UAV platform is the Quadrotor A Quadrotor is a helicopter with four rotors, that make it more stable; but more complex to model and control. Characteristics that provide a clear advantage over other fixed wing UAVs are VTOL and hovering capabilities as well as a greater maneuverability. It is also simple in construction and design compared to a scaled single rotorcraft. Flying such UAVs using a traditional radio Transmitter-Receiver setup can be a daunting task especially in high stress situations. In order to make such platforms widely applicable, a certain level of autonomy is imperative to the future of such UAVs. This thesis paper presents a methodology for the autonomous control of a Quadrotor UAV using Fuzzy Logic. Fuzzy logic control has been chosen over conventional control methods as it can deal effectively with highly nonlinear systems, allows for imprecise data and is extremely modular. Modularity and adaptability are the key cornerstones of FLC. The objective of this thesis is to present the steps of designing, building and simulating an intelligent flight control module for a Quadrotor UAV. In the course of this research effort, a Quadrotor UAV is indigenously developed utilizing the resources of an online open source project called Aeroquad. System design is comprehensively dealt with. A math model for the Quadrotor is developed and a

  10. Fault current reduction by SFCL in a distribution system with PV using fuzzy logic technique

    NASA Astrophysics Data System (ADS)

    Mounika, M.; Lingareddy, P.

    2017-07-01

    In the modern power system, as the utilization of electric power is very wide, there is a frequent occurring of any fault or disturbance in power system. It causes a high short circuit current. Due to this fault, high currents occurs results to large mechanical forces, these forces cause overheating of the equipment. If the large size equipment are used in power system then they need a large protection scheme for severe fault conditions. Generally, the maintenance of electrical power system reliability is more important. But the elimination of fault is not possible in power systems. So the only alternate solution is to minimize the fault currents. For this the Super Conducting Fault Current Limiter using fuzzy logic technique is the best electric equipment which is used for reducing the severe fault current levels. In this paper, we simulated the unsymmetrical and symmetrical faults with fuzzy based superconducting fault current limiter. In our analysis it is proved that, fuzzy logic based super conducting fault current limiter reduces fault current quickly to a lower value.

  11. Use of an Electronic Tongue System and Fuzzy Logic to Analyze Water Samples

    NASA Astrophysics Data System (ADS)

    Braga, Guilherme S.; Paterno, Leonardo G.; Fonseca, Fernando J.

    2009-05-01

    An electronic tongue (ET) system incorporating 8 chemical sensors was used in combination with two pattern recognition tools, namely principal component analysis (PCA) and Fuzzy logic for discriminating/classification of water samples from different sources (tap, distilled and three brands of mineral water). The Fuzzy program exhibited a higher accuracy than the PCA and allowed the ET to classify correctly 4 in 5 types of water. Exception was made for one brand of mineral water which was sometimes misclassified as tap water. On the other hand, the PCA grouped water samples in three clusters, one with the distilled water; a second with tap water and one brand of mineral water, and the third with the other two other brands of mineral water. Samples in the second and third clusters could not be distinguished. Nevertheless, close grouping between repeated tests indicated that the ET system response is reproducible. The potential use of the Fuzzy logic as the data processing tool in combination with an electronic tongue system is discussed.

  12. Preventive Maintenance Prioritization by Fuzzy Logic for Seamless Hydro Power Generation

    NASA Astrophysics Data System (ADS)

    Roy, P. K.; Adhikary, P.; Mazumdar, A.

    2014-06-01

    Preventive maintenance prioritization is one of the most important criteria for the electricity generation planners to minimize the down time and production costs. Break down of equipments increases costs and plant down time results in loss of business. This work focuses on prioritizing the preventive maintenance for seamless hydro power generation considering (24 × 7) client's power demand using fuzzy logic. The main task involves prioritizing the maintenance work considering constraints of varied power demand and hydro turbine plant breakdown. Fuzzy logic is used to optimize the preventive maintenance prioritization under the main constraints. Manual fuzzy arithmetic is used to develop the model and MATLAB Fuzzy Inference System editor used to validate the same. This novel fuzzy logic approach of preventive maintenance prioritizing for hydro power generation is absent in renewable power generation and industrial engineering literatures due to its assessment complexity.

  13. A fuzzy logic sliding mode controlled electronic differential for a direct wheel drive EV

    NASA Astrophysics Data System (ADS)

    Ozkop, Emre; Altas, Ismail H.; Okumus, H. Ibrahim; Sharaf, Adel M.

    2015-11-01

    In this study, a direct wheel drive electric vehicle based on an electronic differential system with a fuzzy logic sliding mode controller (FLSMC) is studied. The conventional sliding surface is modified using a fuzzy rule base to obtain fuzzy dynamic sliding surfaces by changing its slopes using the global error and its derivative in a fuzzy logic inference system. The controller is compared with proportional-integral-derivative (PID) and sliding mode controllers (SMCs), which are usually preferred to be used in industry. The proposed controller provides robustness and flexibility to direct wheel drive electric vehicles. The fuzzy logic sliding mode controller, electronic differential system and the overall electrical vehicle mechanism are modelled and digitally simulated by using the Matlab software. Simulation results show that the system with FLSMC has better efficiency and performance compared to those of PID and SMCs.

  14. Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass.

    PubMed

    Tai, Chao; Voltan, Diego S; Keshwani, Deepak R; Meyer, George E; Kuhar, Pankaj S

    2016-06-01

    A fuzzy logic feedback control system was developed for process monitoring and feeding control in fed-batch enzymatic hydrolysis of a lignocellulosic biomass, dilute acid-pretreated corn stover. Digested glucose from hydrolysis reaction was assigned as input while doser feeding time and speed of pretreated biomass were responses from fuzzy logic control system. Membership functions for these three variables and rule-base were created based on batch hydrolysis data. The system response was first tested in LabVIEW environment then the performance was evaluated through real-time hydrolysis reaction. The feeding operations were determined timely by fuzzy logic control system and efficient responses were shown to plateau phases during hydrolysis. Feeding of proper amount of cellulose and maintaining solids content was well balanced. Fuzzy logic proved to be a robust and effective online feeding control tool for fed-batch enzymatic hydrolysis.

  15. A new approach of active compliance control via fuzzy logic control for multifingered robot hand

    NASA Astrophysics Data System (ADS)

    Jamil, M. F. A.; Jalani, J.; Ahmad, A.

    2016-07-01

    Safety is a vital issue in Human-Robot Interaction (HRI). In order to guarantee safety in HRI, a model reference impedance control can be a very useful approach introducing a compliant control. In particular, this paper establishes a fuzzy logic compliance control (i.e. active compliance control) to reduce impact and forces during physical interaction between humans/objects and robots. Exploiting a virtual mass-spring-damper system allows us to determine a desired compliant level by understanding the behavior of the model reference impedance control. The performance of fuzzy logic compliant control is tested in simulation for a robotic hand known as the RED Hand. The results show that the fuzzy logic is a feasible control approach, particularly to control position and to provide compliant control. In addition, the fuzzy logic control allows us to simplify the controller design process (i.e. avoid complex computation) when dealing with nonlinearities and uncertainties.

  16. ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers

    NASA Astrophysics Data System (ADS)

    César, Manuel Braz; Barros, Rui Carneiro

    2016-11-01

    In this paper, we report on the development of a neuro-fuzzy controller for magnetorheological dampers using an Adaptive Neuro-Fuzzy Inference System or ANFIS. Fuzzy logic based controllers are capable to deal with non-linear or uncertain systems, which make them particularly well suited for civil engineering applications. The main objective is to develop a semi-active control system with a MR damper to reduce the response of a three degrees-of-freedom (DOFs) building structure. The control system is designed using ANFIS to optimize the fuzzy inference rule of a simple fuzzy logic controller. The results show that the proposed semi-active neuro-fuzzy based controller is effective in reducing the response of structural system.

  17. Fuzzy logic and image processing techniques for the interpretation of seismic data

    NASA Astrophysics Data System (ADS)

    Orozco-del-Castillo, M. G.; Ortiz-Alemán, C.; Urrutia-Fucugauchi, J.; Rodríguez-Castellanos, A.

    2011-06-01

    Since interpretation of seismic data is usually a tedious and repetitive task, the ability to do so automatically or semi-automatically has become an important objective of recent research. We believe that the vagueness and uncertainty in the interpretation process makes fuzzy logic an appropriate tool to deal with seismic data. In this work we developed a semi-automated fuzzy inference system to detect the internal architecture of a mass transport complex (MTC) in seismic images. We propose that the observed characteristics of a MTC can be expressed as fuzzy if-then rules consisting of linguistic values associated with fuzzy membership functions. The constructions of the fuzzy inference system and various image processing techniques are presented. We conclude that this is a well-suited problem for fuzzy logic since the application of the proposed methodology yields a semi-automatically interpreted MTC which closely resembles the MTC from expert manual interpretation.

  18. Applying fuzzy logic to estimate the parameters of the length-weight relationship.

    PubMed

    Bitar, S D; Campos, C P; Freitas, C E C

    2016-05-03

    We evaluated three mathematical procedures to estimate the parameters of the relationship between weight and length for Cichla monoculus: least squares ordinary regression on log-transformed data, non-linear estimation using raw data and a mix of multivariate analysis and fuzzy logic. Our goal was to find an alternative approach that considers the uncertainties inherent to this biological model. We found that non-linear estimation generated more consistent estimates than least squares regression. Our results also indicate that it is possible to find consistent estimates of the parameters directly from the centers of mass of each cluster. However, the most important result is the intervals obtained with the fuzzy inference system.

  19. Systematic methods for the design of a class of fuzzy logic controllers

    NASA Astrophysics Data System (ADS)

    Yasin, Saad Yaser

    2002-09-01

    Fuzzy logic control, a relatively new branch of control, can be used effectively whenever conventional control techniques become inapplicable or impractical. Various attempts have been made to create a generalized fuzzy control system and to formulate an analytically based fuzzy control law. In this study, two methods, the left and right parameterization method and the normalized spline-base membership function method, were utilized for formulating analytical fuzzy control laws in important practical control applications. The first model was used to design an idle speed controller, while the second was used to control an inverted control problem. The results of both showed that a fuzzy logic control system based on the developed models could be used effectively to control highly nonlinear and complex systems. This study also investigated the application of fuzzy control in areas not fully utilizing fuzzy logic control. Three important practical applications pertaining to the automotive industries were studied. The first automotive-related application was the idle speed of spark ignition engines, using two fuzzy control methods: (1) left and right parameterization, and (2) fuzzy clustering techniques and experimental data. The simulation and experimental results showed that a conventional controller-like performance fuzzy controller could be designed based only on experimental data and intuitive knowledge of the system. In the second application, the automotive cruise control problem, a fuzzy control model was developed using parameters adaptive Proportional plus Integral plus Derivative (PID)-type fuzzy logic controller. Results were comparable to those using linearized conventional PID and linear quadratic regulator (LQR) controllers and, in certain cases and conditions, the developed controller outperformed the conventional PID and LQR controllers. The third application involved the air/fuel ratio control problem, using fuzzy clustering techniques, experimental

  20. Depth Control of Sevofluorane Anesthesia with Microcontroller Based Fuzzy Logic System

    DTIC Science & Technology

    2007-11-02

    sevoflurane in humans”, Anesthesiology, 66:301-303, 1987 [10].YARDIMCI, A., ONURAL A.,”Fuzzy Logic Control of Child Blood Pressure During Anaesthesia...microcontroller-based fuzzy logic control system according to the blood pressure and heart rate taken from the patient. The potential benefits of the... blood pressure and hearth rate. The main reason for automating the control of depth anesthesia is to release the anesthesiologist so that he or

  1. Neural Network and Fuzzy Logic Technology for Naval Flight Control Systems

    DTIC Science & Technology

    1991-08-06

    it is still uncertain what neural network and fuzzy logic functions are both technologically feasible and suitable for flight control system...this program is focused on the development of a neural network FCS design tool, a neural network flight control law emulator, a fuzzy logic automatic...carrier landing system and a neural network flight control configuration management system. For each project, some initial results are given. Also

  2. Experimental Evaluation of Fuzzy Logic Control of a Flexible Arm Manipulator

    DTIC Science & Technology

    1993-12-09

    motor . The encoder consists of an LED source and lens which transmits collimated light from the emitter module through a metal code wheel and phase plate...information needed to run a fuzzy logic based controller . The first of the five steps is that of fuzzification. This step simply converts a input. or a...Hardware, 15 Degree Slew 32 3.6.3 Robustness To examine the performance robustness of the fuzzy logic controller , three off-design tests were accomplished

  3. MRI and PET image fusion using fuzzy logic and image local features.

    PubMed

    Javed, Umer; Riaz, Muhammad Mohsin; Ghafoor, Abdul; Ali, Syed Sohaib; Cheema, Tanveer Ahmed

    2014-01-01

    An image fusion technique for magnetic resonance imaging (MRI) and positron emission tomography (PET) using local features and fuzzy logic is presented. The aim of proposed technique is to maximally combine useful information present in MRI and PET images. Image local features are extracted and combined with fuzzy logic to compute weights for each pixel. Simulation results show that the proposed scheme produces significantly better results compared to state-of-art schemes.

  4. 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.

  5. Simulation of the Predictive Control Algorithm for Container Crane Operation using Matlab Fuzzy Logic Tool Box

    NASA Technical Reports Server (NTRS)

    Richardson, Albert O.

    1997-01-01

    This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.

  6. Obtaining ABET Student Outcome Satisfaction from Course Learning Outcome Data Using Fuzzy Logic

    ERIC Educational Resources Information Center

    Imam, Muhammad Hasan; Tasadduq, Imran Ali; Ahmad, Abdul-Rahim; Aldosari, Fahd

    2017-01-01

    One of the approaches for obtaining the satisfaction data for ABET "Student Outcomes" (SOs) is to transform Course Learning Outcomes (CLOs) satisfaction data obtained through assessment of CLOs to SO satisfaction data. Considering the fuzzy nature of metrics of CLOs and SOs, a Fuzzy Logic algorithm has been proposed to extract SO…

  7. Simulation of the Predictive Control Algorithm for Container Crane Operation using Matlab Fuzzy Logic Tool Box

    NASA Technical Reports Server (NTRS)

    Richardson, Albert O.

    1997-01-01

    This research has investigated the use of fuzzy logic, via the Matlab Fuzzy Logic Tool Box, to design optimized controller systems. The engineering system for which the controller was designed and simulate was the container crane. The fuzzy logic algorithm that was investigated was the 'predictive control' algorithm. The plant dynamics of the container crane is representative of many important systems including robotic arm movements. The container crane that was investigated had a trolley motor and hoist motor. Total distance to be traveled by the trolley was 15 meters. The obstruction height was 5 meters. Crane height was 17.8 meters. Trolley mass was 7500 kilograms. Load mass was 6450 kilograms. Maximum trolley and rope velocities were 1.25 meters per sec. and 0.3 meters per sec., respectively. The fuzzy logic approach allowed the inclusion, in the controller model, of performance indices that are more effectively defined in linguistic terms. These include 'safety' and 'cargo swaying'. Two fuzzy inference systems were implemented using the Matlab simulation package, namely the Mamdani system (which relates fuzzy input variables to fuzzy output variables), and the Sugeno system (which relates fuzzy input variables to crisp output variable). It is found that the Sugeno FIS is better suited to including aspects of those plant dynamics whose mathematical relationships can be determined.

  8. Applying Fuzzy Logic to Comparative Distribution Modelling: A Case Study with Two Sympatric Amphibians

    PubMed Central

    Barbosa, A. Márcia; Real, Raimundo

    2012-01-01

    We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourability model based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation “A and not B”) were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning. PMID:22629142

  9. Applying fuzzy logic to comparative distribution modelling: a case study with two sympatric amphibians.

    PubMed

    Barbosa, A Márcia; Real, Raimundo

    2012-01-01

    We modelled the distributions of two toads (Bufo bufo and Epidalea calamita) in the Iberian Peninsula using the favourability function, which makes predictions directly comparable for different species and allows fuzzy logic operations to relate different models. The fuzzy intersection between individual models, representing favourability for the presence of both species simultaneously, was compared with another favourability model built on the presences shared by both species. The fuzzy union between individual models, representing favourability for the presence of any of the two species, was compared with another favourability model based on the presences of either or both of them. The fuzzy intersections between favourability for each species and the complementary of favourability for the other (corresponding to the logical operation "A and not B") were compared with models of exclusive presence of one species versus the exclusive presence of the other. The results of modelling combined species data were highly similar to those of fuzzy logic operations between individual models, proving fuzzy logic and the favourability function valuable for comparative distribution modelling. We highlight several advantages of fuzzy logic over other forms of combining distribution models, including the possibility to combine multiple species models for management and conservation planning.

  10. Neural-network-based fuzzy logic control system with applications on compliant robot control

    NASA Astrophysics Data System (ADS)

    Hor, MawKae; Lu, Hui L.

    1994-10-01

    In view of the success of neural network applications in inverted pendulum control, speech recognition, and other problem solving, we believe that one could inject the noise removing concepts and learning spirits into the algorithm in constructing the neural networks and apply it to the various tasks such as compliant coordinated motion using multiple robots. Based on the fuzzy logic, a fuzzy logical control system is a logical system which is much closer to human thinking than any other logical systems. During recent years, fuzzy logic control has emerged as a fruitful area in applications, especially the applications lacking quantitative data regarding the input-output relations. Whereas, the connectionist model injects the learning ability to the fuzzy logic system. This model, proposed by Lin and Lee, is a connected neural network that embedded the fuzzy rules in the architecture. Since this model is general enough and we expect the embedded fuzzy concepts can solve the problems caused by the defective training data, it is chosen as our base structure. Appropriate modifications have been made to this model to reflect the real situations encountered in the robot applications. Our goal is to control two different types of robots for coordinated motion using sensory feedback information.

  11. Fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization techniques.

    PubMed

    Chen, Shyi-Ming; Manalu, Gandhi Maruli Tua; Pan, Jeng-Shyang; Liu, Hsiang-Chuan

    2013-06-01

    In this paper, we present a new method for fuzzy forecasting based on two-factors second-order fuzzy-trend logical relationship groups and particle swarm optimization (PSO) techniques. First, we fuzzify the historical training data of the main factor and the secondary factor, respectively, to form two-factors second-order fuzzy logical relationships. Then, we group the two-factors second-order fuzzy logical relationships into two-factors second-order fuzzy-trend logical relationship groups. Then, we obtain the optimal weighting vector for each fuzzy-trend logical relationship group by using PSO techniques to perform the forecasting. We also apply the proposed method to forecast the Taiwan Stock Exchange Capitalization Weighted Stock Index and the NTD/USD exchange rates. The experimental results show that the proposed method gets better forecasting performance than the existing methods.

  12. Fuzzy logic controller versus classical logic controller for residential hybrid solar-wind-storage energy system

    NASA Astrophysics Data System (ADS)

    Derrouazin, A.; Aillerie, M.; Mekkakia-Maaza, N.; Charles, J. P.

    2016-07-01

    Several researches for management of diverse hybrid energy systems and many techniques have been proposed for robustness, savings and environmental purpose. In this work we aim to make a comparative study between two supervision and control techniques: fuzzy and classic logics to manage the hybrid energy system applied for typical housing fed by solar and wind power, with rack of batteries for storage. The system is assisted by the electric grid during energy drop moments. A hydrogen production device is integrated into the system to retrieve surplus energy production from renewable sources for the household purposes, intending the maximum exploitation of these sources over years. The models have been achieved and generated signals for electronic switches command of proposed both techniques are presented and discussed in this paper.

  13. Fuzzy logic controller versus classical logic controller for residential hybrid solar-wind-storage energy system

    SciTech Connect

    Derrouazin, A.; Aillerie, M. Charles, J. P.; Mekkakia-Maaza, N.

    2016-07-25

    Several researches for management of diverse hybrid energy systems and many techniques have been proposed for robustness, savings and environmental purpose. In this work we aim to make a comparative study between two supervision and control techniques: fuzzy and classic logics to manage the hybrid energy system applied for typical housing fed by solar and wind power, with rack of batteries for storage. The system is assisted by the electric grid during energy drop moments. A hydrogen production device is integrated into the system to retrieve surplus energy production from renewable sources for the household purposes, intending the maximum exploitation of these sources over years. The models have been achieved and generated signals for electronic switches command of proposed both techniques are presented and discussed in this paper.

  14. A comparison of neural network models, fuzzy logic, and multiple linear regression for prediction of hatchability.

    PubMed

    Mehri, M

    2013-04-01

    Application of appropriate models to approximate the performance function warrants more precise prediction and helps to make the best decisions in the poultry industry. This study reevaluated the factors affecting hatchability in laying hens from 29 to 56 wk of age. Twenty-eight data lines representing 4 inputs consisting of egg weight, eggshell thickness, egg sphericity, and yolk/albumin ratio and 1 output, hatchability, were obtained from the literature and used to train an artificial neural network (ANN). The prediction ability of ANN was compared with that of fuzzy logic to evaluate the fitness of these 2 methods. The models were compared using R(2), mean absolute deviation (MAD), mean squared error (MSE), mean absolute percentage error (MAPE), and bias. The developed model was used to assess the relative importance of each variable on the hatchability by calculating the variable sensitivity ratio. The statistical evaluations showed that the ANN-based model predicted hatchability more accurately than fuzzy logic. The ANN-based model had a higher determination of coefficient (R(2) = 0.99) and lower residual distribution (MAD = 0.005; MSE = 0.00004; MAPE = 0.732; bias = 0.0012) than fuzzy logic (R(2) = 0.87; MAD = 0.014; MSE = 0.0004; MAPE = 2.095; bias = 0.0046). The sensitivity analysis revealed that the most important variable in the ANN-based model of hatchability was egg weight (variable sensitivity ratio, VSR = 283.11), followed by yolk/albumin ratio (VSR = 113.16), eggshell thickness (VSR = 16.23), and egg sphericity (VSR = 3.63). The results of this research showed that the universal approximation capability of ANN made it a powerful tool to approximate complex functions such as hatchability in the incubation process.

  15. Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1

    NASA Technical Reports Server (NTRS)

    Culbert, Christopher J. (Editor)

    1993-01-01

    Documented here are papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake. The workshop was held June 1-3, 1992 at the Lyndon B. Johnson Space Center in Houston, Texas. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control, and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

  16. Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2

    NASA Technical Reports Server (NTRS)

    Culbert, Christopher J. (Editor)

    1993-01-01

    Papers presented at the Neural Networks and Fuzzy Logic Workshop sponsored by the National Aeronautics and Space Administration and cosponsored by the University of Houston, Clear Lake, held 1-3 Jun. 1992 at the Lyndon B. Johnson Space Center in Houston, Texas are included. During the three days approximately 50 papers were presented. Technical topics addressed included adaptive systems; learning algorithms; network architectures; vision; robotics; neurobiological connections; speech recognition and synthesis; fuzzy set theory and application, control and dynamics processing; space applications; fuzzy logic and neural network computers; approximate reasoning; and multiobject decision making.

  17. Development of a fuzzy logic controller for dc/dc converters: Design, computer simulation, and experimental evaluation

    SciTech Connect

    So, W.C.; Tse, C.K.; Lee, Y.S.

    1996-01-01

    The design of a fuzzy logic controller for dc/dc converters is described in this paper. A brief review of fuzzy logic and its application to control is first given. Then, the derivation of a fuzzy control algorithm for regulating dc/dc converters is described in detail. The proposed fuzzy control is evaluated by computer simulations as well as experimental measurements of the closed-loop performance of simple dc/dc converters in respect of load regulation and line regulation.

  18. Development of fuzzy logic system to predict the SAW weldment shape profiles

    NASA Astrophysics Data System (ADS)

    Narang, H. K.; Mahapatra, M. M.; Jha, P. K.; Biswas, P.

    2012-09-01

    A fuzzy model was presented to predict the weldment shape profile of submerged arc welds (SAW) including the shape of heat affected zone (HAZ). The SAW bead-on-plates were welded by following a full factorial design matrix. The design matrix consisted of three levels of input welding process parameters. The welds were cross-sectioned and etched, and the zones were measured. A mapping technique was used to measure the various segments of the weld zones. These mapped zones were used to build a fuzzy logic model. The membership functions of the fuzzy model were chosen for the accurate prediction of the weld zone. The fuzzy model was further tested for a set of test case data. The weld zone predicted by the fuzzy logic model was compared with the experimentally obtained shape profiles and close agreement between the two was noted. The mapping technique developed for the weld zones and the fuzzy logic model can be used for on-line control of the SAW process. From the SAW fuzzy logic model an estimation of the fusion and HAZ can also be developed.

  19. Fuzzy logic based sensor performance evaluation of vehicle mounted metal detector systems

    NASA Astrophysics Data System (ADS)

    Abeynayake, Canicious; Tran, Minh D.

    2015-05-01

    Vehicle Mounted Metal Detector (VMMD) systems are widely used for detection of threat objects in humanitarian demining and military route clearance scenarios. Due to the diverse nature of such operational conditions, operational use of VMMD without a proper understanding of its capability boundaries may lead to heavy causalities. Multi-criteria fitness evaluations are crucial for determining capability boundaries of any sensor-based demining equipment. Evaluation of sensor based military equipment is a multi-disciplinary topic combining the efforts of researchers, operators, managers and commanders having different professional backgrounds and knowledge profiles. Information acquired through field tests usually involves uncertainty, vagueness and imprecision due to variations in test and evaluation conditions during a single test or series of tests. This report presents a fuzzy logic based methodology for experimental data analysis and performance evaluation of VMMD. This data evaluation methodology has been developed to evaluate sensor performance by consolidating expert knowledge with experimental data. A case study is presented by implementing the proposed data analysis framework in a VMMD evaluation scenario. The results of this analysis confirm accuracy, practicability and reliability of the fuzzy logic based sensor performance evaluation framework.

  20. Fuzzy logic controller for the electric motor driving the astronomical telescope

    NASA Astrophysics Data System (ADS)

    Soliman, Hussein F.; Attia, Abdel-Fattah A.; Badr, Mohammed A.; Osman, Anas M.; Gamaleldin, Abdul A.

    1998-05-01

    The paper presents an application of fuzzy logic controller to regulate the DC motor driver system of astronomical telescope. The mathematical model of such a telescope is highly nonlinear coupled equations. However, the accuracy requirement in telescope system exceed those of other industrial plants. Fuzzy logic controller provides means to deal with nonlinear functions. A fuzzy logic controller (FLC) was designed to enhance the performance of a two-link model of astronomical telescope. The proposed FLC utilizes the position deviation for the desired value, and its rate of change to regulate the armature voltage of the DC motor drive of each link. The final action of FLC is equivalent to PD controller with a variable gain by using an expert look- up table. This work presents the derivation of the mathematical model of 14 inch Celestron telescope and computer simulation of its motion. The FLC contains two groups of fuzzy sets.

  1. Automated mango fruit assessment using fuzzy logic approach

    NASA Astrophysics Data System (ADS)

    Hasan, Suzanawati Abu; Kin, Teoh Yeong; Sauddin@Sa'duddin, Suraiya; Aziz, Azlan Abdul; Othman, Mahmod; Mansor, Ab Razak; Parnabas, Vincent

    2014-06-01

    In term of value and volume of production, mango is the third most important fruit product next to pineapple and banana. Accurate size assessment of mango fruits during harvesting is vital to ensure that they are classified to the grade accordingly. However, the current practice in mango industry is grading the mango fruit manually using human graders. This method is inconsistent, inefficient and labor intensive. In this project, a new method of automated mango size and grade assessment is developed using RGB fiber optic sensor and fuzzy logic approach. The calculation of maximum, minimum and mean values based on RGB fiber optic sensor and the decision making development using minimum entropy formulation to analyse the data and make the classification for the mango fruit. This proposed method is capable to differentiate three different grades of mango fruit automatically with 77.78% of overall accuracy compared to human graders sorting. This method was found to be helpful for the application in the current agricultural industry.

  2. Mobile Health in Maternal and Newborn Care: Fuzzy Logic

    PubMed Central

    Premji, Shahirose

    2014-01-01

    Whether mHealth improves maternal and newborn health outcomes remains uncertain as the response is perhaps not true or false but lies somewhere in between when considering unintended harmful consequences. Fuzzy logic, a mathematical approach to computing, extends the traditional binary “true or false” (one or zero) to exemplify this notion of partial truths that lies between completely true and false. The commentary explores health, socio-ecological and environmental consequences–positive, neutral or negative. Of particular significance is the negative influence of mHealth on maternal care-behaviors, which can increase stress reactivity and vulnerability to stress-induced illness across the lifespan of the child and establish pathways for intergenerational transmission of behaviors. A mHealth “fingerprinting” approach is essential to monitor psychosocial, economic, cultural, environmental and physical impact of mHealth intervention and make evidence-informed decision(s) about use of mHealth in maternal and newborn care. PMID:25003177

  3. Evaluation of a fuzzy logic controller for laser thermal therapy

    NASA Astrophysics Data System (ADS)

    Choy, Vanessa; Sadeghian, Alireza; Sherar, Michael D.; Whelan, William M.

    2002-06-01

    Laser thermal therapy (LTT) is a minimally invasive surgical technique used to destroy solid tumors while minimizing damage to adjacent normal tissues. Optical energy, delivered through fibers implanted into the target volume, raises tissue temperatures above 60 degree(s)C resulting in coagulative necrosis (thermal damage). Thermal damage volumes, however, can be irregular and unpredictable, resulting from dynamic changes in the tissue properties during treatment. A closed-loop feedback fuzzy logic controller for LTT was developed with the tissue treated as a black-box system. Preliminary testing was conducted for simulated LTT with a single spherically emitting source fiber at the center of 5 mm and 10 mm diameter target tissues. Dynamic changes in blood perfusion and tissue optical properties due to heating were incorporated into the LTT simulator. Input laser power was modulated to control the temperature field in an attempt to reach target temperatures at the source (90 degree(s)C to avoid tissue charring) and at the target boundary (55 degree(s)C). In all simulations, thermal damage based on Arrhenius formulation ((Omega) equals 1) was reached at the target boundary. The controller also responded efficiently to unexpected, rapid temperature changes.

  4. Design and fuzzy logic control of an active wrist orthosis.

    PubMed

    Kilic, Ergin; Dogan, Erdi

    2017-08-01

    People who perform excessive wrist movements throughout the day because of their professions have a higher risk of developing lateral and medial epicondylitis. If proper precautions are not taken against these diseases, serious consequences such as job loss and early retirement can occur. In this study, the design and control of an active wrist orthosis that is mobile, powerful and lightweight is presented as a means to avoid the occurrence and/or for the treatment of repetitive strain injuries in an effective manner. The device has an electromyography-based control strategy so that the user's intention always comes first. In fact, the device-user interaction is mainly activated by the electromyography signals measured from the forearm muscles that are responsible for the extension and flexion wrist movements. Contractions of the muscles are detected using surface electromyography sensors, and the desired quantity of the velocity value of the wrist is extracted from a fuzzy logic controller. Then, the actuator system of the device comes into play by conveying the necessary motion support to the wrist. Experimental studies show that the presented device actually reduces the demand on the muscles involved in repetitive strain injuries while performing challenging daily life activities including extension and flexion wrist motions.

  5. Mobile health in maternal and newborn care: fuzzy logic.

    PubMed

    Premji, Shahirose

    2014-06-01

    Whether mHealth improves maternal and newborn health outcomes remains uncertain as the response is perhaps not true or false but lies somewhere in between when considering unintended harmful consequences. Fuzzy logic, a mathematical approach to computing, extends the traditional binary “true or false” (one or zero) to exemplify this notion of partial truths that lies between completely true and false. The commentary explores health, socio-ecological and environmental consequences–positive, neutral or negative. Of particular significance is the negative influence of mHealth on maternal care-behaviors, which can increase stress reactivity and vulnerability to stress-induced illness across the lifespan of the child and establish pathways for intergenerational transmission of behaviors. A mHealth “fingerprinting” approach is essential to monitor psychosocial, economic, cultural, environmental and physical impact of mHealth intervention and make evidence-informed decision(s) about use of mHealth in maternal and newborn care.

  6. Remote triage support algorithm based on fuzzy logic.

    PubMed

    Achkoski, Jugoslav; Koceski, S; Bogatinov, D; Temelkovski, B; Stevanovski, G; Kocev, I

    2017-06-01

    This paper presents a remote triage support algorithm as a part of a complex military telemedicine system which provides continuous monitoring of soldiers' vital sign data gathered on-site using unobtrusive set of sensors. The proposed fuzzy logic-based algorithm takes physiological data and classifies the casualties according to their health risk level, calculated following the Modified Early Warning Score (MEWS) methodology. To verify the algorithm, eight different evaluation scenarios using random vital sign data have been created. In each scenario, the hypothetical condition of the victims was assessed in parallel both by the system as well as by 50 doctors with significant experience in the field. The results showed that there is high (0.928) average correlation of the classification results. This suggests that the proposed algorithm can be used for automated remote triage in real life-saving situations even before the medical team arrives at the spot, and shorten the response times. Moreover, an additional study has been conducted in order to increase the computational efficiency of the algorithm, without compromising the quality of the classification results. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  7. A new adaptive configuration of PID type fuzzy logic controller.

    PubMed

    Fereidouni, Alireza; Masoum, Mohammad A S; Moghbel, Moayed

    2015-05-01

    In this paper, an adaptive configuration for PID type fuzzy logic controller (FLC) is proposed to improve the performances of both conventional PID (C-PID) controller and conventional PID type FLC (C-PID-FLC). The proposed configuration is called adaptive because its output scaling factors (SFs) are dynamically tuned while the controller is functioning. The initial values of SFs are calculated based on its well-tuned counterpart while the proceeding values are generated using a proposed stochastic hybrid bacterial foraging particle swarm optimization (h-BF-PSO) algorithm. The performance of the proposed configuration is evaluated through extensive simulations for different operating conditions (changes in reference, load disturbance and noise signals). The results reveal that the proposed scheme performs significantly better over the C-PID controller and the C-PID-FLC in terms of several performance indices (integral absolute error (IAE), integral-of-time-multiplied absolute error (ITAE) and integral-of-time-multiplied squared error (ITSE)), overshoot and settling time for plants with and without dead time. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  8. Fuzzy logic and neural networks in artificial intelligence and pattern recognition

    NASA Astrophysics Data System (ADS)

    Sanchez, Elie

    1991-10-01

    With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

  9. Fuzzy-Logic Based Vibration Suppression Control Experiments on Active Structures

    NASA Astrophysics Data System (ADS)

    Kwak, M. K.; Sciulli, D.

    1996-03-01

    This paper is concerned with the fuzzy-logic based vibration suppression control of active structures equipped with piezoelectric sensors and actuators. The control methodology is based on the fuzzy logic control of the variable structures system type. The sufficient condition for the closed-loop stability of the decentralized fuzzy control for the system equipped with collocated sensors and actuators is derived from the sufficient condition of the decentralized collocated variable system control. Hence, it is concluded that the fuzzy control is in fact the variation of the variable structure system control in this case. Comparison of the variable structure system to the fuzzy control leads to a new fuzzy rule of the vibration suppression of the active structure equipped with collocated sensors and actuators. It is shown that the fuzzy-logic control can be designed for the collocated system without any knowledge of the system to be controlled. However, this may not be true in the case of multi-input and multi-output non-collocated systems. All the developments are demonstrated by means of a real-time fuzzy control experiment on the cantilever beam with surface-bonded piezoceramic sensors and actuators.

  10. Fuzzy logic based intelligent control of a variable speed cage machine wind generation system

    SciTech Connect

    Simoes, M.G.; Bose, B.K.; Spiegel, R.J.

    1997-01-01

    The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The generation system has fuzzy logic control with vector control in the inner loops. A fuzzy controller tracks the generator speed with the wind velocity to extract the maximum power. A second fuzzy controller programs the machine flux for light load efficiency improvement, and a third fuzzy controller gives robust speed control against wind gust and turbine oscillatory torque. The complete control system has been developed, analyzed, and validated by simulation study. Performances have then been evaluated in detail.

  11. Application of Fuzzy-Logic Controller and Neural Networks Controller in Gas Turbine Speed Control and Overheating Control and Surge Control on Transient Performance

    NASA Astrophysics Data System (ADS)

    Torghabeh, A. A.; Tousi, A. M.

    2007-08-01

    This paper presents Fuzzy Logic and Neural Networks approach to Gas Turbine Fuel schedules. Modeling of non-linear system using feed forward artificial Neural Networks using data generated by a simulated gas turbine program is introduced. Two artificial Neural Networks are used , depicting the non-linear relationship between gas generator speed and fuel flow, and turbine inlet temperature and fuel flow respectively . Off-line fast simulations are used for engine controller design for turbojet engine based on repeated simulation. The Mamdani and Sugeno models are used to expression the Fuzzy system . The linguistic Fuzzy rules and membership functions are presents and a Fuzzy controller will be proposed to provide an Open-Loop control for the gas turbine engine during acceleration and deceleration . MATLAB Simulink was used to apply the Fuzzy Logic and Neural Networks analysis. Both systems were able to approximate functions characterizing the acceleration and deceleration schedules . Surge and Flame-out avoidance during acceleration and deceleration phases are then checked . Turbine Inlet Temperature also checked and controls by Neural Networks controller. This Fuzzy Logic and Neural Network Controllers output results are validated and evaluated by GSP software . The validation results are used to evaluate the generalization ability of these artificial Neural Networks and Fuzzy Logic controllers.

  12. A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control.

    PubMed

    Ajiboye, Abidemi Bolu; Weir, Richard F ff

    2005-09-01

    This paper presents a heuristic fuzzy logic approach to multiple electromyogram (EMG) pattern recognition for multifunctional prosthesis control. Basic signal statistics (mean and standard deviation) are used for membership function construction, and fuzzy c-means (FCMs) data clustering is used to automate the construction of a simple amplitude-driven inference rule base. The result is a system that is transparent to, and easily "tweaked" by, the prosthetist/clinician. Other algorithms in current literature assume a longer period of unperceivable delay, while the system we present has an update rate of 45.7 ms with little postprocessing time, making it suitable for real-time application. Five subjects were investigated (three with intact limbs, one with a unilateral transradial amputation, and one with a unilateral transradial limb-deficiency from birth). Four subjects were used for system offline analysis, and the remaining intact-limbed subject was used for system real-time analysis. We discriminated between four EMG patterns for subjects with intact limbs, and between three patterns for limb-deficient subjects. Overall classification rates ranged from 94% to 99%. The fuzzy algorithm also demonstrated success in real-time classification, both during steady state motions and motion state transitioning. This functionality allows for seamless control of multiple degrees-of-freedom in a multifunctional prosthesis.

  13. Fuzzy sets and data analysis.

    NASA Astrophysics Data System (ADS)

    di Gesù, V.

    Methods and their applications to data analysis problems in fuzzy-sets theory are presented. Fuzzy-sets theory seems to be a powerful tool to model uncertainty and vagueness present in the data and to represent the human thinking in a more natural way.

  14. Ensemble of ground subsidence hazard maps using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Park, Inhye; Lee, Jiyeong; Saro, Lee

    2014-06-01

    Hazard maps of ground subsidence around abandoned underground coal mines (AUCMs) in Samcheok, Korea, were constructed using fuzzy ensemble techniques and a geographical information system (GIS). To evaluate the factors related to ground subsidence, a spatial database was constructed from topographic, geologic, mine tunnel, land use, groundwater, and ground subsidence maps. Spatial data, topography, geology, and various ground-engineering data for the subsidence area were collected and compiled in a database for mapping ground-subsidence hazard (GSH). The subsidence area was randomly split 70/30 for training and validation of the models. The relationships between the detected ground-subsidence area and the factors were identified and quantified by frequency ratio (FR), logistic regression (LR) and artificial neural network (ANN) models. The relationships were used as factor ratings in the overlay analysis to create ground-subsidence hazard indexes and maps. The three GSH maps were then used as new input factors and integrated using fuzzy-ensemble methods to make better hazard maps. All of the hazard maps were validated by comparison with known subsidence areas that were not used directly in the analysis. As the result, the ensemble model was found to be more effective in terms of prediction accuracy than the individual model.

  15. Methodological development of fuzzy-logic controllers from multivariable linear control.

    PubMed

    Tso, S K; Fung, Y H

    1997-01-01

    It is the function of the design of a fuzzy-logic controller to determine the universes of discourse of the antecedents and the consequents, number of membership labels, distribution and shape of membership functions, rule formulation, etc. Much of the information is usually extracted from expert knowledge, operator experience, or heuristic thinking. It is hence difficult to mechanize the first-stage design of fuzzy-logic controllers using linguistic labels whose performance is no worse than that of conventional multivariable linear controllers such as state-feedback controllers, PID controllers, etc. In this paper, an original systematic seven-step linear-to-fuzzy (LIN2FUZ) algorithm is proposed for generating the labels, universes of discourse of the antecedents and the consequents, and fuzzy rules of ;basically linear' fuzzy-logic controllers, given the reference design of available conventional multivariable linear controllers. The functionally equivalent fuzzy-logic controllers can thus provide the sound basis for the further development to achieve performance beyond the capability or the conventional controllers. The validity and effectiveness of the proposed LIN2FUZ algorithm are demonstrated by a four-input one-output inverted pendulum system.

  16. Can fuzzy logic bring complex problems into focus? Modeling imprecise factors in environmental policy

    SciTech Connect

    McKone, Thomas E.; Deshpande, Ashok W.

    2004-06-14

    In modeling complex environmental problems, we often fail to make precise statements about inputs and outcome. In this case the fuzzy logic method native to the human mind provides a useful way to get at these problems. Fuzzy logic represents a significant change in both the approach to and outcome of environmental evaluations. Risk assessment is currently based on the implicit premise that probability theory provides the necessary and sufficient tools for dealing with uncertainty and variability. The key advantage of fuzzy methods is the way they reflect the human mind in its remarkable ability to store and process information which is consistently imprecise, uncertain, and resistant to classification. Our case study illustrates the ability of fuzzy logic to integrate statistical measurements with imprecise health goals. But we submit that fuzzy logic and probability theory are complementary and not competitive. In the world of soft computing, fuzzy logic has been widely used and has often been the ''smart'' behind smart machines. But it will require more effort and case studies to establish its niche in risk assessment or other types of impact assessment. Although we often hear complaints about ''bright lines,'' could we adapt to a system that relaxes these lines to fuzzy gradations? Would decision makers and the public accept expressions of water or air quality goals in linguistic terms with computed degrees of certainty? Resistance is likely. In many regions, such as the US and European Union, it is likely that both decision makers and members of the public are more comfortable with our current system in which government agencies avoid confronting uncertainties by setting guidelines that are crisp and often fail to communicate uncertainty. But some day perhaps a more comprehensive approach that includes exposure surveys, toxicological data, epidemiological studies coupled with fuzzy modeling will go a long way in resolving some of the conflict, divisiveness

  17. Fuzzy-logic-based resource allocation for isolated and multiple platforms

    NASA Astrophysics Data System (ADS)

    Smith, James F., III; Rhyne, Robert D., II

    2000-08-01

    Modern naval battle forces generally include many different platforms each with its own sensors, radar, ESM, and communications. The sharing of information measured by local sensors via communication links across the battle group should allow for optimal or near optimal decision. The survival of the battle group or members of the group depends on the automatic real-time allocation of various resources. A fuzzy logic algorithm has been developed that automatically allocates electronic attack resources in real- time. The particular approach to fuzzy logic that is used is the fuzzy decision tree, a generalization of the standard artificial intelligence technique of decision trees. The controller must be able to make decisions based on rules provided by experts. The fuzzy logic approach allows the direct incorporation of expertise forming a fuzzy linguistic description, i.e. a formal representation of the system in terms of fuzzy if-then rules. Genetic algorithm based optimization is conducted to determine the form of the membership functions for the fuzzy root concepts. The isolated platform and multi platform resource manager models are discussed as well as the underlying multi-platform communication model. The resource manager is shown to exhibit excellent performance under many demanding scenarios.

  18. Integration of Genetic Algorithms and Fuzzy Logic for Urban Growth Modeling

    NASA Astrophysics Data System (ADS)

    Foroutan, E.; Delavar, M. R.; Araabi, B. N.

    2012-07-01

    Urban growth phenomenon as a spatio-temporal continuous process is subject to spatial uncertainty. This inherent uncertainty cannot be fully addressed by the conventional methods based on the Boolean algebra. Fuzzy logic can be employed to overcome this limitation. Fuzzy logic preserves the continuity of dynamic urban growth spatially by choosing fuzzy membership functions, fuzzy rules and the fuzzification-defuzzification process. Fuzzy membership functions and fuzzy rule sets as the heart of fuzzy logic are rather subjective and dependent on the expert. However, due to lack of a definite method for determining the membership function parameters, certain optimization is needed to tune the parameters and improve the performance of the model. This paper integrates genetic algorithms and fuzzy logic as a genetic fuzzy system (GFS) for modeling dynamic urban growth. The proposed approach is applied for modeling urban growth in Tehran Metropolitan Area in Iran. Historical land use/cover data of Tehran Metropolitan Area extracted from the 1988 and 1999 Landsat ETM+ images are employed in order to simulate the urban growth. The extracted land use classes of the year 1988 include urban areas, street, vegetation areas, slope and elevation used as urban growth physical driving forces. Relative Operating Characteristic (ROC) curve as an fitness function has been used to evaluate the performance of the GFS algorithm. The optimum membership function parameter is applied for generating a suitability map for the urban growth. Comparing the suitability map and real land use map of 1999 gives the threshold value for the best suitability map which can simulate the land use map of 1999. The simulation outcomes in terms of kappa of 89.13% and overall map accuracy of 95.58% demonstrated the efficiency and reliability of the proposed model.

  19. Design of adaptive fuzzy logic controller based on linguistic-hedge concepts and genetic algorithms.

    PubMed

    Liu, B D; Chen, C Y; Tsao, J Y

    2001-01-01

    In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.

  20. Mapping vulnerability of multiple aquifers using multiple models and fuzzy logic to objectively derive model structures.

    PubMed

    Nadiri, Ata Allah; Sedghi, Zahra; Khatibi, Rahman; Gharekhani, Maryam

    2017-09-01

    Driven by contamination risks, mapping Vulnerability Indices (VI) of multiple aquifers (both unconfined and confined) is investigated by integrating the basic DRASTIC framework with multiple models overarched by Artificial Neural Networks (ANN). The DRASTIC framework is a proactive tool to assess VI values using the data from the hydrosphere, lithosphere and anthroposphere. However, a research case arises for the application of multiple models on the ground of poor determination coefficients between the VI values and non-point anthropogenic contaminants. The paper formulates SCFL models, which are derived from the multiple model philosophy of Supervised Committee (SC) machines and Fuzzy Logic (FL) and hence SCFL as their integration. The Fuzzy Logic-based (FL) models include: Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Larsen Fuzzy Logic (LFL) models. The basic DRASTIC framework uses prescribed rating and weighting values based on expert judgment but the four FL-based models (SFL, MFL, LFL and SCFL) derive their values as per internal strategy within these models. The paper reports that FL and multiple models improve considerably on the correlation between the modeled vulnerability indices and observed nitrate-N values and as such it provides evidence that the SCFL multiple models can be an alternative to the basic framework even for multiple aquifers. The study area with multiple aquifers is in Varzeqan plain, East Azerbaijan, northwest Iran. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Assessment of groundwater vulnerability using supervised committee to combine fuzzy logic models.

    PubMed

    Nadiri, Ata Allah; Gharekhani, Maryam; Khatibi, Rahman; Moghaddam, Asghar Asghari

    2017-03-01

    Vulnerability indices of an aquifer assessed by different fuzzy logic (FL) models often give rise to differing values with no theoretical or empirical basis to establish a validated baseline or to develop a comparison basis between the modeling results and baselines, if any. Therefore, this research presents a supervised committee fuzzy logic (SCFL) method, which uses artificial neural networks to overarch and combine a selection of FL models. The indices are expressed by the widely used DRASTIC framework, which include geological, hydrological, and hydrogeological parameters often subject to uncertainty. DRASTIC indices represent collectively intrinsic (or natural) vulnerability and give a sense of contaminants, such as nitrate-N, percolating to aquifers from the surface. The study area is an aquifer in Ardabil plain, the province of Ardabil, northwest Iran. Improvements on vulnerability indices are achieved by FL techniques, which comprise Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), and Larsen fuzzy logic (LFL). As the correlation between estimated DRASTIC vulnerability index values and nitrate-N values is as low as 0.4, it is improved significantly by FL models (SFL, MFL, and LFL), which perform in similar ways but have differences. Their synergy is exploited by SCFL and uses the FL modeling results "conditioned" by nitrate-N values to raise their correlation to higher than 0.9.

  2. Prevention of dialysis hypotension episodes using fuzzy logic control system.

    PubMed

    Mancini, Elena; Mambelli, Emanuele; Irpinia, Mina; Gabrielli, Danila; Cascone, Carmelo; Conte, Ferruccio; Meneghel, Gina; Cavatorta, Fosco; Antonelli, Alessandro; Villa, Giuseppe; Dal Canton, Antonio; Cagnoli, Leonardo; Aucella, Filippo; Fiorini, Fulvio; Gaggiotti, Enzo; Triolo, Giorgio; Nuzzo, Vitale; Santoro, Antonio

    2007-05-01

    Automatic systems for stabilizing blood pressure (BP) during dialysis are few and only control those variables indirectly related to BP. Due to complex BP regulation under dynamic dialysis conditions, BP itself appears to be the most consistent input parameter for a device addressed to preventing dialysis hypotension (DH). An automatic system (ABPS, automatic blood pressure stabilization) for BP control by fluid removal feedback regulation is implemented on a dialysis machine (Dialog Advanced, Braun). A fuzzy logic (FL) control runs in the system, using instantaneous BP as the input variable governing the ultrafiltration rate (UFR) according to the BP trend. The system is user-friendly and just requires the input of two data: critical BP (individually defined as the possible level of DH risk) and the highest UFR applicable (percentage of the mean UFR). We evaluated this system's capacity to prevent DH in 55 RDT hypotension-prone patients. Sessions with (treatment A) and without (treatment B) ABPS were alternated one-by-one for 30 dialysis sessions per patient (674 with ABPS vs 698 without). Despite comparable treatment times and UF volumes, severe DH appeared in 8.3% of sessions in treatment A vs 13.8% in treatment B (-39%, P=0.01). Mild DH fell non-significantly (-12.3%). There was a similar percentage of sessions in which the planned body weight loss was not achieved and dialysis time was prolonged. In conclusion, FL may be suited to interpreting and controlling the trend of a determined multi-variable parameter like BP. The medical knowledge of the patient and the consequent updating of input parameters depending on the patient's clinical conditions seem to be the main factors for obtaining optimal results.

  3. A novel approach to microcalcification detection using fuzzy logic technique.

    PubMed

    Cheng, H D; Lui, Y M; Freimanis, R I

    1998-06-01

    Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach.

  4. North American Fuzzy Logic Processing Society (NAFIPS 1992), volume 1

    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 as neural networks and genetic algorithms. NAFIPS '92 was directed toward the advancement, commercialization, and engineering development of these technologies.

  5. 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.

  6. 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}

  7. An Application of Fuzzy Logic Control to a Classical Military Tracking Problem

    DTIC Science & Technology

    1994-05-19

    34Probability Measures of Fuzzy Events", Journal of Mathematical Analysis and Applications , vol.23, 1968, p.421. 9. Kosko, Bart. "Fuzziness Versus...January 1973, pp.28-44. Zadeh, L.A. "Probability Measures of Fuzzy Events", Journal of Mathematical Analysis and Applications , vol.23, 1968, pp.421

  8. Synthesis of nonlinear control strategies from fuzzy logic control algorithms

    NASA Technical Reports Server (NTRS)

    Langari, Reza

    1993-01-01

    Fuzzy control has been recognized as an alternative to conventional control techniques in situations where the plant model is not sufficiently well known to warrant the application of conventional control techniques. Precisely what fuzzy control does and how it does what it does is not quite clear, however. This important issue is discussed and in particular it is shown how a given fuzzy control scheme can resolve into a nonlinear control law and that in those situations the success of fuzzy control hinges on its ability to compensate for nonlinearities in plant dynamics.

  9. Using fuzzy logic to integrate neural networks and knowledge-based systems

    NASA Technical Reports Server (NTRS)

    Yen, John

    1991-01-01

    Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

  10. Modelling of the automatic stabilization system of the aircraft course by a fuzzy logic method

    NASA Astrophysics Data System (ADS)

    Mamonova, T.; Syryamkin, V.; Vasilyeva, T.

    2016-04-01

    The problem of the present paper concerns the development of a fuzzy model of the system of an aircraft course stabilization. In this work modelling of the aircraft course stabilization system with the application of fuzzy logic is specified. Thus the authors have used the data taken for an ordinary passenger plane. As a result of the study the stabilization system models were realised in the environment of Matlab package Simulink on the basis of the PID-regulator and fuzzy logic. The authors of the paper have shown that the use of the method of artificial intelligence allows reducing the time of regulation to 1, which is 50 times faster than the time when standard receptions of the management theory are used. This fact demonstrates a positive influence of the use of fuzzy regulation.

  11. Design and Implementation of Takagi-Sugeno Fuzzy Logic Controller for Shunt Compensator

    NASA Astrophysics Data System (ADS)

    Singh, Alka; Badoni, Manoj

    2016-12-01

    This paper describes the application of Takagi-Sugeno (TS) type fuzzy logic controller to a three-phase shunt compensator in power distribution system. The shunt compensator is used for power quality improvement and has the ability to provide reactive power compensation, reduce the level of harmonics in supply currents, power factor correction and load balancing. Additionally, it can also be used to regulate voltage at the point of common coupling (PCC). The paper discusses the design of TS fuzzy logic controller and its implementation based on only four rules. The smaller number of rules makes it suitable for experimental verification as compared to Mamdani fuzzy controller. A small laboratory prototype of the system is developed and the control algorithm is verified experimentally. The TS fuzzy controller is compared with the proportional integral based industrial controller and their performance is compared under a wide variation of dynamic load changes.

  12. A fuzzy logic approach to modeling the underground economy in Taiwan

    NASA Astrophysics Data System (ADS)

    Yu, Tiffany Hui-Kuang; Wang, David Han-Min; Chen, Su-Jane

    2006-04-01

    The size of the ‘underground economy’ (UE) is valuable information in the formulation of macroeconomic and fiscal policy. This study applies fuzzy set theory and fuzzy logic to model Taiwan's UE over the period from 1960 to 2003. Two major factors affecting the size of the UE, the effective tax rate and the degree of government regulation, are used. The size of Taiwan's UE is scaled and compared with those of other models. Although our approach yields different estimates, similar patterns and leading are exhibited throughout the period. The advantage of applying fuzzy logic is twofold. First, it can avoid the complex calculations in conventional econometric models. Second, fuzzy rules with linguistic terms are easy for human to understand.

  13. The fundamental Fuzzy logic operators and some complex boolean logic circuits implemented by the chromogenism of a spirooxazine.

    PubMed

    Gentili, Pier Luigi

    2011-12-07

    1,3-Dihydro-1,3,3-trimethyl-8'-nitro-spiro[2H-indole-2,3'-[3H]naphth[2,1-b][1,4]oxazine] (SpO) is a photochromic, acidichromic and metallochromic compound. Its chromogenic properties are characterized in acetonitrile, at room temperature. They are exploited to process both boolean and Fuzzy logic. By using HClO(4), AlCl(3) and Cu(ClO(4))(2) as chemical inputs, UV radiation as power supply, and the absorbance at specific wavelengths in the visible as optical output, SpO results in a five-states molecular switch whereby some complex boolean logic circuits are implemented. If the chemical inputs are varied in an analog manner, the solution of SpO assumes an infinite number of colours. Therefore, by choosing the RGB colour coordinates as optical outputs, the fundamental operators of the "infinite-valued" Fuzzy logic are implemented. Particularly, two Fuzzy logic systems are built upon a new defuzzification procedure imitating the way humans perceive colours.

  14. Fuzzy Logic and Glacier Dynamics Assessment: New Paradigms for Operational Hazard Detection Systems

    NASA Astrophysics Data System (ADS)

    Furfaro, R.; Kargel, J. S.; Fink, W.; Bishop, M. P.

    2008-12-01

    Satellite-based remote sensing is critical for monitoring highly dynamic environments that include rapidly changing alpine glaciers, melt-water production, and a variety of natural hazards. Multi-spectral and multi- temporal satellite data in conjunction with digital elevation models can be utilized to assess supraglacial and proglacial lakes, valley impoundment water volumes, and the potential for flood and debris-flow hazards. Advanced remote sensing and GIS-based methodologies represent the only effective approach for periodic assessment and detection of glacier hazards using spatio-temporal data and analysis. Such approaches, however, do not address all of the requirements needed for the development of hazard/disaster warning systems and the generation of unique information to help establish mitigation strategies. Consequently, our objectives are to introduce the methods of fuzzy logic as an additional level of analysis and interpretation to demonstrate how intelligent, knowledge-driven algorithms can be used to assess glacier dynamics and glacier-induced hazards. Operational monitoring of dynamic environments and natural hazards will require multiple levels of analysis and information production using on-board automation. These systems must autonomously assess the hazard potential related to surface processes and the topography, while being able to identify disaster conditions. Such systems should (1) include analytical capabilities to permit automated and comprehensive identification, characterization, and quantification of terrain features (e.g., via Automated Global Feature Analyzer "AGFA"); (2) permit operational multi-scale hazard potential assessment (e.g., automatic global, regional and local assessment capabilities); and (3) permit data integration that fuses existing data and real-time data acquisition into a spatio-temporal framework that facilitates intelligent assessment and monitoring. The fuzzy logic framework may be an ideal approach that serves

  15. Evaluating environmental sustainability: an integration of multiple-criteria decision-making and fuzzy logic.

    PubMed

    Liu, Kevin F R

    2007-05-01

    While pursuing economic development, countries around the world have become aware of the importance of environmental sustainability; therefore, the evaluation of environmental sustainability has become a significant issue. Traditionally, multiple-criteria decision-making (MCDM) was widely used as a way of evaluating environmental sustainability, Recently, several researchers have attempted to implement this evaluation with fuzzy logic since they recognized the assessment of environmental sustainability as a subjective judgment Intuition. This paper outlines a new evaluation-framework of environmental sustainability, which integrates fuzzy logic into MCDM. This evaluation-framework consists of 36 structured and 5 unstructured decision-points, wherein MCDM is used to handle the former and fuzzy logic serves for the latter, With the integrated evaluation-framework, the evaluations of environmental sustainability in 146 countries are calculated, ranked and clustered, and the evaluation results are very helpful to these countries, as they identify their obstacles towards environmental sustainability.

  16. Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control

    NASA Astrophysics Data System (ADS)

    Othman, Ahmed M.; El-arini, Mahdi M. M.; Ghitas, Ahmed; Fathy, Ahmed

    2012-12-01

    In the recent years, the solar energy becomes one of the most important alternative sources of electric energy, so it is important to improve the efficiency and reliability of the photovoltaic (PV) systems. Maximum power point tracking (MPPT) plays an important role in photovoltaic power systems because it maximize the power output from a PV system for a given set of conditions, and therefore maximize their array efficiency. This paper presents a maximum power point tracker (MPPT) using Fuzzy Logic theory for a PV system. The work is focused on the well known Perturb and Observe (P&O) algorithm and is compared to a designed fuzzy logic controller (FLC). The simulation work dealing with MPPT controller; a DC/DC Ćuk converter feeding a load is achieved. The results showed that the proposed Fuzzy Logic MPPT in the PV system is valid.

  17. Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic

    DOEpatents

    Feddema, John T.; Driessen, Brian J.; Kwok, Kwan S.

    2002-01-01

    A decentralized fuzzy logic control system for one vehicle or for multiple robotic vehicles provides a way to control each vehicle to converge on a goal without collisions between vehicles or collisions with other obstacles, in the presence of noisy input measurements and a limited amount of compute-power and memory on board each robotic vehicle. The fuzzy controller demonstrates improved robustness to noise relative to an exact controller.

  18. Assessment of Benefits and Drawbacks of Using Fuzzy Logic, Especially in Fire Control Systems

    DTIC Science & Technology

    1994-03-01

    classitied by :W. Pelt classitication date :September 20, 1993 jp:op:ved ci.’ :::: - - - - title :ongerubriceerd manage mentuittreksel :Ongerubriceerd...kanonsysteem het gebruik van "fuzzy reasoning" technieken voorkwam. Dit voorstel is gedaan door SIGNAAL, in een opdracht van de KM. Het TNO-FEL had een...van bet gebruik van fuzzy logic in vergelijking met ’conventionele’ methoden. Geconcludeerd wordt dat er in principe goede mogelijkhieden voor

  19. A fuzzy logic based spacecraft controller for six degree of freedom control and performance results

    NASA Technical Reports Server (NTRS)

    Lea, Robert N.; Hoblit, Jeffrey; Jani, Yashvant

    1991-01-01

    The development philosophy of the fuzzy logic controller is explained, details of the rules and membership functions used are given, and the early results of testing of the control system for a representative range of scenarios are reported. The fuzzy attitude controller was found capable of performing all rotational maneuvers, including rate hold and rate maneuvers. It handles all orbital perturbations very efficiently and is very responsive in correcting errors.

  20. A Fuzzy Logic Prompting Mechanism Based on Pattern Recognition and Accumulated Activity Effective Index Using a Smartphone Embedded Sensor.

    PubMed

    Liu, Chung-Tse; Chan, Chia-Tai

    2016-08-19

    Sufficient physical activity can reduce many adverse conditions and contribute to a healthy life. Nevertheless, inactivity is prevalent on an international scale. Improving physical activity is an essential concern for public health. Reminders that help people change their health behaviors are widely applied in health care services. However, timed-based reminders deliver periodic prompts suffer from flexibility and dependency issues which may decrease prompt effectiveness. We propose a fuzzy logic prompting mechanism, Accumulated Activity Effective Index Reminder (AAEIReminder), based on pattern recognition and activity effective analysis to manage physical activity. AAEIReminder recognizes activity levels using a smartphone-embedded sensor for pattern recognition and analyzing the amount of physical activity in activity effective analysis. AAEIReminder can infer activity situations such as the amount of physical activity and days spent exercising through fuzzy logic, and decides whether a prompt should be delivered to a user. This prompting system was implemented in smartphones and was used in a short-term real-world trial by seventeenth participants for validation. The results demonstrated that the AAEIReminder is feasible. The fuzzy logic prompting mechanism can deliver prompts automatically based on pattern recognition and activity effective analysis. AAEIReminder provides flexibility which may increase the prompts' efficiency.

  1. A Fuzzy Logic Prompting Mechanism Based on Pattern Recognition and Accumulated Activity Effective Index Using a Smartphone Embedded Sensor

    PubMed Central

    Liu, Chung-Tse; Chan, Chia-Tai

    2016-01-01

    Sufficient physical activity can reduce many adverse conditions and contribute to a healthy life. Nevertheless, inactivity is prevalent on an international scale. Improving physical activity is an essential concern for public health. Reminders that help people change their health behaviors are widely applied in health care services. However, timed-based reminders deliver periodic prompts suffer from flexibility and dependency issues which may decrease prompt effectiveness. We propose a fuzzy logic prompting mechanism, Accumulated Activity Effective Index Reminder (AAEIReminder), based on pattern recognition and activity effective analysis to manage physical activity. AAEIReminder recognizes activity levels using a smartphone-embedded sensor for pattern recognition and analyzing the amount of physical activity in activity effective analysis. AAEIReminder can infer activity situations such as the amount of physical activity and days spent exercising through fuzzy logic, and decides whether a prompt should be delivered to a user. This prompting system was implemented in smartphones and was used in a short-term real-world trial by seventeenth participants for validation. The results demonstrated that the AAEIReminder is feasible. The fuzzy logic prompting mechanism can deliver prompts automatically based on pattern recognition and activity effective analysis. AAEIReminder provides flexibility which may increase the prompts’ efficiency. PMID:27548184

  2. Fuzzy Logic: Toward Measuring Gottfredson's Concept of Occupational Social Space.

    ERIC Educational Resources Information Center

    Hesketh, Beryl; And Others

    1989-01-01

    Investigated the application of fuzzy graphic rating scale to measurement of preferences for occupational sex type, prestige, and interests using Gottfredson's concept of occupational social space. Reported reliability and validity data with illustrative examples of respondents' interpretations of their own fuzzy ratings. Outlined counseling and…

  3. Fuzzy Logic: Toward Measuring Gottfredson's Concept of Occupational Social Space.

    ERIC Educational Resources Information Center

    Hesketh, Beryl; And Others

    1989-01-01

    Investigated the application of fuzzy graphic rating scale to measurement of preferences for occupational sex type, prestige, and interests using Gottfredson's concept of occupational social space. Reported reliability and validity data with illustrative examples of respondents' interpretations of their own fuzzy ratings. Outlined counseling and…

  4. A controller based on Optimal Type-2 Fuzzy Logic: systematic design, optimization and real-time implementation.

    PubMed

    Fayek, H M; Elamvazuthi, I; Perumal, N; Venkatesh, B

    2014-09-01

    A computationally-efficient systematic procedure to design an Optimal Type-2 Fuzzy Logic Controller (OT2FLC) is proposed. The main scheme is to optimize the gains of the controller using Particle Swarm Optimization (PSO), then optimize only two parameters per type-2 membership function using Genetic Algorithm (GA). The proposed OT2FLC was implemented in real-time to control the position of a DC servomotor, which is part of a robotic arm. The performance judgments were carried out based on the Integral Absolute Error (IAE), as well as the computational cost. Various type-2 defuzzification methods were investigated in real-time. A comparative analysis with an Optimal Type-1 Fuzzy Logic Controller (OT1FLC) and a PI controller, demonstrated OT2FLC׳s superiority; which is evident in handling uncertainty and imprecision induced in the system by means of noise and disturbances. Copyright © 2014 ISA. Published by Elsevier Ltd. All rights reserved.

  5. a Fuzzy Logic-Based Approach for the Detection of Flooded Vegetation by Means of Synthetic Aperture Radar Data

    NASA Astrophysics Data System (ADS)

    Tsyganskaya, V.; Martinis, S.; Twele, A.; Cao, W.; Schmitt, A.; Marzahn, P.; Ludwig, R.

    2016-06-01

    In this paper an algorithm designed to map flooded vegetation from synthetic aperture radar (SAR) imagery is introduced. The approach is based on fuzzy logic which enables to deal with the ambiguity of SAR data and to integrate multiple ancillary data containing topographical information, simple hydraulic considerations and land cover information. This allows the exclusion of image elements with a backscatter value similar to flooded vegetation, to significantly reduce misclassification errors. The flooded vegetation mapping procedure is tested on a flood event that occurred in Germany over parts of the Saale catchment on January 2011 using a time series of high resolution TerraSAR-X data covering the time interval from 2009 to 2015. The results show that the analysis of multi-temporal X-band data combined with ancillary data using a fuzzy logic-based approach permits the detection of flooded vegetation areas.

  6. Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

    2001-01-01

    This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

  7. Robust Fault Detection Using Robust Z1 Estimation and Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Curry, Tramone; Collins, Emmanuel G., Jr.; Selekwa, Majura; Guo, Ten-Huei (Technical Monitor)

    2001-01-01

    This research considers the application of robust Z(sub 1), estimation in conjunction with fuzzy logic to robust fault detection for an aircraft fight control system. It begins with the development of robust Z(sub 1) estimators based on multiplier theory and then develops a fixed threshold approach to fault detection (FD). It then considers the use of fuzzy logic for robust residual evaluation and FD. Due to modeling errors and unmeasurable disturbances, it is difficult to distinguish between the effects of an actual fault and those caused by uncertainty and disturbance. Hence, it is the aim of a robust FD system to be sensitive to faults while remaining insensitive to uncertainty and disturbances. While fixed thresholds only allow a decision on whether a fault has or has not occurred, it is more valuable to have the residual evaluation lead to a conclusion related to the degree of, or probability of, a fault. Fuzzy logic is a viable means of determining the degree of a fault and allows the introduction of human observations that may not be incorporated in the rigorous threshold theory. Hence, fuzzy logic can provide a more reliable and informative fault detection process. Using an aircraft flight control system, the results of FD using robust Z(sub 1) estimation with a fixed threshold are demonstrated. FD that combines robust Z(sub 1) estimation and fuzzy logic is also demonstrated. It is seen that combining the robust estimator with fuzzy logic proves to be advantageous in increasing the sensitivity to smaller faults while remaining insensitive to uncertainty and disturbances.

  8. A PI-fuzzy logic controller for the regulation of blood glucose level in diabetic patients.

    PubMed

    Ibbini, M

    2006-01-01

    This manuscript investigates different fuzzy logic controllers for the regulation of blood glucose level in diabetic patients. While fuzzy logic control is still intuitive and at a very early stage, it has already been implemented in many industrial plants and reported results are very promising. A fuzzy logic control (FLC) scheme was recently proposed for maintaining blood glucose level in diabetics within acceptable limits, and was shown to be more effective with better transient characteristics than conventional techniques. In fact, FLC is based on human expertise and on desired output characteristics, and hence does not require precise mathematical models. This observation makes fuzzy rule-based technique very suitable for biomedical systems where models are, in general, either very complicated or over-simplistic. Another attractive feature of fuzzy techniques is their insensitivity to system parameter variations, as numerical values of physiological parameters are often not precise and usually vary from patient to another. PI and PID controllers are very popular and are efficiently used in many industrial plants. Fuzzy PI and PID controllers behave in a similar fashion to those classical controllers with the obvious advantage that the controller parameters are time dependant on the range of the control variables and consequently, result in a better performance. In this manuscript, a fuzzy PI controller is designed using a simplified design scheme and then subjected to simulations of the two common diabetes disturbances--sudden glucose meal and system parameter variations. The performance of the proposed fuzzy PI controller is compared to that of the conventional PID and optimal techniques and is shown to be superior. Moreover, the proposed fuzzy PI controller is shown to be more effective than the previously proposed FLC, especially with respect to the overshoot and settling time.

  9. Multi-objective decision-making under uncertainty: Fuzzy logic methods

    NASA Technical Reports Server (NTRS)

    Hardy, Terry L.

    1994-01-01

    Selecting the best option among alternatives is often a difficult process. This process becomes even more difficult when the evaluation criteria are vague or qualitative, and when the objectives vary in importance and scope. Fuzzy logic allows for quantitative representation of vague or fuzzy objectives, and therefore is well-suited for multi-objective decision-making. This paper presents methods employing fuzzy logic concepts to assist in the decision-making process. In addition, this paper describes software developed at NASA Lewis Research Center for assisting in the decision-making process. Two diverse examples are used to illustrate the use of fuzzy logic in choosing an alternative among many options and objectives. One example is the selection of a lunar lander ascent propulsion system, and the other example is the selection of an aeration system for improving the water quality of the Cuyahoga River in Cleveland, Ohio. The fuzzy logic techniques provided here are powerful tools which complement existing approaches, and therefore should be considered in future decision-making activities.

  10. PC based speed control of dc motor using fuzzy logic controller

    SciTech Connect

    Mandal, S.K.; Kanphade, R.D.; Lavekar, K.P.

    1998-07-01

    The dc motor is extensively used as constant speed drive in textile mills, paper mills, printing press, etc.. If the load and supply voltage are time varying, the speed will be changed. Since last few decades the conventional PID controllers are used to maintain the constant speed by controlling the duty ratio of Chopper. Generally, four quadrant chopper is used for regenerative braking and reverse motoring operation. Fuzzy Logic is newly introduced in control system. Fuzzy Control is based on Fuzzy Logic, a logical system which is too much closer in spirit to human thinking and natural language. The Fuzzy Logic Controller (FLC) provides a linguistic control strategy based on knowledge base of the system. Firstly, the machine is started very smoothly from zero to reference speed in the proposed scheme by increasing the duty ratio. Then change and rate of change of speed (dN, dN/dt), change and rate of change input voltage (dV, dV/dt) and load current are input to FLC. The new value of duty ratio is determined from the Fuzzy rule base and defuzzification method. The chopper will be 'ON' according to new duty ratio to maintain the constant speed. The dynamic and steady state performance of the proposed system is better than conventional control system. In this paper mathematical simulation and experimental implementation are carried out to investigate the drive performance.

  11. Nonlinear Aerodynamic Modeling From Flight Data Using Advanced Piloted Maneuvers and Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Brandon, Jay M.; Morelli, Eugene A.

    2012-01-01

    Results of the Aeronautics Research Mission Directorate Seedling Project Phase I research project entitled "Nonlinear Aerodynamics Modeling using Fuzzy Logic" are presented. Efficient and rapid flight test capabilities were developed for estimating highly nonlinear models of airplane aerodynamics over a large flight envelope. Results showed that the flight maneuvers developed, used in conjunction with the fuzzy-logic system identification algorithms, produced very good model fits of the data, with no model structure inputs required, for flight conditions ranging from cruise to departure and spin conditions.

  12. Automated Interpretation of LIBS Spectra using a Fuzzy Logic Inference Engine

    SciTech Connect

    Jeremy J. Hatch; Timothy R. McJunkin; Cynthia Hanson; Jill R. Scott

    2012-02-01

    Automated interpretation of laser-induced breakdown spectroscopy (LIBS) data is necessary due to the plethora of spectra that can be acquired in a relatively short time. However, traditional chemometric and artificial neural network methods that have been employed are not always transparent to a skilled user. A fuzzy logic approach to data interpretation has now been adapted to LIBS spectral interpretation. A fuzzy logic inference engine (FLIE) was used to differentiate between various copper containing and stainless steel alloys as well as unknowns. Results using FLIE indicate a high degree of confidence in spectral assignment.

  13. Fuzzy logic and a risk-based graded approach for developing S/RIDs: An introduction

    SciTech Connect

    Wayland, J.R.

    1996-01-01

    A Standards/Requirements Identification Document (S/RID) is the set of expressed performance expectations, or standards, for a facility. Critical to the development of an integrated standards-based management is the identification of a set of necessary and sufficient standards from a selected set of standards/requirements. There is a need for a formal, rigorous selection process for the S/RIDs. This is the first of three reports that develop a fuzzy logic selection process. In this report the fundamentals of fuzzy logic are discussed as they apply to a risk-based graded approach.

  14. A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.

    PubMed

    Thounaojam, Dalton Meitei; Khelchandra, Thongam; Manglem Singh, Kh; Roy, Sudipta

    2016-01-01

    This paper proposed a shot boundary detection approach using Genetic Algorithm and Fuzzy Logic. In this, the membership functions of the fuzzy system are calculated using Genetic Algorithm by taking preobserved actual values for shot boundaries. The classification of the types of shot transitions is done by the fuzzy system. Experimental results show that the accuracy of the shot boundary detection increases with the increase in iterations or generations of the GA optimization process. The proposed system is compared to latest techniques and yields better result in terms of F1score parameter.

  15. Process capability analysis using fuzzy sets theory

    NASA Astrophysics Data System (ADS)

    Munoz, Jairo

    1993-12-01

    If tolerances are defined by crisp numerical values, several vehicles based on probabilities and classical statistics exist to conduct a process capability analysis. If data are categorical, however, and if they are obtained from subjective evaluations, the existing methods are inappropriate. Furthermore, if specifications are set in lexical terms or are loosely defined, current approaches are impossible to implement. This paper applies fuzzy logic theory to study process capability in the presence of uncertainty and categorical data. Examples are discussed using TIG welding experimental data.

  16. Design and implementation of the tree-based fuzzy logic controller.

    PubMed

    Liu, B D; Huang, C Y

    1997-01-01

    In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search spaces of the fuzzy inference process are largely reduced; the operation of the inference process can be simplified as a one-dimensional matrix operation because of the fuzzy tree approach; and the controller has regular and modular properties, so it is easy to be implemented by hardware. Furthermore, the proposed fuzzy tree approach has been applied to design the color reproduction system for verifying the proposed methodology. The color reproduction system is mainly used to obtain a color image through the printer that is identical to the original one. In addition to the software simulation, an FPGA is used to implement the prototype hardware system for real-time application. Experimental results show that the effect of color correction is quite good and that the prototype hardware system can operate correctly under the condition of 30 MHz clock rate.

  17. A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

    NASA Technical Reports Server (NTRS)

    Shehadeh, Hana; Lea, Robert N.

    1992-01-01

    Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

  18. A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

    NASA Technical Reports Server (NTRS)

    Shehadeh, Hana; Lea, Robert N.

    1992-01-01

    Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

  19. Fuzzy logic: A “simple” solution for complexities in neurosciences?

    PubMed Central

    Godil, Saniya Siraj; Shamim, Muhammad Shahzad; Enam, Syed Ather; Qidwai, Uvais

    2011-01-01

    Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum. Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology. Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures. Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences. PMID:21541006

  20. Validation of Fuzzy Logic Method for Automated Mass Spectral Classification for Mineral Imaging

    SciTech Connect

    B. Yan; B. Yan; T. R. McJunkiin; D. L. Stoner

    2006-12-01

    Imaging mass spectrometry requires the acquisition and interpretation of hundreds to thousands of individual spectra in order to map the mineral phases within heterogeneous geomatrices. A fuzzy logic inference engine (FLIE) was developed to automate data interpretation. To evaluate the strengths and limitations of FLIE, the chemical images obtained using FLIE were compared with those developed using two chemometric methods: principle component analysis (PCA) and cluster analysis (K-Means). Two heterogeneous geomatrices, a low-grade chalcopyrite ore and basalt, were imaged using a laser-desorption Fourier transform mass spectrometer. Similar mineral distribution patterns in the chalcopyrite ore sample were obtained by the three data analysis methods with most of the differences occurring at the interfaces between mineral phases. PCA missed one minor mineral phase in the chalcopyrite ore sample and did not clearly differentiate among the mineral classes of the basalt. K-Means cluster analysis differentiated among the various mineral phases in both samples, but improperly grouped some spectra in the chalcopyrite sample that only contained unanticipated high mass peaks. Unlike the chemometric methods, FLIE was able to classify spectra as unknowns for those spectra that fell below the confidence level threshold. A nearest neighbor approach, included in FLIE, was used to classify the unknowns to form a visually complete image; however, the unknowns identified by FLIE can be informative because they highlight potential problems or overlooked results. In conclusion, this study validated the fuzzy logic-based approach used in our laboratory and reveald some limitations in the three techniques that were evaluated.

  1. Adaptive fuzzy logic control of a static VAR system

    SciTech Connect

    Dash, P.K.; Routray, A.; Panda, P.C.; Panda, S.K.

    1995-12-31

    A fuzzy gain scheduling scheme for PID controller for transient and dynamic voltage stabilization of power transmission systems has been presented in this paper. Fuzzy rules and reasoning are utilized on-line to determine the controller parameters based on the error signal and its derivative. The static VAR controller is designed with the bus angle deviation and its rate as the input signal to a fuzzy PI or PID control loop. This control is tested for a power transmission system supplying dynamic loads and provides superior performance.

  2. A fuzzy logic enhanced environmental protection education model for policies decision support in green community development.

    PubMed

    Hsueh, Sung-Lin

    2013-01-01

    This study proposes the promotion of environmental protection education among communities as a solution to the serious problems of high energy consumption and carbon emissions around the world. Environmental protection education has direct and lasting influences on everyone in society; therefore, it is helpful in our fight against many serious problems caused by high energy consumption. In this study, the Delphi method and the fuzzy logic theory are used to develop a quantitizing assessment model based on qualitative analysis. This model can be used to assess the results and influences of community residents' participation in environmental protection education on green community development. In addition, it can be used to provide references for governing authorities in their decision making of green community development policies.

  3. A Fuzzy Logic Enhanced Environmental Protection Education Model for Policies Decision Support in Green Community Development

    PubMed Central

    2013-01-01

    This study proposes the promotion of environmental protection education among communities as a solution to the serious problems of high energy consumption and carbon emissions around the world. Environmental protection education has direct and lasting influences on everyone in society; therefore, it is helpful in our fight against many serious problems caused by high energy consumption. In this study, the Delphi method and the fuzzy logic theory are used to develop a quantizing assessment model based on qualitative analysis. This model can be used to assess the results and influences of community residents' participation in environmental protection education on green community development. In addition, it can be used to provide references for governing authorities in their decision making of green community development policies. PMID:24363614

  4. Fuzzy Logic as a Computational Tool for Quantitative Modelling of Biological Systems with Uncertain Kinetic Data.

    PubMed

    Bordon, Jure; Moskon, Miha; Zimic, Nikolaj; Mraz, Miha

    2015-01-01

    Quantitative modelling of biological systems has become an indispensable computational approach in the design of novel and analysis of existing biological systems. However, kinetic data that describe the system's dynamics need to be known in order to obtain relevant results with the conventional modelling techniques. These data are often hard or even impossible to obtain. Here, we present a quantitative fuzzy logic modelling approach that is able to cope with unknown kinetic data and thus produce relevant results even though kinetic data are incomplete or only vaguely defined. Moreover, the approach can be used in the combination with the existing state-of-the-art quantitative modelling techniques only in certain parts of the system, i.e., where kinetic data are missing. The case study of the approach proposed here is performed on the model of three-gene repressilator.

  5. A study of fuzzy logic ensemble system performance on face recognition problem

    NASA Astrophysics Data System (ADS)

    Polyakova, A.; Lipinskiy, L.

    2017-02-01

    Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.

  6. SOM guided fuzzy logic prospectivity model for gold in the Häme Belt, southwestern Finland

    NASA Astrophysics Data System (ADS)

    Leväniemi, Hanna; Hulkki, Helena; Tiainen, Markku

    2017-04-01

    This study investigated gold prospectivity in the Paleoproterozoic Häme Belt, located in southwestern Finland. The Häme Belt comprises calc-alkaline and tholeitic volcanic rocks, migmatites, granitoids, and mafic to ultramafic intrusions. Mineral exploration in the region has resulted in the discovery of several gold occurrences during recent decades; however, no prospectivity modeling for gold has yet been conducted. This study integrated till geochemical and geophysical data to examine and extract data characteristics critical for gold occurrences. Modeling was guided by self-organizing map (SOM) analysis to define essential data associations and to aid in model input data selection and generation. The final fuzzy logic prospectivity model map yielded high predictability values for most known Au or Cu-Au occurrences, but also highlighted new targets for exploration.

  7. Search and selection hotel system in Surabaya based on geographic information system (GIS) with fuzzy logic

    NASA Astrophysics Data System (ADS)

    Purbandini, Taufik

    2016-03-01

    Surabaya is a metropolitan city in Indonesia. When the visitor has an interest in Surabaya for several days, then the visitor was looking for lodging that is closest to the interests of making it more efficient and practical. It was not a waste of time for the businessman because of congestion and so we need full information about the hotel as an inn during a stay in Surabaya began name, address of the hotel, the hotel's website, the distance from the hotel to the destination until the display of the map along the route with the help of Google Maps. This system was designed using fuzzy logic which aims to assist the user in making decisions. Design of hotel search and selection system was done through four stages. The first phase was the collection of data and as the factors that influence the decision-making along with the limit values of these factors. Factors that influence covers a distance of the hotel, the price of hotel rooms, and hotel reviews. The second stage was the processing of data and information by creating membership functions. The third stage was the analysis of systems with fuzzy logic. The steps were performed in systems analysis, namely fuzzification, inference using Mamdani, and defuzzification. The last stage was the design and construction of the system. Designing the system using use case diagrams and activity diagram to describe any process that occurs. Development system includes system implementation and evaluation systems. Implementation of mobile with Android-based system so that these applications were user friendly.

  8. Modeling and simulation of evacuation behavior using fuzzy logic in a goal finding application

    NASA Astrophysics Data System (ADS)

    Sharma, Sharad; Ogunlana, Kola; Sree, Swetha

    2016-05-01

    Modeling and simulation has been widely used as a training and educational tool for depicting different evacuation strategies and damage control decisions during evacuation. However, there are few simulation environments that can include human behavior with low to high levels of fidelity. It is well known that crowd stampede induced by panic leads to fatalities as people are crushed or trampled. Our proposed goal finding application can be used to model situations that are difficult to test in real-life due to safety considerations. It is able to include agent characteristics and behaviors. Findings of this model are very encouraging as agents are able to assume various roles to utilize fuzzy logic on the way to reaching their goals. Fuzzy logic is used to model stress, panic and the uncertainty of emotions. The fuzzy rules link these parts together while feeding into behavioral rules. The contributions of this paper lies in our approach of utilizing fuzzy logic to show learning and adaptive behavior of agents in a goal finding application. The proposed application will aid in running multiple evacuation drills for what-if scenarios by incorporating human behavioral characteristics that can scale from a room to building. Our results show that the inclusion of fuzzy attributes made the evacuation time of the agents closer to the real time drills.

  9. A fuzzy logic approach to assess groundwater pollution levels below agricultural fields.

    PubMed

    Muhammetoglu, Ayse; Yardimci, Ahmet

    2006-07-01

    A fuzzy logic approach has been developed to assess the groundwater pollution levels below agricultural fields. The data collected for Kumluca Plain of Turkey have been utilized to develop the approach. The plain is known with its intensive agricultural activities, which imply excessive application of fertilizers. The characteristics of the soils and underlying groundwater for this plain were monitored during the years 1999 and 2000. Additionally, an extensive field survey related to the types and yields of crops, fertilizer application and irrigation water was carried out. Both the soil and groundwater have exhibited high levels of nitrogen, phosphorus and salinity with considerable spatial and temporal variations. The pollution level of groundwater at several established stations within the plain were assessed using Fuzzy Logic. Water Pollution Index (WPI) values are calculated by Fuzzy Logic utilizing the most significant groundwater pollutants in the area namely nitrite, nitrate and orthophosphate together with the groundwater vulnerability to pollution. The results of the calculated WPI and the monitoring study have yielded good agreement. WPI indicated high to moderate water pollution levels at Kumluca plain depending on factors such as agricultural age, depth to groundwater, soil characteristics and vulnerability of groundwater to pollution. Fuzzy Logic approach has shown to be a practical, simple and useful tool to assess groundwater pollution levels.

  10. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    EPA Science Inventory

    The report gives results of a demonstration of the successful application of fuzzy logic to enhance the performance and control of a variable-speed wind generation system. A squirrel cage induction generator feeds the power to either a double-sided pulse-width modulation converte...

  11. Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model.

    PubMed

    Vesely, Stepan; Klöckner, Christian A; Dohnal, Mirko

    2016-03-01

    In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N=664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N=332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold-out data not included in building the models, N=332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. Copyright © 2015 Elsevier Ltd. All rights reserved.

  12. A Comparison of Neural Networks and Fuzzy Logic Methods for Process Modeling

    NASA Technical Reports Server (NTRS)

    Cios, Krzysztof J.; Sala, Dorel M.; Berke, Laszlo

    1996-01-01

    The goal of this work was to analyze the potential of neural networks and fuzzy logic methods to develop approximate response surfaces as process modeling, that is for mapping of input into output. Structural response was chosen as an example. Each of the many methods surveyed are explained and the results are presented. Future research directions are also discussed.

  13. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    EPA Science Inventory

    The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

  14. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    EPA Science Inventory

    The report gives results of a demonstration of the successful application of fuzzy logic to enhance the performance and control of a variable-speed wind generation system. A squirrel cage induction generator feeds the power to either a double-sided pulse-width modulation converte...

  15. FUZZY-LOGIC-BASED CONTROLLERS FOR EFFICIENCY OPTIMIZATION OF INVERTER-FED INDUCTION MOTOR DRIVES

    EPA Science Inventory

    This paper describes a fuzzy-logic-based energy optimizing controller to improve the efficiency of induction motor/drives operating at various load (torque) and speed conditions. Improvement of induction motor efficiency is important not only from the considerations of energy sav...

  16. The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic

    PubMed Central

    Li, Ning; Martínez, José-Fernán; Díaz, Vicente Hernández

    2015-01-01

    Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters’ dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively. PMID:26266412

  17. The Balanced Cross-Layer Design Routing Algorithm in Wireless Sensor Networks Using Fuzzy Logic.

    PubMed

    Li, Ning; Martínez, José-Fernán; Hernández Díaz, Vicente

    2015-08-10

    Recently, the cross-layer design for the wireless sensor network communication protocol has become more and more important and popular. Considering the disadvantages of the traditional cross-layer routing algorithms, in this paper we propose a new fuzzy logic-based routing algorithm, named the Balanced Cross-layer Fuzzy Logic (BCFL) routing algorithm. In BCFL, we use the cross-layer parameters' dispersion as the fuzzy logic inference system inputs. Moreover, we give each cross-layer parameter a dynamic weight according the value of the dispersion. For getting a balanced solution, the parameter whose dispersion is large will have small weight, and vice versa. In order to compare it with the traditional cross-layer routing algorithms, BCFL is evaluated through extensive simulations. The simulation results show that the new routing algorithm can handle the multiple constraints without increasing the complexity of the algorithm and can achieve the most balanced performance on selecting the next hop relay node. Moreover, the Balanced Cross-layer Fuzzy Logic routing algorithm can adapt to the dynamic changing of the network conditions and topology effectively.

  18. A "fuzzy"-logic language for encoding multiple physical traits in biomolecules.

    PubMed

    Warszawski, Shira; Netzer, Ravit; Tawfik, Dan S; Fleishman, Sarel J

    2014-12-12

    To carry out their activities, biological macromolecules balance different physical traits, such as stability, interaction affinity, and selectivity. How such often opposing traits are encoded in a macromolecular system is critical to our understanding of evolutionary processes and ability to design new molecules with desired functions. We present a framework for constraining design simulations to balance different physical characteristics. Each trait is represented by the equilibrium fractional occupancy of the desired state relative to its alternatives, ranging from none to full occupancy, and the different traits are combined using Boolean operators to effect a "fuzzy"-logic language for encoding any combination of traits. In another paper, we presented a new combinatorial backbone design algorithm AbDesign where the fuzzy-logic framework was used to optimize protein backbones and sequences for both stability and binding affinity in antibody-design simulation. We now extend this framework and find that fuzzy-logic design simulations reproduce sequence and structure design principles seen in nature to underlie exquisite specificity on the one hand and multispecificity on the other hand. The fuzzy-logic language is broadly applicable and could help define the space of tolerated and beneficial mutations in natural biomolecular systems and design artificial molecules that encode complex characteristics. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  19. Application of Two-Valued and Fuzzy Logics Teaching in Understanding the Precise and Approximate Concepts

    ERIC Educational Resources Information Center

    Bayekolaei, Mehraneh Delaviz; Nor, Norjoharuddeen Bin Mohd; Sohaei, Reza; Berneti, Abdul Karim Maleki; Zerafat, Romina; Saravi, Hanieh Rasouli

    2015-01-01

    This research aimed to examine the application of two-valued and fuzzy logics teaching in better understanding the precise approximate concepts of chapter 4 of Sixth grade mathematics. Participants of this study were 30 Sixth grade mathematics students from an elementary school in Sari (a city in the north of Iran) in the academic year of…

  20. FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM

    EPA Science Inventory

    The paper describes a variable-speed wind generation system where fuzzy logic principles are used to optimize efficiency and enhance performance control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which either pump...

  1. Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor

    NASA Astrophysics Data System (ADS)

    Afiqah Zainal, Nurul; Sooi Tat, Chan; Ajisman

    2016-02-01

    Renewable energy produced by solar module gives advantages for generated three- phase induction motor in remote area. But, solar module's ou tput is uncertain and complex. Fuzzy logic controller is one of controllers that can handle non-linear system and maximum power of solar module. Fuzzy logic controller used for Maximum Power Point Tracking (MPPT) technique to control Pulse-Width Modulation (PWM) for switching power electronics circuit. DC-DC boost converter used to boost up photovoltaic voltage to desired output and supply voltage source inverter which controlled by three-phase PWM generated by microcontroller. IGBT switched Voltage source inverter (VSI) produced alternating current (AC) voltage from direct current (DC) source to control speed of three-phase induction motor from boost converter output. Results showed that, the output power of solar module is optimized and controlled by using fuzzy logic controller. Besides that, the three-phase induction motor can be drive and control using VSI switching by the PWM signal generated by the fuzzy logic controller. This concluded that the non-linear system can be controlled and used in driving three-phase induction motor.

  2. FUZZY-LOGIC-BASED CONTROLLERS FOR EFFICIENCY OPTIMIZATION OF INVERTER-FED INDUCTION MOTOR DRIVES

    EPA Science Inventory

    This paper describes a fuzzy-logic-based energy optimizing controller to improve the efficiency of induction motor/drives operating at various load (torque) and speed conditions. Improvement of induction motor efficiency is important not only from the considerations of energy sav...

  3. Fuzzy logic techniques for rendezvous and docking of two geostationary satellites

    NASA Technical Reports Server (NTRS)

    Ortega, Guillermo

    1995-01-01

    Large assemblings in space require the ability to manage rendezvous and docking operations. In future these techniques will be required for the gradual build up of big telecommunication platforms in the geostationary orbit. The paper discusses the use of fuzzy logic to model and implement a control system for the docking/berthing of two satellites in geostationary orbit. The system mounted in a chaser vehicle determines the actual state of both satellites and generates torques to execute maneuvers to establish the structural latching. The paper describes the proximity operations to collocate the two satellites in the same orbital window, the fuzzy guidance and navigation of the chaser approaching the target and the final Fuzzy berthing. The fuzzy logic system represents a knowledge based controller that realizes the close loop operations autonomously replacing the conventional control algorithms. The goal is to produce smooth control actions in the proximity of the target and during the docking to avoid disturbance torques in the final assembly orbit. The knowledge of the fuzzy controller consists of a data base of rules and the definitions of the fuzzy sets. The knowledge of an experienced spacecraft controller is captured into a set of rules forming the Rules Data Base.

  4. Fuzzy logic selection as a new reliable tool to identify molecular grade signatures in breast cancer--the INNODIAG study.

    PubMed

    Kempowsky-Hamon, Tatiana; Valle, Carine; Lacroix-Triki, Magali; Hedjazi, Lyamine; Trouilh, Lidwine; Lamarre, Sophie; Labourdette, Delphine; Roger, Laurence; Mhamdi, Loubna; Dalenc, Florence; Filleron, Thomas; Favre, Gilles; François, Jean-Marie; Le Lann, Marie-Véronique; Anton-Leberre, Véronique

    2015-02-07

    Personalized medicine has become a priority in breast cancer patient management. In addition to the routinely used clinicopathological characteristics, clinicians will have to face an increasing amount of data derived from tumor molecular profiling. The aims of this study were to develop a new gene selection method based on a fuzzy logic selection and classification algorithm, and to validate the gene signatures obtained on breast cancer patient cohorts. We analyzed data from four published gene expression datasets for breast carcinomas. We identified the best discriminating genes by comparing molecular expression profiles between histologic grade 1 and 3 tumors for each of the training datasets. The most pertinent probes were selected and used to define fuzzy molecular grade 1-like (good prognosis) and fuzzy molecular grade 3-like (poor prognosis) profiles. To evaluate the prognostic performance of the fuzzy grade signatures in breast cancer tumors, a Kaplan-Meier analysis was conducted to compare the relapse-free survival deduced from histologic grade and fuzzy molecular grade classification. We applied the fuzzy logic selection on breast cancer databases and obtained four new gene signatures. Analysis in the training public sets showed good performance of these gene signatures for grade (sensitivity from 90% to 95%, specificity 67% to 93%). To validate these gene signatures, we designed probes on custom microarrays and tested them on 150 invasive breast carcinomas. Good performance was obtained with an error rate of less than 10%. For one gene signature, among 74 histologic grade 3 and 18 grade 1 tumors, 88 cases (96%) were correctly assigned. Interestingly histologic grade 2 tumors (n = 58) were split in these two molecular grade categories. We confirmed the use of fuzzy logic selection as a new tool to identify gene signatures with good reliability and increased classification power. This method based on artificial intelligence algorithms was successfully

  5. A fuzzy-based histogram analysis technique for skin lesion discrimination in dermatology clinical images

    PubMed Central

    Stanley, R. Joe; Moss, Randy Hays; Van Stoecker, William; Aggarwal, Chetna

    2011-01-01

    A fuzzy logic-based color histogram analysis technique is presented for discriminating benign skin lesions from malignant melanomas in dermatology clinical images. The approach utilizes a fuzzy set for benign skin lesion color, and alpha-cut and support set cardinality for quantifying a fuzzy ratio skin lesion color feature. Skin lesion discrimination results are reported for the fuzzy ratio and fusion with a previously determined percent melanoma color feature over a data set of 258 clinical images. For the fusion technique, alpha-cuts for the fuzzy ratio can be chosen to recognize over 93.30% of melanomas with approximately 15.67% false positive lesions. PMID:12821032

  6. A fuzzy-logic antiswing controller for three-dimensional overhead cranes.

    PubMed

    Cho, Sung-Kun; Lee, Ho-Hoon

    2002-04-01

    In this paper, a new fuzzy antiswing control scheme is proposed for a three-dimensional overhead crane. The proposed control consists of a position servo control and a fuzzy-logic control. The position servo control is used to control crane position and rope length, and the fuzzy-logic control is used to suppress load swing. The proposed control guarantees not only prompt suppression of load swing but also accurate control of crane position and rope length for simultaneous travel, traverse, and hoisting motions of the crane. Furthermore, the proposed control provides practical gain tuning criteria for easy application. The effectiveness of the proposed control is shown by experiments with a three-dimensional prototype overhead crane.

  7. VANET Broadcast Protocol Based on Fuzzy Logic and Lightweight Retransmission Mechanism

    NASA Astrophysics Data System (ADS)

    Wu, Celimuge; Ohzahata, Satoshi; Kato, Toshihiko

    Vehicular ad hoc networks have been attracting the interest of both academic and industrial communities on account of their potential role in Intelligent Transportation Systems (ITS). However, due to vehicle movement and fading in wireless communications, providing a reliable and efficient multi-hop broadcast service in vehicular ad hoc networks is still an open research topic. In this paper, we propose FUZZBR (FUZZy BRoadcast), a fuzzy logic based multi-hop broadcast protocol for information dissemination in vehicular ad hoc networks. FUZZBR has low message overhead since it uses only a subset of neighbor nodes to relay data messages. In the relay node selection, FUZZBR jointly considers multiple metrics of inter-vehicle distance, node mobility and signal strength by employing the fuzzy logic. FUZZBR also uses a lightweight retransmission mechanism to retransmit a packet when a relay fails. We use computer simulations to evaluate the performance of FUZZBR.

  8. Fuzzy Logic Path Planning System for Collision Avoidance by an Autonomous Rover Vehicle

    NASA Technical Reports Server (NTRS)

    Murphy, Michael G.

    1991-01-01

    Systems already developed at JSC have shown the benefits of applying fuzzy logic control theory to space related operations. Four major issues are addressed that are associated with developing an autonomous collision avoidance subsystem within a path planning system designed for application in a remote, hostile environment that does not lend itself well to remote manipulation of the vehicle involved through Earth-based telecommunication. A good focus for this is unmanned exploration of the surface of Mars. The uncertainties involved indicate that robust approaches such as fuzzy logic control are particularly appropriate. The four major issues addressed are: (1) avoidance of a single fuzzy moving obstacle; (2) back off from a dead end in a static obstacle environment; (3) fusion of sensor data to detect obstacles; and (4) options for adaptive learning in a path planning system.

  9. Fuzzy Logic-Supported Detection of Complex Geospatial Features in a Web Service Environment

    NASA Astrophysics Data System (ADS)

    He, L. L.; Di, L. P.; Yue, P.; Zhang, M. D.

    2013-10-01

    Spatial relations among simple features can be used to characterize complex geospatial features. These spatial relations are often represented using linguistic terms such as near, which have inherent vagueness and imprecision. Fuzzy logic can be used to modeling fuzziness of the terms. Once simple features are extracted from remote sensing imagery, degree of satisfaction of spatial relations among these simple features can be derived to detect complex features. The derivation process can be performed in a distributed service environment, which benefits Earth science society in the last decade. Workflow-based service can provide ondemand uncertainty-aware discovery of complex features in a distributed environment. A use case on the complex facility detection illustrates the applicability of the fuzzy logic-supported service-oriented approach.

  10. The Work Ratio--modeling the likelihood of return to work for workers with musculoskeletal disorders: A fuzzy logic approach.

    PubMed

    Apalit, Nathan

    2010-01-01

    The world of musculoskeletal disorders (MSDs) is complicated and fuzzy. Fuzzy logic provides a precise framework for complex problems characterized by uncertainty, vagueness and imprecision. Although fuzzy logic would appear to be an ideal modeling language to help address the complexity of MSDs, little research has been done in this regard. The Work Ratio is a novel mathematical model that uses fuzzy logic to provide a numerical and linguistic valuation of the likelihood of return to work and remaining at work. It can be used for a worker with any MSD at any point in time. Basic mathematical concepts from set theory and fuzzy logic are reviewed. A case study is then used to illustrate the use of the Work Ratio. Its potential strengths and limitations are discussed. Further research of its use with a variety of MSDs, settings and multidisciplinary teams is needed to confirm its universal value.

  11. Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI).

    PubMed

    Javid, Allahbakhsh; Hamedian, Amir Abbas; Gharibi, Hamed; Sowlat, Mohammad Hossein

    2016-02-01

    In the past few decades, Indoor Air Pollution (IAP) has become a primary concern to the point. It is increasingly believed to be of equal or greater importance to human health compared to ambient air. However, due to the lack of comprehensive indices for the integrated assessment of indoor air quality (IAQ), we aimed to develop a novel, Fuzzy-Based Indoor Air Quality Index (FIAQI) to bridge the existing gap in this area. We based our index on fuzzy logic, which enables us to overcome the limitations of traditional methods applied to develop environmental quality indices. Fifteen parameters, including the criteria air pollutants, volatile organic compounds, and bioaerosols were included in the FIAQI due mainly to their significant health effects. Weighting factors were assigned to the parameters based on the medical evidence available in the literature on their health effects. The final FIAQI consisted of 108 rules. In order to demonstrate the performance of the index, data were intentionally generated to cover a variety of quality levels. In addition, a sensitivity analysis was conducted to assess the validity of the index. The FIAQI tends to be a comprehensive tool to classify IAQ and produce accurate results. It seems useful and reliable to be considered by authorities to assess IAQ environments.

  12. Towards the Application of Fuzzy Logic for Developing a Novel Indoor Air Quality Index (FIAQI)

    PubMed Central

    JAVID, Allahbakhsh; HAMEDIAN, Amir Abbas; GHARIBI, Hamed; SOWLAT, Mohammad Hossein

    2016-01-01

    Background: In the past few decades, Indoor Air Pollution (IAP) has become a primary concern to the point. It is increasingly believed to be of equal or greater importance to human health compared to ambient air. However, due to the lack of comprehensive indices for the integrated assessment of indoor air quality (IAQ), we aimed to develop a novel, Fuzzy-Based Indoor Air Quality Index (FIAQI) to bridge the existing gap in this area. Methods: We based our index on fuzzy logic, which enables us to overcome the limitations of traditional methods applied to develop environmental quality indices. Fifteen parameters, including the criteria air pollutants, volatile organic compounds, and bioaerosols were included in the FIAQI due mainly to their significant health effects. Weighting factors were assigned to the parameters based on the medical evidence available in the literature on their health effects. The final FIAQI consisted of 108 rules. In order to demonstrate the performance of the index, data were intentionally generated to cover a variety of quality levels. In addition, a sensitivity analysis was conducted to assess the validity of the index. Results: The FIAQI tends to be a comprehensive tool to classify IAQ and produce accurate results. Conclusion: It seems useful and reliable to be considered by authorities to assess IAQ environments. PMID:27114985

  13. Combined fuzzy logic and random walker algorithm for PET image tumor delineation.

    PubMed

    Soufi, Motahare; Kamali-Asl, Alireza; Geramifar, Parham; Abdoli, Mehrsima; Rahmim, Arman

    2016-02-01

    The random walk (RW) technique serves as a powerful tool for PET tumor delineation, which typically involves significant noise and/or blurring. One challenging step is hard decision-making in pixel labeling. Fuzzy logic techniques have achieved increasing application in edge detection. We aimed to combine the advantages of fuzzy edge detection with the RW technique to improve PET tumor delineation. A fuzzy inference system was designed for tumor edge detection from RW probabilities. Three clinical PET/computed tomography datasets containing 12 liver, 13 lung, and 18 abdomen tumors were analyzed, with manual expert tumor contouring as ground truth. The standard RW and proposed combined method were compared quantitatively using the dice similarity coefficient, the Hausdorff distance, and the mean standard uptake value. The dice similarity coefficient of the proposed method versus standard RW showed significant mean improvements of 21.0±7.2, 12.3±5.8, and 18.4%±6.1% for liver, lung, and abdominal tumors, respectively, whereas the mean improvements in the Hausdorff distance were 3.6±1.4, 1.3±0.4, 1.8±0.8 mm, and the mean improvements in SUVmean error were 15.5±6.3, 11.7±8.6, and 14.1±6.8% (all P's<0.001). For all tumor sizes, the proposed method outperformed the RW algorithm. Furthermore, tumor edge analysis demonstrated further enhancement of the performance of the algorithm, relative to the RW method, with decreasing edge gradients. The proposed technique improves PET lesion delineation at different tumor sites. It depicts greater effectiveness in tumors with smaller size and/or low edge gradients, wherein most PET segmentation algorithms encounter serious challenges. Favorable execution time and accurate performance of the algorithm make it a great tool for clinical applications.

  14. The Fuzzy Logic of MicroRNA Regulation: A Key to Control Cell Complexity

    PubMed Central

    Ripoli, Andrea; Rainaldi, Giuseppe; Rizzo, Milena; Mercatanti, Alberto; Pitto, Letizia

    2010-01-01

    Genomic and clinical evidence suggest a major role of microRNAs (miRNAs) in the regulatory mechanisms of gene expression, with a clear impact on development and physiology; miRNAs are a class of endogenous 22-25 nt single-stranded RNA molecules, that negatively regulate gene expression post-transcriptionally, by imperfect base pairing with the 3’ UTR of the corresponding mRNA target. Because of this imperfection, each miRNA can bind multiple targets, and multiple miRNAs can bind the same mRNA target; although digital, the miRNAs control mechanism is characterized by an imprecise action, naturally understandable in the theoretical framework of fuzzy logic. A major practical application of fuzzy logic is represented by the design and the realization of efficient and robust control systems, even when the processes to be controlled show chaotic, deterministic as well unpredictable, behaviours. The vagueness of miRNA action, when considered together with the controlled and chaotic gene expression, is a hint of a cellular fuzzy control system. As a demonstration of the possibility and the effectiveness of miRNA based fuzzy mechanism, a fuzzy cognitive map -a mathematical formalism combining neural network and fuzzy logic- has been developed to study the apoptosis/proliferation control performed by the miRNA-17-92 cluster/E2F1/cMYC circuitry. When experimentally demonstrated, the concept of fuzzy control could modify the way we analyse and model gene expression, with a possible impact on the way we imagine and design therapeutic intervention based on miRNA silencing. PMID:21286312

  15. Hybrid neural network and fuzzy logic approaches for rendezvous and capture in space

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Castellano, Timothy

    1991-01-01

    The nonlinear behavior of many practical systems and unavailability of quantitative data regarding the input-output relations makes the analytical modeling of these systems very difficult. On the other hand, approximate reasoning-based controllers which do not require analytical models have demonstrated a number of successful applications such as the subway system in the city of Sendai. These applications have mainly concentrated on emulating the performance of a skilled human operator in the form of linguistic rules. However, the process of learning and tuning the control rules to achieve the desired performance remains a difficult task. Fuzzy Logic Control is based on fuzzy set theory. A fuzzy set is an extension of a crisp set. Crisp sets only allow full membership or no membership at all, whereas fuzzy sets allow partial membership. In other words, an element may partially belong to a set.

  16. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic.

    PubMed

    Hou, Shi-Wang; Feng, Shunxiao; Wang, Hui

    2016-01-01

    Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

  17. Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

    PubMed Central

    Feng, Shunxiao; Wang, Hui

    2016-01-01

    Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating. PMID:28058046

  18. Construction of a fuzzy and Boolean logic gates based on DNA.

    PubMed

    Zadegan, Reza M; Jepsen, Mette D E; Hildebrandt, Lasse L; Birkedal, Victoria; Kjems, Jørgen

    2015-04-17

    Logic gates are devices that can perform logical operations by transforming a set of inputs into a predictable single detectable output. The hybridization properties, structure, and function of nucleic acids can be used to make DNA-based logic gates. These devices are important modules in molecular computing and biosensing. The ideal logic gate system should provide a wide selection of logical operations, and be integrable in multiple copies into more complex structures. Here we show the successful construction of a small DNA-based logic gate complex that produces fluorescent outputs corresponding to the operation of the six Boolean logic gates AND, NAND, OR, NOR, XOR, and XNOR. The logic gate complex is shown to work also when implemented in a three-dimensional DNA origami box structure, where it controlled the position of the lid in a closed or open position. Implementation of multiple microRNA sensitive DNA locks on one DNA origami box structure enabled fuzzy logical operation that allows biosensing of complex molecular signals. Integrating logic gates with DNA origami systems opens a vast avenue to applications in the fields of nanomedicine for diagnostics and therapeutics. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  19. Summary report: A preliminary investigation into the use of fuzzy logic for the control of redundant manipulators

    NASA Technical Reports Server (NTRS)

    Cheatham, John B., Jr.; Magee, Kevin N.

    1991-01-01

    The Rice University Department of Mechanical Engineering and Materials Sciences' Robotics Group designed and built an eight degree of freedom redundant manipulator. Fuzzy logic was proposed as a control scheme for tasks not directly controlled by a human operator. In preliminary work, fuzzy logic control was implemented for a camera tracking system and a six degree of freedom manipulator. Both preliminary systems use real time vision data as input to fuzzy controllers. Related projects include integration of tactile sensing and fuzzy control of a redundant snake-like arm that is under construction.

  20. Building Better Discipline Strategies for Schools by Fuzzy Logics

    ERIC Educational Resources Information Center

    Chang, Dian-Fu; Juan, Ya-Yun; Chou, Wen-Ching

    2014-01-01

    This study aims to realize better discipline strategies for applying in high schools. We invited 400 teachers to participate the survey and collected their perceptions on the discipline strategies in terms of the acceptance of strategies and their effectiveness in schools. Based on the idea of fuzzy statistics, this study transformed the fuzzy…

  1. Quantified moving average strategy of crude oil futures market based on fuzzy logic rules and genetic algorithms

    NASA Astrophysics Data System (ADS)

    Liu, Xiaojia; An, Haizhong; Wang, Lijun; Guan, Qing

    2017-09-01

    The moving average strategy is a technical indicator that can generate trading signals to assist investment. While the trading signals tell the traders timing to buy or sell, the moving average cannot tell the trading volume, which is a crucial factor for investment. This paper proposes a fuzzy moving average strategy, in which the fuzzy logic rule is used to determine the strength of trading signals, i.e., the trading volume. To compose one fuzzy logic rule, we use four types of moving averages, the length of the moving average period, the fuzzy extent, and the recommend value. Ten fuzzy logic rules form a fuzzy set, which generates a rating level that decides the trading volume. In this process, we apply genetic algorithms to identify an optimal fuzzy logic rule set and utilize crude oil futures prices from the New York Mercantile Exchange (NYMEX) as the experiment data. Each experiment is repeated for 20 times. The results show that firstly the fuzzy moving average strategy can obtain a more stable rate of return than the moving average strategies. Secondly, holding amounts series is highly sensitive to price series. Thirdly, simple moving average methods are more efficient. Lastly, the fuzzy extents of extremely low, high, and very high are more popular. These results are helpful in investment decisions.

  2. Coastal vulnerability assessment using Fuzzy Logic and Bayesian Belief Network approaches

    NASA Astrophysics Data System (ADS)

    Valentini, Emiliana; Nguyen Xuan, Alessandra; Filipponi, Federico; Taramelli, Andrea

    2017-04-01

    dimension. According to the results using fuzzy operators, the analysis greatest weakness is the limited capacity to represent the relation among the different considered variables. The BBN approach, based on the definition of conditional probabilities, has allowed determining the trend of distributions of vulnerability along-shore, highlighting which parts of the coast are most likely to have higher or lower vulnerability than others. In BBN analysis, the greatest weakness emerge in the case of arbitrary definition of conditional probabilities (i.e. when there is a lack of information on the past hazardous events) because it is not possible to derive the individual contribution of each variable. As conclusion, the two approaches could be used together in the perspective of enhancing the multiple components in vulnerability assessment: the BBN as a preliminary assessment to provide a coarse description of the vulnerability distribution, and the Fuzzy Logic as an extended assessment to provide more space based information.

  3. Fluorescence intensity positivity classification of Hep-2 cells images using fuzzy logic

    NASA Astrophysics Data System (ADS)

    Sazali, Dayang Farzana Abang; Janier, Josefina Barnachea; May, Zazilah Bt.

    2014-10-01

    Indirect Immunofluorescence (IIF) is a good standard used for antinuclear autoantibody (ANA) test using Hep-2 cells to determine specific diseases. Different classifier algorithm methods have been proposed in previous works however, there still no valid set as a standard to classify the fluorescence intensity. This paper presents the use of fuzzy logic to classify the fluorescence intensity and to determine the positivity of the Hep-2 cell serum samples. The fuzzy algorithm involves the image pre-processing by filtering the noises and smoothen the image, converting the red, green and blue (RGB) color space of images to luminosity layer, chromaticity layer "a" and "b" (LAB) color space where the mean value of the lightness and chromaticity layer "a" was extracted and classified by using fuzzy logic algorithm based on the standard score ranges of antinuclear autoantibody (ANA) fluorescence intensity. Using 100 data sets of positive and intermediate fluorescence intensity for testing the performance measurements, the fuzzy logic obtained an accuracy of intermediate and positive class as 85% and 87% respectively.

  4. Fuzzy Logic Controller for Hemodialysis Machine Based on Human Body Model

    PubMed Central

    Nafisi, Vahid Reza; Eghbal, Manouchehr; Motlagh, Mohammad Reza Jahed; Yavari, Fatemeh

    2011-01-01

    Fuzzy controllers are being used in various control schemes. The aim of this study is to adjust the hemodialysis machine parameters by utilizing a fuzzy logic controller (FLC) so that patient's hemodynamic condition remains stable during hemodialysis treatment. For this purpose, a comprehensive mathematical model of the arterial pressure response during hemodialysis, including hemodynamic, osmotic, and regulatory phenomena has been used. The multi-input multi-output (MIMO) fuzzy logic controller receives three parameters from the model (heart rate, arterial blood pressure, and relative blood volume) as input. According to the changes in the controller input values and its rule base, the outputs change so that the patient's hemodynamic condition remains stable. The results of the simulations illustrate that applying the controller can improve the stability of a patient's hemodynamic condition during hemodialysis treatment and it also decreases the treatment time. Furthermore, by using fuzzy logic, there is no need to have prior knowledge about the system under control and the FLC is compatible with different patients. PMID:22606657

  5. Fuzzy logic control system to provide autonomous collision avoidance for Mars rover vehicle

    NASA Technical Reports Server (NTRS)

    Murphy, Michael G.

    1990-01-01

    NASA is currently involved with planning unmanned missions to Mars to investigate the terrain and process soil samples in advance of a manned mission. A key issue involved in unmanned surface exploration on Mars is that of supporting autonomous maneuvering since radio communication involves lengthy delays. It is anticipated that specific target locations will be designated for sample gathering. In maneuvering autonomously from a starting position to a target position, the rover will need to avoid a variety of obstacles such as boulders or troughs that may block the shortest path to the target. The physical integrity of the rover needs to be maintained while minimizing the time and distance required to attain the target position. Fuzzy logic lends itself well to building reliable control systems that function in the presence of uncertainty or ambiguity. The following major issues are discussed: (1) the nature of fuzzy logic control systems and software tools to implement them; (2) collision avoidance in the presence of fuzzy parameters; and (3) techniques for adaptation in fuzzy logic control systems.

  6. Application of fuzzy logic in intelligent software agents for IP selection

    NASA Astrophysics Data System (ADS)

    Liu, Jian; Shragowitz, Eugene B.

    1999-11-01

    IPs (Intellectual Properties) are becoming increasingly essential in today's electronic system design. One of important issues in design reuse is the IP selection, i.e. finding an existing solution that matches the user's expectations best. This paper describes the Internet-based intelligent software system (Software Agent) that helps the user to pick out the optimal designs among those marketed by the IP vendors. The Software Agent for IP Selection (SAFIPS) conducts dialogues with both the IP users and IP vendors, narrowing the choices after evaluating general characteristics first, followed by matching behavioral, RTL, logic, and physical levels. The SAFIPS system conducts reasoning based on fuzzy logic rules derived in the process of dialogues of the software agent with the IP users and vendors. In addition to the dialogue system and fuzzy logic inference system, the SAFIPS includes a HDL simulator and fuzzy logic evaluator that are used to measure the level of matching of the user's behavioral model with the IP vendor's model.

  7. Using Fuzzy Logic for Performance Evaluation in Reinforcement Learning

    NASA Technical Reports Server (NTRS)

    Berenji, Hamid R.; Khedkar, Pratap S.

    1992-01-01

    Current reinforcement learning algorithms require long training periods which generally limit their applicability to small size problems. A new architecture is described which uses fuzzy rules to initialize its two neural networks: a neural network for performance evaluation and another for action selection. This architecture is applied to control of dynamic systems and it is demonstrated that it is possible to start with an approximate prior knowledge and learn to refine it through experiments using reinforcement learning.

  8. Fuzzy propositions weighted by veracities or how to relate fuzzy logic and probability theory for segmentation of ultrasound images

    NASA Astrophysics Data System (ADS)

    Debon, Renaud; Solaiman, Basel; Cauvin, Jean-Michel; Robaszkiewicz, Michel; Roux, Christian

    2002-03-01

    In medical imaging, and more generally in medical information, researches go towards fusion systems. Nowadays, the steps of information source definition, the pertinent data extraction and the fusion need to be conducted as a whole. In this work, our interest is related to the esophagus wall segmentation from ultrasound images sequences. We aim to elaborate a general methodology of data mining that coherently links works on data selection and fusion architectures, in order to extract useful information from raw data and to integrate efficiently the physician a prior. In the presented method, based on fuzzy logic, some fuzzy propositions are defined using physicians a prior knowledge. The use of probabilistic distributions, estimated thanks to a learning base of pathologic and non-pathologic cases, enables the veracity of these propositions to be qualified. This promising idea enables information to be managed through the consideration of both information imprecision and uncertainty. In the same time, the obtained benefit, when a prior knowledge source is injected in a fusion based decision system, can be quantified. By considering that, the fuzzyfication stage is optimized relatively to a given criteria using a genetic algorithm. By this manner, fuzzy sets corresponding to the physicians ambiguous a prior are defined objectively. At this level, we successively compare performances obtained when fuzzy functions are defined empirically and when they are optimized. We conclude this paper with the first results on esophagus wall segmentation and outline some further works.

  9. A fuzzy logic-based tool to assess beef cattle ranching sustainability in complex environmental systems.

    PubMed

    Santos, Sandra A; de Lima, Helano Póvoas; Massruhá, Silvia M F S; de Abreu, Urbano G P; Tomás, Walfrido M; Salis, Suzana M; Cardoso, Evaldo L; de Oliveira, Márcia Divina; Soares, Márcia Toffani S; Dos Santos, Antônio; de Oliveira, Luiz Orcírio F; Calheiros, Débora F; Crispim, Sandra M A; Soriano, Balbina M A; Amâncio, Christiane O G; Nunes, Alessandro Pacheco; Pellegrin, Luiz Alberto

    2017-08-01

    One of the most relevant issues in discussion worldwide nowadays is the concept of sustainability. However, sustainability assessment is a difficult task due to the complexity of factors involved in the natural world added to the human interference. In order to assess the sustainability of beef ranching in complex and uncertain tropical environment systems this paper describes a decision support system based on fuzzy rule-approach, the Sustainable Pantanal Ranch (SPR). This tool was built by a set of measurements and indicators integrated by fuzzy logic to evaluate the attributes of the three dimensions of sustainability. Indicators and decision rules, as well as scenario evaluations, were obtained from workshops involving multi-disciplinary team of experts. A Fuzzy Rule-Based System (FRBS) was developed to each attribute, dimension and general index. The essential parts of the FRBS are the knowledge database, rules and the inference engine. The FuzzyGen and WebFuzzy tools were developed to support the FRBS and both showed efficiency and low cost for digital applications. The results of each attribute, dimension and index were presented as radar graphs, showing the individual value (0-10) of each indicator. In the validation process using the WebFuzzy, different combinations of indicators were made for each attribute index to show the corresponding output, and which confirm the feasibility and usability of the tool. Copyright © 2017 Elsevier Ltd. All rights reserved.

  10. Integrating fuzzy logic and statistics to improve the reliable delimitation of biogeographic regions and transition zones.

    PubMed

    Olivero, Jesús; Márquez, Ana L; Real, Raimundo

    2013-01-01

    This study uses the amphibian species of the Mediterranean basin to develop a consistent procedure based on fuzzy sets with which biogeographic regions and biotic transition zones can be objectively detected and reliably mapped. Biogeographical regionalizations are abstractions of the geographical organization of life on Earth that provide frameworks for cataloguing species and ecosystems, for answering basic questions in biogeography, evolutionary biology, and systematics, and for assessing priorities for conservation. On the other hand, limits between regions may form sharply defined boundaries along some parts of their borders, whereas elsewhere they may consist of broad transition zones. The fuzzy set approach provides a heuristic way to analyse the complexity of the biota within an area; significantly different regions are detected whose mutual limits are sometimes fuzzy, sometimes clearly crisp. Most of the regionalizations described in the literature for the Mediterranean biogeographical area present a certain degree of convergence when they are compared within the context of fuzzy interpretation, as many of the differences found between regionalizations are located in transition zones, according to our case study. Compared with other classification procedures based on fuzzy sets, the novelty of our method is that both fuzzy logic and statistics are used together in a synergy in order to avoid arbitrary decisions in the definition of biogeographic regions and transition zones.

  11. Program Theory Evaluation: Logic Analysis

    ERIC Educational Resources Information Center

    Brousselle, Astrid; Champagne, Francois

    2011-01-01

    Program theory evaluation, which has grown in use over the past 10 years, assesses whether a program is designed in such a way that it can achieve its intended outcomes. This article describes a particular type of program theory evaluation--logic analysis--that allows us to test the plausibility of a program's theory using scientific knowledge.…

  12. Program Theory Evaluation: Logic Analysis

    ERIC Educational Resources Information Center

    Brousselle, Astrid; Champagne, Francois

    2011-01-01

    Program theory evaluation, which has grown in use over the past 10 years, assesses whether a program is designed in such a way that it can achieve its intended outcomes. This article describes a particular type of program theory evaluation--logic analysis--that allows us to test the plausibility of a program's theory using scientific knowledge.…

  13. A Fuzzy Logic Based Controller for the Automated Alignment of a Laser-beam-smoothing Spatial Filter

    NASA Technical Reports Server (NTRS)

    Krasowski, M. J.; Dickens, D. E.

    1992-01-01

    A fuzzy logic based controller for a laser-beam-smoothing spatial filter is described. It is demonstrated that a human operator's alignment actions can easily be described by a system of fuzzy rules of inference. The final configuration uses inexpensive, off-the-shelf hardware and allows for a compact, readily implemented embedded control system.

  14. A novel approach in water quality assessment based on fuzzy logic.

    PubMed

    Gharibi, Hamed; Mahvi, Amir Hossein; Nabizadeh, Ramin; Arabalibeik, Hossein; Yunesian, Masud; Sowlat, Mohammad Hossein

    2012-12-15

    The present work aimed at developing a novel water quality index based on fuzzy logic, that is, a comprehensive artificial intelligence (AI) approach to the development of environmental indices for routine assessment of surface water quality, particularly for human drinking purposes. Twenty parameters were included based on their critical importance for the overall water quality and their potential impact on human health. To assess the performance of the proposed index under actual conditions, a case study was conducted at Mamloo dam, Iran, employing water quality data of four sampling stations in the water basin of the dam from 2006 to 2009. Results of this study indicated that the general quality of water in all the sampling stations over all the years of the study period is fairly low (yearly averages are usually in the range of 45-55). According to the results of ANOVA test, water quality did not significantly change over time in any of the sampling stations (P > 0.05). In addition, comparison of the outputs of the fuzzy-based proposed index proposed with those of the NSF water quality index (the WQI) and Canadian Water Quality Index (CWQI) showed similar results and were sensitive to changes in the level of water quality parameters. However, the index proposed by the present study produced a more stringent outputs compared to the WQI and CWQI. Results of the sensitivity analysis suggested that the index is robust against the changes in the rules. In conclusion, the proposed index seems to produce accurate and reliable results and can therefore be used as a comprehensive tool for water quality assessment, especially for the analysis of human drinking water.

  15. Development of a Fuzzy Logic Expert System for Pile Selection

    DTIC Science & Technology

    1989-01-01

    Bjerrum (7) compared bitumen to an electro- osmosis process for reducing down-drag and reported the latter to be the more efficient. Studies on bitumen, of...any stage of rule firing, all fireable rules are fired at the same time. The problem of memory conflicting was resolved using weakly mono- tonic logic

  16. Fuzzy logic, PSO based fuzzy logic algorithm and current controls comparative for grid-connected hybrid system

    NASA Astrophysics Data System (ADS)

    Borni, A.; Abdelkrim, T.; Zaghba, L.; Bouchakour, A.; Lakhdari, A.; Zarour, L.

    2017-02-01

    In this paper the model of a grid connected hybrid system is presented. The hybrid system includes a variable speed wind turbine controlled by aFuzzy MPPT control, and a photovoltaic generator controlled with PSO Fuzzy MPPT control to compensate the power fluctuations caused by the wind in a short and long term, the inverter currents injected to the grid is controlled by a decoupled PI current control. In the first phase, we start by modeling of the conversion system components; the wind system is consisted of a turbine coupled to a gearless permanent magnet generator (PMG), the AC/DC and DC-DC (Boost) converter are responsible to feed the electric energy produced by the PMG to the DC-link. The solar system consists of a photovoltaic generator (GPV) connected to a DC/DC boost converter controlled by a PSO fuzzy MPPT control to extract at any moment the maximum available power at the GPV terminals, the system is based on maximum utilization of both of sources because of their complementary. At the end. The active power reached to the DC-link is injected to the grid through a DC/AC inverter, this function is achieved by controlling the DC bus voltage to keep it constant and close to its reference value, The simulation studies have been performed using Matlab/Simulink. It can be concluded that a good control system performance can be achieved.

  17. Fuzzy logic electric vehicle regenerative antiskid braking and traction control system

    DOEpatents

    Cikanek, S.R.

    1994-10-25

    An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control. 123 figs.

  18. Fuzzy logic merger of spectral and ecological information for improved montane forest mapping.

    USGS Publications Warehouse

    White, Joseph D.; Running, Steven W.; Ryan, Kevin C.; Key, Carl H.

    2002-01-01

    Environmental data are often utilized to guide interpretation of spectral information based on context, however, these are also important in deriving vegetation maps themselves, especially where ecological information can be mapped spatially. A vegetation classification procedure is presented which combines a classification of spectral data from Landsat‐5 Thematic Mapper (TM) and environmental data based on topography and fire history. These data were combined utilizing fuzzy logic where assignment of each pixel to a single vegetation category was derived comparing the partial membership of each vegetation category within spectral and environmental classes. Partial membership was assigned from canopy cover for forest types measured from field sampling. Initial classification of spectral and ecological data produced map accuracies of less than 50% due to overlap between spectrally similar vegetation and limited spatial precision for predicting local vegetation types solely from the ecological information. Combination of environmental data through fuzzy logic increased overall mapping accuracy (70%) in coniferous forest communities of northwestern Montana, USA.

  19. A fuzzy logic model to forecast stock market momentum in Indonesia's property and real estate sector

    NASA Astrophysics Data System (ADS)

    Penawar, H. K.; Rustam, Z.

    2017-07-01

    The Capital market has the important role in Indonesia's economy. The capital market does not only support the economy of Indonesia but also being an indicator Indonesia's economy improvement. Something that has been traded in the capital market is stock (stock market). Nowadays, the stock market is full of uncertainty. That uncertainty values make predicting stock market is all that we have to do before we make a decision in the stock market. One that can be predicted in the stock market is momentum. To forecast stock market momentum, it can use fuzzy logic model. In the process of modeling, it will be used 14 days historical data that consisting the value of open, high, low, and close, to predict the next 5 days momentum categories. There are three momentum categories namely Bullish, Neutral, and Bearish. To illustrate the fuzzy logic model, we will use stocks data from several companies that listed on Indonesia Stock Exchange (IDX) in property and real estate sector.

  20. Investigation of the Flutter Suppression by Fuzzy Logic Control for Hypersonic Wing

    NASA Astrophysics Data System (ADS)

    Li, Dongxu; Luo, Qing; Xu, Rui

    This paper presents a fundamental study of flutter characteristics and control performance of an aeroelastic system based on a two-dimensional double wedge wing in the hypersonic regime. Dynamic equations were established based on the modified third order nonlinear piston theory and some nonlinear structural effects are also included. A set of important parameters are observed. And then aeroelastic control law is designed to suppress the amplitude of the LCOs for the system in the sub/supercritical speed range by applying fuzzy logic control on the input of the deflection of the flap. The overall effects of the parameters on the aeroelastic system were outlined. Nonlinear aeroelastic responses in the open- and closed-loop system are obtained through numerical methods. The simulations show fuzzy logic control methods are effective in suppressing flutter and provide a smart approach for this complicated system.

  1. A Genetic Algorithm Optimised Fuzzy Logic Controller for Automatic Generation Control for Single Area System

    NASA Astrophysics Data System (ADS)

    Saini, J. S.; Jain, V.

    2015-03-01

    This paper presents a genetic algorithm (GA)-based design and optimization of fuzzy logic controller (FLC) for automatic generation control (AGC) for a single area. FLCs are characterized by a set of parameters, which are optimized using GA to improve their performance. The design of input and output membership functions (mfs) of an FLC is carried out by automatically tuning (off-line) the parameters of the membership functions. Tuning is based on maximization of a comprehensive fitness function constructed as inverse of a weighted average of three performance indices, i.e., integral square deviation (ISD), the integral of square of the frequency deviation and peak overshoot (Mp), and settling time (ts). The GA-optimized FLC (GAFLC) shows better performance as compared to a conventional proportional integral (PI) and a hand-designed fuzzy logic controller not only for a standard system (displaying frequency deviations) but also under parametric and load disturbances.

  2. Adaptive fuzzy logic restriction rules for error correction and safe stimulation patterns during functional electrical stimulation.

    PubMed

    Hansen, M; Haugland, M K

    2001-01-01

    Adaptive restriction rules based on fuzzy logic have been developed to eliminate errors and to increase stimulation safety in the foot-drop correction application, specifically when using adaptive logic networks to provide a stimulation control signal based on neural activity recorded from peripheral sensory nerve branches. The fuzzy rules were designed to increase flexibility and offer easier customization, compared to earlier versions of restriction rules. The rules developed quantified the duration of swing and stance phases into states of accepting or rejecting new transitions, based on the cyclic nature of gait and statistics on the current gait patterns. The rules were easy to custom design for a specific application, using linguistic terms to model the actions of the rules. The rules were tested using pre-recorded gait data processed through a gait event detector and proved to reduce detection delay and the number of errors, compared to conventional rules.

  3. A new approach to diabetic control: fuzzy logic and insulin pump technology.

    PubMed

    Grant, Paul

    2007-09-01

    Diabetes is a major health problem. Since the utilisation of insulin in the 1920s there have been myriad problems in developing suitable technologies to formulate and administer correct dosages to temper this metabolic disease. From multiple daily injections, nasal inhalations and enzymatic supplementation these artificial shortcuts still do not have the ability to fully replicate a 'normoglycaemic' state of being. In this paper, we sought to explore the use of insulin pumps and the application of fuzzy logic technology to act as an 'artificial pancreas' in diabetic patients. This paper builds on our previous work [Grant P, Naesh, O. Fuzzy logic and decision-making in anaesthetics. J Roy Soc Med 2005;98(1):7-9 [review

  4. Fuzzy logic based expert system for the treatment of mobile tooth.

    PubMed

    Mago, Vijay Kumar; Mago, Anjali; Sharma, Poonam; Mago, Jagmohan

    2011-01-01

    The aim of this research work is to design an expert system to assist dentist in treating the mobile tooth. There is lack of consistency among dentists in choosing the treatment plan. Moreover, there is no expert system currently available to verify and support such decision making in dentistry. A Fuzzy Logic based expert system has been designed to accept imprecise and vague values of dental sign-symptoms related to mobile tooth and the system suggests treatment plan(s). The comparison of predictions made by the system with those of the dentist is conducted. Chi-square Test of homogeneity is conducted and it is found that the system is capable of predicting accurate results. With this system, dentist feels more confident while planning the treatment of mobile tooth as he can verify his decision with the expert system. The authors also argue that Fuzzy Logic provides an appropriate mechanism to handle imprecise values of dental domain.

  5. A Novel Strain-Based Method to Estimate Tire Conditions Using Fuzzy Logic for Intelligent Tires.

    PubMed

    Garcia-Pozuelo, Daniel; Olatunbosun, Oluremi; Yunta, Jorge; Yang, Xiaoguang; Diaz, Vicente

    2017-02-10

    The so-called intelligent tires are one of the most promising research fields for automotive engineers. These tires are equipped with sensors which provide information about vehicle dynamics. Up to now, the commercial intelligent tires only provide information about inflation pressure and their contribution to stability control systems is currently very limited. Nowadays one of the major problems for intelligent tire development is how to embed feasible and low cost sensors to obtain reliable information such as inflation pressure, vertical load or rolling speed. These parameters provide key information for vehicle dynamics characterization. In this paper, we propose a novel algorithm based on fuzzy logic to estimate the mentioned parameters by means of a single strain-based system. Experimental tests have been carried out in order to prove the suitability and durability of the proposed on-board strain sensor system, as well as its low cost advantages, and the accuracy of the obtained estimations by means of fuzzy logic.

  6. Fuzzy-logic-based hybrid locomotion mode classification for an active pelvis orthosis: Preliminary results.

    PubMed

    Yuan, Kebin; Parri, Andrea; Yan, Tingfang; Wang, Long; Munih, Marko; Vitiello, Nicola; Wang, Qining

    2015-01-01

    In this paper, we present a fuzzy-logic-based hybrid locomotion mode classification method for an active pelvis orthosis. Locomotion information measured by the onboard hip joint angle sensors and the pressure insoles is used to classify five locomotion modes, including two static modes (sitting, standing still), and three dynamic modes (level-ground walking, ascending stairs, and descending stairs). The proposed method classifies these two kinds of modes first by monitoring the variation of the relative hip joint angle between the two legs within a specific period. Static states are then classified by the time-based absolute hip joint angle. As for dynamic modes, a fuzzy-logic based method is proposed for the classification. Preliminary experimental results with three able-bodied subjects achieve an off-line classification accuracy higher than 99.49%.

  7. Train velocity estimation method based on an adaptive filter with fuzzy logic

    NASA Astrophysics Data System (ADS)

    Pichlík, Petr; Zděnek, Jiří

    2017-03-01

    The train velocity is difficult to determine when the velocity is measured only on the driven or braked locomotive wheelsets. In this case, the calculated train velocity is different from the actual train velocity due to slip velocity or skid velocity respectively. The train velocity is needed for a locomotive controller proper work. For this purpose, an adaptive filter that is tuned by a fuzzy logic is designed and described in the paper. The filter calculates the train longitudinal velocity based on locomotive wheelset velocity. The fuzzy logic is used for the tuning of the filter according to actual wheelset acceleration and wheelset jerk. The simulation results are based on real measured data on a freight train. The results show that the calculated velocity corresponds to the actual train velocity.

  8. Dynamic response improvement of doubly fed induction generator-based wind farm using fuzzy logic controller

    NASA Astrophysics Data System (ADS)

    Hasanien, Hany M.; Al-Ammar, Essam A.

    2012-11-01

    Doubly fed induction generator (DFIG) based wind farm is today the most widely used concept. This paper presents dynamic response enhancement of DFIG based wind farm under remote fault conditions using the fuzzy logic controller. The goal of the work is to improve the dynamic response of DFIG based wind farm during and after the clearance of fault using the proposed controller. The stability of wind farm during and after the clearance of fault is investigated. The effectiveness of the fuzzy logic controller is then compared with that of a PI controller. The validity of the controllers in restoring the wind farms normal operation after the clearance of fault is illustrated by the simulation results which are carried out using MATLAB/SIMULINK. Simulation results are analyzed under different fault conditions.

  9. Fuzzy logic electric vehicle regenerative antiskid braking and traction control system

    SciTech Connect

    Cikanek, Susan R.

    1994-01-01

    An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control.

  10. Fuzzy logic electric vehicle regenerative antiskid braking and traction control system

    SciTech Connect

    Cikanek, S.R.

    1994-10-25

    An regenerative antiskid braking and traction control system using fuzzy logic for an electric or hybrid vehicle having a regenerative braking system operatively connected to an electric traction motor, and a separate hydraulic braking system includes sensors for monitoring present vehicle parameters and a processor, responsive to the sensors, for calculating vehicle parameters defining the vehicle behavior not directly measurable by the sensor and determining if regenerative antiskid braking control, requiring hydraulic braking control, and requiring traction control are required. The processor then employs fuzzy logic based on the determined vehicle state and provides command signals to a motor controller to control operation of the electric traction motor and to the brake controller to control fluid pressure applied at each vehicle wheel to provide the appropriate regenerative braking control, hydraulic braking control, and traction control. 123 figs.

  11. PI and fuzzy logic controllers for shunt Active Power Filter--a report.

    PubMed

    P, Karuppanan; Mahapatra, Kamala Kanta

    2012-01-01

    This paper presents a shunt Active Power Filter (APF) for power quality improvements in terms of harmonics and reactive power compensation in the distribution network. The compensation process is based only on source current extraction that reduces the number of sensors as well as its complexity. A Proportional Integral (PI) or Fuzzy Logic Controller (FLC) is used to extract the required reference current from the distorted line-current, and this controls the DC-side capacitor voltage of the inverter. The shunt APF is implemented with PWM-current controlled Voltage Source Inverter (VSI) and the switching patterns are generated through a novel Adaptive-Fuzzy Hysteresis Current Controller (A-F-HCC). The proposed adaptive-fuzzy-HCC is compared with fixed-HCC and adaptive-HCC techniques and the superior features of this novel approach are established. The FLC based shunt APF system is validated through extensive simulation for diode-rectifier/R-L loads.

  12. The stock-flow model of spatial data infrastructure development refined by fuzzy logic.

    PubMed

    Abdolmajidi, Ehsan; Harrie, Lars; Mansourian, Ali

    2016-01-01

    The system dynamics technique has been demonstrated to be a proper method by which to model and simulate the development of spatial data infrastructures (SDI). An SDI is a collaborative effort to manage and share spatial data at different political and administrative levels. It is comprised of various dynamically interacting quantitative and qualitative (linguistic) variables. To incorporate linguistic variables and their joint effects in an SDI-development model more effectively, we suggest employing fuzzy logic. Not all fuzzy models are able to model the dynamic behavior of SDIs properly. Therefore, this paper aims to investigate different fuzzy models and their suitability for modeling SDIs. To that end, two inference and two defuzzification methods were used for the fuzzification of the joint effect of two variables in an existing SDI model. The results show that the Average-Average inference and Center of Area defuzzification can better model the dynamics of SDI development.

  13. Flight test results of the fuzzy logic adaptive controller-helicopter (FLAC-H)

    NASA Astrophysics Data System (ADS)

    Wade, Robert L.; Walker, Gregory W.

    1996-05-01

    The fuzzy logic adaptive controller for helicopters (FLAC-H) demonstration is a cooperative effort between the US Army Simulation, Training, and Instrumentation Command (STRICOM), the US Army Aviation and Troop Command, and the US Army Missile Command to demonstrate a low-cost drone control system for both full-scale and sub-scale helicopters. FLAC-H was demonstrated on one of STRICOM's fleet of full-scale rotary-winged target drones. FLAC-H exploits fuzzy logic in its flight control system to provide a robust solution to the control of the helicopter's dynamic, nonlinear system. Straight forward, common sense fuzzy rules governing helicopter flight are processed instead of complex mathematical models. This has resulted in a simplified solution to the complexities of helicopter flight. Incorporation of fuzzy logic reduced the cost of development and should also reduce the cost of maintenance of the system. An adaptive algorithm allows the FLAC-H to 'learn' how to fly the helicopter, enabling the control system to adjust to varying helicopter configurations. The adaptive algorithm, based on genetic algorithms, alters the fuzzy rules and their related sets to improve the performance characteristics of the system. This learning allows FLAC-H to automatically be integrated into a new airframe, reducing the development costs associated with altering a control system for a new or heavily modified aircraft. Successful flight tests of the FLAC-H on a UH-1H target drone were completed in September 1994 at the White Sands Missile Range in New Mexico. This paper discuses the objective of the system, its design, and performance.

  14. Dispositional logic

    NASA Technical Reports Server (NTRS)

    Le Balleur, J. C.

    1988-01-01

    The applicability of conventional mathematical analysis (based on the combination of two-valued logic and probability theory) to problems in which human judgment, perception, or emotions play significant roles is considered theoretically. It is shown that dispositional logic, a branch of fuzzy logic, has particular relevance to the common-sense reasoning typical of human decision-making. The concepts of dispositionality and usuality are defined analytically, and a dispositional conjunctive rule and dispositional modus ponens are derived.

  15. Adaptively managing wildlife for climate change: a fuzzy logic approach.

    PubMed

    Prato, Tony

    2011-07-01

    Wildlife managers have little or no control over climate change. However, they may be able to alleviate potential adverse impacts of future climate change by adaptively managing wildlife for climate change. In particular, wildlife managers can evaluate the efficacy of compensatory management actions (CMAs) in alleviating potential adverse impacts of future climate change on wildlife species using probability-based or fuzzy decision rules. Application of probability-based decision rules requires managers to specify certain probabilities, which is not possible when they are uncertain about the relationships between observed and true ecological conditions for a species. Under such uncertainty, the efficacy of CMAs can be evaluated and the best CMA selected using fuzzy decision rules. The latter are described and demonstrated using three constructed cases that assume: (1) a single ecological indicator (e.g., population size for a species) in a single time period; (2) multiple ecological indicators for a species in a single time period; and (3) multiple ecological conditions for a species in multiple time periods.

  16. PI and fuzzy logic controllers for shunt Active Power Filter--a report.

    PubMed

    Karuppanan, P; Mahapatra, Kamala Kanta

    2012-09-01

    The authors acknowledge certain errors in their recently published paper titled "PI and fuzzy logic controllers for shunt active power filter--A report.The ambiguity in band width calculation of adaptive hysteresis controller and control aspects of dc-link voltage issues are addressed. The shunt APF system is validated through extensive simulation and the results are support features of the proposed technique.

  17. Fuzzy Logic Module of Convolutional Neural Network for Handwritten Digits Recognition

    NASA Astrophysics Data System (ADS)

    Popko, E. A.; Weinstein, I. A.

    2016-08-01

    Optical character recognition is one of the important issues in the field of pattern recognition. This paper presents a method for recognizing handwritten digits based on the modeling of convolutional neural network. The integrated fuzzy logic module based on a structural approach was developed. Used system architecture adjusted the output of the neural network to improve quality of symbol identification. It was shown that proposed algorithm was flexible and high recognition rate of 99.23% was achieved.

  18. A fuzzy logic controller for hormone administration using an implantable pump

    NASA Technical Reports Server (NTRS)

    Coles, L. Stephen; Wells, George H., Jr.

    1994-01-01

    This paper describes the requirements for a Fuzzy Logic Controller for the physiologic administration of hormones by means of a FDA-approved surgically implantable infusion pump. Results of a LabVIEW computer simulation for the administration of insulin for diabetic adult patients as well as human growth hormone for pediatric patients are presented. A VHS video tape of the simulation in action has been prepared and is available for viewing.

  19. Environmental impact assessment procedure: A new approach based on fuzzy logic

    SciTech Connect

    Peche, Roberto; Rodriguez, Esther

    2009-09-15

    The information related to the different environmental impacts produced by the execution of activities and projects is often limited, described by semantic variables and, affected by a high degree of inaccuracy and uncertainty, thereby making fuzzy logic a suitable tool with which to express and treat this information. The present study proposes a new approach based on fuzzy logic to carry out the environmental impact assessment (EIA) of these activities and projects. Firstly, a set of impact properties is stated and two nondimensional parameters - ranging from 0 to 100 -are assigned, (p{sub i}) to assess the value of the property and (v{sub i}) to assess its contribution to each environmental impact. Next, the impact properties are described by means of fuzzy numbers p{sub i}{sup -} using generalised confidence intervals. Then, a procedure based on fuzzy arithmetic is developed to define the assessment functions v-bar = f(p-bar) - conventional mathematical functions, which incorporate the knowledge of these impact properties and give the fuzzy values v{sub i}{sup -} corresponding to each p{sub i}{sup -}. Subsequently, the fuzzy value of each environmental impact V-bar is estimated by aggregation of the values v{sub i}{sup -}, in order to obtain the total positive and negative environmental impacts V{sup +-} and V{sup --} and, later - from them - the total environmental impact of the activity or project TV{sup -}. Finally, the defuzzyfication of TV{sup -} leads to a punctual impact estimator TV{sup (1)} - a conventional EI estimation - and its corresponding uncertainty interval estimator left brace(delta{sub l}(TV{sup -}),delta{sub r}(TV{sup -})right brace, which represent the total value of the environmental impact caused by the execution of the considered activity or project.

  20. Fuzzy logic based risk assessment of effluents from waste-water treatment plants.

    PubMed

    Cabanillas, Julián; Ginebreda, Antoni; Guillén, Daniel; Martínez, Elena; Barceló, Damià; Moragas, Lucas; Robusté, Jordi; Darbra, Rosa Ma

    2012-11-15

    This paper presents a new methodology to assess the risk of water effluents from waste-water treatment plants (WWTPs) based on fuzzy logic, a well-known theory to deal with uncertainty, especially in the environmental field where data are often lacking. The method has been tested using the effluent's pollution data coming from 22 waste-water treatment plants (WWTPs) located in Catalonia (NE Spain). Thirty-eight pollutants were analyzed along three campaigns performed yearly from 2008 to 2010. Whereas 9 compounds have been detected in more than 70% of the samples analyzed, 7 compounds have been found at levels equal or higher than the river Environmental Quality Standards set by the Water Framework Directive. Upon combination of both criteria (presence and concentration), compounds of greatest environmental concern in the WWTP studied are nickel, the herbicide diuron, and the endocrine disruptors nonyl and octylphenol. It is remarkable the low variability of the pollutant concentration just differing for the case of nickel and zinc. These low values of exposure together with other pollutants' characteristics provide a medium or low risk assessment for all the WWTPs. The results of this new method have been compared with COMMPS procedure, a solid method developed in the context of the Water Framework Directive, and they show that the fuzzy model is more conservative than COMMPS. This is due to different reasons: the fuzzy model takes into account the persistence of chemical compounds whereas COMMPS does not; the fuzzy model includes the weights provided by an expert group inquired in previous works and also considers the uncertainty of the environmental data, avoiding the crisp values and offering a range of overlapping between the different fuzzy sets. However, the results even if being more conservative with fuzzy logic, are in good agreement with a solid methodology such as the COMMPS procedure.

  1. Fuzzy-logic based strategy for validation of multiplex methods: example with qualitative GMO assays.

    PubMed

    Bellocchi, Gianni; Bertholet, Vincent; Hamels, Sandrine; Moens, W; Remacle, José; Van den Eede, Guy

    2010-02-01

    This paper illustrates the advantages that a fuzzy-based aggregation method could bring into the validation of a multiplex method for GMO detection (DualChip GMO kit, Eppendorf). Guidelines for validation of chemical, bio-chemical, pharmaceutical and genetic methods have been developed and ad hoc validation statistics are available and routinely used, for in-house and inter-laboratory testing, and decision-making. Fuzzy logic allows summarising the information obtained by independent validation statistics into one synthetic indicator of overall method performance. The microarray technology, introduced for simultaneous identification of multiple GMOs, poses specific validation issues (patterns of performance for a variety of GMOs at different concentrations). A fuzzy-based indicator for overall evaluation is illustrated in this paper, and applied to validation data for different genetically modified elements. Remarks were drawn on the analytical results. The fuzzy-logic based rules were shown to be applicable to improve interpretation of results and facilitate overall evaluation of the multiplex method.

  2. Hybrid intelligent systems for time series prediction using neural networks, fuzzy logic, and fractal theory.

    PubMed

    Castillo, O; Melin, P

    2002-01-01

    In this paper, we describe a new method for the estimation of the fractal dimension of a geometrical object using fuzzy logic techniques. The fractal dimension is a mathematical concept, which measures the geometrical complexity of an object. The algorithms for estimating the fractal dimension calculate a numerical value using as data a time series for the specific problem. This numerical (crisp) value gives an idea of the complexity of the geometrical object (or time series). However, there is an underlying uncertainty in the estimation of the fractal dimension because we use only a sample of points of the object, and also because the numerical algorithms for the fractal dimension are not completely accurate. For this reason, we have proposed a new definition of the fractal dimension that incorporates the concept of a fuzzy set. This new definition can be considered a weaker definition (but more realistic) of the fractal dimension, and we have named this the "fuzzy fractal dimension." We can apply this new definition of the fractal dimension in conjunction with soft computing techniques for the problem of time series prediction. We have developed hybrid intelligent systems combining neural networks, fuzzy logic, and the fractal dimension, for the problem of time series prediction, and we have achieved very good results.

  3. Spatiotemporal prediction applying fuzzy logic in a sequence of satellite images

    NASA Astrophysics Data System (ADS)

    Mezzadri-Centeno, Tania; Selleron, Gilles

    2002-01-01

    Spatial evolutions of anthropized ecosystems and the progressive transformation of spaces in the course of time emerge more and more as a special interest issue in researches about the environment. This evolution constitutes one of the major concerns in the domain of environmental space management. The landscape evolution of a region area and the perspectives for a future state rises an issue particularly important. What will be the state of the region in 15, 30 or 50 years? Time can produce transformations over a region area like emergence, disappearance or union of spatial entities... These transformations are called temporal phenomena. We propose to predict the forestry evolution in the forthcoming years on an experimental area, which reveals these spatial transformations. The proposed method is based on the analysis of terrain landscape given a sequence of n satellite images, which represent the state of a region area in different years. For these purposes, we have developed a specific spatio-temporal prediction approach, linking results of forestry evolution analysis and fuzzy logic. The method is supported by the analysis of the landscape dynamics of a test-site located in a tropical rain country: the oriental piedmont of Andes Mountain in Venezuela. This large area - at the scale of a spot satellite image - is typical of tropical deforestation in a pioneer front. The presented approach allows the geographer interested in environmental prospective problems to get type cartographical documents showing future conditions of a landscape. The experimental tests have showed promising results.

  4. Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks

    PubMed Central

    Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

    2015-01-01

    Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node’s role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network’s lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively. PMID:26295238

  5. Towards Resilient Critical Infrastructures: Application of Type-2 Fuzzy Logic in Embedded Network Security Cyber Sensor

    SciTech Connect

    Ondrej Linda; Todd Vollmer; Jim Alves-Foss; Milos Manic

    2011-08-01

    Resiliency and cyber security of modern critical infrastructures is becoming increasingly important with the growing number of threats in the cyber-environment. This paper proposes an extension to a previously developed fuzzy logic based anomaly detection network security cyber sensor via incorporating Type-2 Fuzzy Logic (T2 FL). In general, fuzzy logic provides a framework for system modeling in linguistic form capable of coping with imprecise and vague meanings of words. T2 FL is an extension of Type-1 FL which proved to be successful in modeling and minimizing the effects of various kinds of dynamic uncertainties. In this paper, T2 FL provides a basis for robust anomaly detection and cyber security state awareness. In addition, the proposed algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental cyber-security test-bed.

  6. Fuzzy Logic based Handoff Latency Reduction Mechanism in Layer 2 of Heterogeneous Mobile IPv6 Networks

    NASA Astrophysics Data System (ADS)

    Anwar, Farhat; Masud, Mosharrof H.; Latif, Suhaimi A.

    2013-12-01

    Mobile IPv6 (MIPv6) is one of the pioneer standards that support mobility in IPv6 environment. It has been designed to support different types of technologies for providing seamless communications in next generation network. However, MIPv6 and subsequent standards have some limitations due to its handoff latency. In this paper, a fuzzy logic based mechanism is proposed to reduce the handoff latency of MIPv6 for Layer 2 (L2) by scanning the Access Points (APs) while the Mobile Node (MN) is moving among different APs. Handoff latency occurs when the MN switches from one AP to another in L2. Heterogeneous network is considered in this research in order to reduce the delays in L2. Received Signal Strength Indicator (RSSI) and velocity of the MN are considered as the input of fuzzy logic technique. This technique helps the MN to measure optimum signal quality from APs for the speedy mobile node based on fuzzy logic input rules and makes a list of interfaces. A suitable interface from the list of available interfaces can be selected like WiFi, WiMAX or GSM. Simulation results show 55% handoff latency reduction and 50% packet loss improvement in L2 compared to standard to MIPv6.

  7. Proposed Clinical Application for Tuning Fuzzy Logic Controller of Artificial Pancreas Utilizing a Personalization Factor

    PubMed Central

    Mauseth, Richard; Wang, Youqing; Dassau, Eyal; Kircher, Robert; Matheson, Donald; Zisser, Howard; Jovanoviĉ, Lois; Doyle, Francis J.

    2010-01-01

    Background Physicians tailor insulin dosing based on blood glucose goals, response to insulin, compliance, lifestyle, eating habits, daily schedule, and fear of and ability to detect hypoglycemia. Method We introduce a method that allows a physician to tune a fuzzy logic controller (FLC) artificial pancreas (AP) for a particular patient. It utilizes the physician’s judgment and weighing of various factors. The personalization factor (PF) is a scaling of the dose produced by the FLC and is used to customize the dosing. The PF has discrete values of 1 through 5. The proposed method was developed using a database of results from 30 University of Virginia/Padova Metabolic Simulator in silico subjects (10 adults, 10 adolescents, and 10 children). Various meal sizes and timing were used to provide the physician information on which to base an initial dosing regimen and PF. Future decisions on dosing aggressiveness using the PF would be based on the patient’s data at follow-up. Results Three examples of a wide variation in diabetes situations are given to illustrate the physician’s thought process when initially configuring the AP system for a specific patient. Conclusions Fuzzy logic controllers are developed by encoding human expertise into the design of the controller. The FLC methodology allows for the real-time scaling of doses without compromising the integrity of the dosing rules matrix. The use of the PF to individualize the AP system is enabled by the fuzzy logic development methodology. PMID:20663457

  8. Proposed clinical application for tuning fuzzy logic controller of artificial pancreas utilizing a personalization factor.

    PubMed

    Mauseth, Richard; Wang, Youqing; Dassau, Eyal; Kircher, Robert; Matheson, Donald; Zisser, Howard; Jovanovic, Lois; Doyle, Francis J

    2010-07-01

    Physicians tailor insulin dosing based on blood glucose goals, response to insulin, compliance, lifestyle, eating habits, daily schedule, and fear of and ability to detect hypoglycemia. We introduce a method that allows a physician to tune a fuzzy logic controller (FLC) artificial pancreas (AP) for a particular patient. It utilizes the physician's judgment and weighing of various factors. The personalization factor (PF) is a scaling of the dose produced by the FLC and is used to customize the dosing. The PF has discrete values of 1 through 5. The proposed method was developed using a database of results from 30 University of Virginia/Padova Metabolic Simulator in silico subjects (10 adults, 10 adolescents, and 10 children). Various meal sizes and timing were used to provide the physician information on which to base an initial dosing regimen and PF. Future decisions on dosing aggressiveness using the PF would be based on the patient's data at follow-up. Three examples of a wide variation in diabetes situations are given to illustrate the physician's thought process when initially configuring the AP system for a specific patient. Fuzzy logic controllers are developed by encoding human expertise into the design of the controller. The FLC methodology allows for the real-time scaling of doses without compromising the integrity of the dosing rules matrix. The use of the PF to individualize the AP system is enabled by the fuzzy logic development methodology. 2010 Diabetes Technology Society.

  9. Fuzzy Logic-Based Guaranteed Lifetime Protocol for Real-Time Wireless Sensor Networks.

    PubMed

    Shah, Babar; Iqbal, Farkhund; Abbas, Ali; Kim, Ki-Il

    2015-08-18

    Few techniques for guaranteeing a network lifetime have been proposed despite its great impact on network management. Moreover, since the existing schemes are mostly dependent on the combination of disparate parameters, they do not provide additional services, such as real-time communications and balanced energy consumption among sensor nodes; thus, the adaptability problems remain unresolved among nodes in wireless sensor networks (WSNs). To solve these problems, we propose a novel fuzzy logic model to provide real-time communication in a guaranteed WSN lifetime. The proposed fuzzy logic controller accepts the input descriptors energy, time and velocity to determine each node's role for the next duration and the next hop relay node for real-time packets. Through the simulation results, we verified that both the guaranteed network's lifetime and real-time delivery are efficiently ensured by the new fuzzy logic model. In more detail, the above-mentioned two performance metrics are improved up to 8%, as compared to our previous work, and 14% compared to existing schemes, respectively.

  10. Clinical outcome prediction in aneurysmal subarachnoid hemorrhage using Bayesian neural networks with fuzzy logic inferences.

    PubMed

    Lo, Benjamin W Y; Macdonald, R Loch; Baker, Andrew; Levine, Mitchell A H

    2013-01-01

    The novel clinical prediction approach of Bayesian neural networks with fuzzy logic inferences is created and applied to derive prognostic decision rules in cerebral aneurysmal subarachnoid hemorrhage (aSAH). The approach of Bayesian neural networks with fuzzy logic inferences was applied to data from five trials of Tirilazad for aneurysmal subarachnoid hemorrhage (3551 patients). Bayesian meta-analyses of observational studies on aSAH prognostic factors gave generalizable posterior distributions of population mean log odd ratios (ORs). Similar trends were noted in Bayesian and linear regression ORs. Significant outcome predictors include normal motor response, cerebral infarction, history of myocardial infarction, cerebral edema, history of diabetes mellitus, fever on day 8, prior subarachnoid hemorrhage, admission angiographic vasospasm, neurological grade, intraventricular hemorrhage, ruptured aneurysm size, history of hypertension, vasospasm day, age and mean arterial pressure. Heteroscedasticity was present in the nontransformed dataset. Artificial neural networks found nonlinear relationships with 11 hidden variables in 1 layer, using the multilayer perceptron model. Fuzzy logic decision rules (centroid defuzzification technique) denoted cut-off points for poor prognosis at greater than 2.5 clusters. This aSAH prognostic system makes use of existing knowledge, recognizes unknown areas, incorporates one's clinical reasoning, and compensates for uncertainty in prognostication.

  11. A Position Controller Model on Color-Based Object Tracking using Fuzzy Logic

    NASA Astrophysics Data System (ADS)

    Cahyo Wibowo, Budi; Much Ibnu Subroto, Imam; Arifin, Bustanul

    2017-04-01

    Robotics vision is applying technology on the camera to view the environmental conditions as well as the function of the human eye. Colour object tracking system is one application of robotics vision technology with the ability to follow the object being detected. Several methods have been used to generate a good response position control, but most are still using conventional control approach. Fuzzy logic which includes several step of which is to determine the value of crisp input must be fuzzification. The output of fuzzification is forwarded to the process of inference in which there are some fuzzy logic rules. The inference output forwarded to the process of defuzzification to be transformed into outputs (crisp output) to drive the servo motors on the X-axis and Y-axis. Fuzzy logic control is applied to the color-based object tracking system, the system is successful to follow a moving object with average speed of 7.35 cm/s in environments with 117 lux light intensity.

  12. Grey fuzzy logic approach for the optimization of DLC thin film coating process parameters using PACVD technique

    NASA Astrophysics Data System (ADS)

    Ghadai, R. K.; Das, P. P.; Shivakoti, I.; Mondal, S. C.; Swain, B. P.

    2017-07-01

    Diamond-like carbon (DLC) coatings are widely used in medical, manufacturing and aerospace industries due to their excellent mechanical, biological, optical and tribological properties. The selection of optimal process parameters for efficient characteristics of DLC film is always a challenging issue for the materials science researchers. The optimal combination of the process parameters involved in the deposition of DLC films provide a better result, which subsequently help other researchers to choose the process parameters. In the present work Grey Relation Analysis (GRA) and Fuzzy-logic are being used for the optimization of process parameters in DLC film coating by using plasma assist chemical vapour deposition (PACVD) technique. The bias voltage, bias frequency, deposition pressure, gas composition are considered as input process parameters and hardness (GPa), Young's modulus (GPa), ratio between diamond to graphic fraction, (Id/Ig) ratio are considered as response parameters. The input parameters are optimized by grey fuzzy analysis. The contribution of individual input parameter is done by ANOVA. In this analysis found that bias voltage having the least influence and gas composition has highest influence in the PACVD deposited DLC films. The grey fuzzy analysis results indicated that optimum results for bias voltage, bias frequency, deposition pressure, gas composition for the DLC thin films are -50 V, 6 kHz, 4 μbar and 60:40 % respectively.

  13. Risk management and the precautionary principle: a fuzzy logic model.

    PubMed

    Cameron, Enrico; Peloso, Gian Francesco

    2005-08-01

    The aim of this article is to illustrate a procedure for applying the precautionary principle within a strategy for reducing the possibility of underestimating the effective risk caused by a phenomenon, product, or process, and of adopting insufficient risk reduction measures or overlooking their need. We start by simply defining risk as the product between the numerical expression of the adverse consequences of an event and the likelihood of its occurrence or the likelihood that such consequences will occur. Uncertainty in likelihood estimates and several key concepts inherent to the precautionary principle, such as sufficient certainty, prevention, and desired level of protection, are represented as fuzzy sets. The strategy described may be viewed as a simplified example of a precautionary decision process that has been chiefly conceived as a theoretical contribution to the debate concerning the precautionary principle, the quantification of its application, and the formal approach to such problems.

  14. Fuzzy logic controller approach in quality and productivity improvement program (PPKP)

    NASA Astrophysics Data System (ADS)

    Ruza, Nadiah; Mustafa, Zainol; Rika Fatimah, P. L.; Hussain, Saiful Izzuan

    2013-04-01

    The education sector plays a major role in building the stability and strength of a country and also the main channel in shaping the quality of nation. Each generation have different educational level. Therefore, improvements should be made on an on-going basis to ensure that quality of education is at high level all the time. In general, this study aimed to determine the effectiveness of the education system for Quality and Productivity Improvement Program (PPKP), Universiti Kebangsaan Malaysia (UKM) from the perspective of alumni as well as their satisfaction and importance level on how PPKP be able to meet the needs of their students. This study discusses the application of Fuzzy Logic Control analysis, which is flexible and adjustable. This analysis also identifies the program's quality of education system through alumni point of view. Overall, it was found that 93.4 percent of respondents felt that all four dimensions of students' needs have high level of importance. The rest felt that the importance level of all four dimensions is modest. Next, in term of satisfaction level with PPKP, only one percent was very satisfied with PPKP's role in meeting the needs of students and the rest felt that their needs are met only at moderate level. Results of this study could be used to improve the quality of education system for PPKP.

  15. Free vibration control of smart composite beams using particle swarm optimized self-tuning fuzzy logic controller

    NASA Astrophysics Data System (ADS)

    Zorić, Nemanja D.; Simonović, Aleksandar M.; Mitrović, Zoran S.; Stupar, Slobodan N.; Obradović, Aleksandar M.; Lukić, Nebojša S.

    2014-10-01

    This paper deals with active free vibrations control of smart composite beams using particle-swarm optimized self-tuning fuzzy logic controller. In order to improve the performance and robustness of the fuzzy logic controller, this paper proposes integration of self-tuning method, where scaling factors of the input variables in the fuzzy logic controller are adjusted via peak observer, with optimization of membership functions using the particle swarm optimization algorithm. The Mamdani and zero-order Takagi-Sugeno-Kang fuzzy inference methods are employed. In order to overcome stability problem, at the same time keeping advantages of the proposed self-tuning fuzzy logic controller, this controller is combined with the LQR making composite controller. Several numerical studies are provided for the cantilever composite beam for both single mode and multimodal cases. In the multimodal case, a large-scale system is decomposed into smaller subsystems in a parallel structure. In order to represent the efficiency of the proposed controller, obtained results are compared with the corresponding results in the cases of the optimized fuzzy logic controllers with constant scaling factors and linear quadratic regulator.

  16. 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.

  17. Fuzzy logic based intelligent control of a variable speed cage machine wind generation system. Report for January 1994-June 1995

    SciTech Connect

    Simoes, M.G.; Bose, B.K.; Spiegel, R.J.

    1995-10-01

    The paper describes a variable speed wind generation system where fuzzy logic principles are used for efficiency optimization and performance enhancement control. A squirrel cage induction generator feeds the power to a double-sided pulse width modulated converter system which pumps power to a utility grid or can supply to an autonomous system. The generation system has fuzzy logic control with vector control in the inner loops. A fuzzy controller tracks the generator speed with the wind velocity to extract the maximum power. A second fuzzy controller programs the machine flux for light load efficiency improvement, and third fuzzy controller gives robust speed control against wind gust and turbine oscillatory torque. The complete control system has been developed, analyzed, validated by simulation study, and then performances have been evaluated in detail.

  18. Modelling and Control of the Qball X4 Quadrotor System based on Pid and Fuzzy Logic Structure

    NASA Astrophysics Data System (ADS)

    Bodrumlu, Tolga; Turan Soylemez, Mehmet; Mutlu, Ilhan

    2017-01-01

    This work focuses on a quadrocopter model, which was developed by QuanserTM and named as Qball X4. First, mathematical model of the Qball X4 is obtained. Then, a conventional PID control technique is presented. This PID control parameters come from Qball user manual. After the presentation of conventional PID control, as an extension of the conventional PID control theory, a different fuzzy controller structure is given. The proposed fuzzy controller structure is based on fuzzy logic and its name is PID type fuzzy controller. All of the simulations are done in MATLABTM environment.

  19. Plasma position control in the STOR-M tokamak: A fuzzy logic approach

    NASA Astrophysics Data System (ADS)

    Morelli, Jordan Edwin

    Adequate control of the position of the plasma column within the STOR-M tokamak is a chief requirement in order for experimental quality discharges to be obtained. Optimal control over tokamak discharge parameters, including the plasma position, is very difficult to achieve. This is due in large part to the difficulty in modelling the tokamak discharge parameters, as they are highly nonlinear and time varying in nature. The difficulty of modelling the tokamak discharge parameters suggests that a control system, such as a fuzzy logic based controller, which does not require a system model may be well suited to the control of fusion plasma. In order to improve the quality of control over the plasma position within the STOR-M tokamak, the existing analog PID controller was modified. These modifications facilitate the application of a digital controller by a personal computer via the Advantech PCL-711B data acquisition card. The performance of the modified plasma position controller and an Arbitrary Signal Generator developed by the author was evaluated. This modified plasma position controller was applied successfully to the STOR-M tokamak during both normal mode and A.C. mode operation. In both cases, the modified controller provided adequate control over the position of the plasma column within the discharge chamber. Furthermore, the modified controller was more convenient to optimize than the original, existing analog PID controller. By taking advantage of the modifications that were made to the plasma position controller, a fuzzy logic controller was developed by the author. The fuzzy logic based plasma position controller was also successfully applied to the STOR-M tokamak during both normal mode and A.C. operation. The fuzzy controller was demonstrated to reliably provide a higher degree of control over the position of the plasma column within the STOR-M tokamak than the modified PID controller.

  20. Fuzzy logic for characterizing the moderate intensity of physical activity in children.

    PubMed

    Zitouni, Djamel; Guinhouya, Benjamin C

    2016-02-01

    The aim of this study was to better characterize the moderate intensity of PA among children by applying fuzzy logic as the most appropriate analytical approach. In this perspective, the 6-MWT was selected as a pertinent exercise modality, which covers as a whole, this intensity level. Methodological study. Fuzzy logic was applied to accelerometer output obtained on 46 children aged 9-11 years. A fuzzy subset A was defined from the reference set E using a membership function (degree of truth). To adequately tap the moderate PA, a core of X¯±σ and a support of X¯±2σ (with X¯ the mean, and σ the standard deviation of the distribution) were selected. The walking speed during the exercise averaged 6.1±0.6kmh(-1) and the mean HR was 135±14bpm. The movement count (419±127 to 433±148 counts) exhibited no significant changes during the test. A value of 260 counts per 5-s (i.e., 3120cpm) had equally 50% of degree of truth to encompass both "light" and "moderate" intensities of PA. Results suggest that the cut-point of >2296cpm covers a low PA at 100% and a moderate PA at 0%. Fuzzy logic provides a robust basis to processing accelerometer data, and brings a reliable solution to the concern about the in-between of PA intensities. Its application to calibration studies should not support the use of a cut-point of about 2000cpm in children, and linguistic variables should now be preferred to numbered data in defining PA intensities. Copyright © 2015 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

  1. Fuzzy-logic modeling of Fenton's strong chemical oxidation process treating three types of landfill leachates.

    PubMed

    Sari, Hanife; Yetilmezsoy, Kaan; Ilhan, Fatih; Yazici, Senem; Kurt, Ugur; Apaydin, Omer

    2013-06-01

    Three multiple input and multiple output-type fuzzy-logic-based models were developed as an artificial intelligence-based approach to model a novel integrated process (UF-IER-EDBM-FO) consisted of ultrafiltration (UF), ion exchange resins (IER), electrodialysis with bipolar membrane (EDBM), and Fenton's oxidation (FO) units treating young, middle-aged, and stabilized landfill leachates. The FO unit was considered as the key process for implementation of the proposed modeling scheme. Four input components such as H(2)O(2)/chemical oxygen demand ratio, H(2)O(2)/Fe(2+) ratio, reaction pH, and reaction time were fuzzified in a Mamdani-type fuzzy inference system to predict the removal efficiencies of chemical oxygen demand, total organic carbon, color, and ammonia nitrogen. A total of 200 rules in the IF-THEN format were established within the framework of a graphical user interface for each fuzzy-logic model. The product (prod) and the center of gravity (centroid) methods were performed as the inference operator and defuzzification methods, respectively, for the proposed prognostic models. Fuzzy-logic predicted results were compared to the outputs of multiple regression models by means of various descriptive statistical indicators, and the proposed methodology was tested against the experimental data. The testing results clearly revealed that the proposed prognostic models showed a superior predictive performance with very high determination coefficients (R (2)) between 0.930 and 0.991. This study indicated a simple means of modeling and potential of a knowledge-based approach for capturing complicated inter-relationships in a highly non-linear problem. Clearly, it was shown that the proposed prognostic models provided a well-suited and cost-effective method to predict removal efficiencies of wastewater parameters prior to discharge to receiving streams.

  2. Weaning infants with respiratory syncytial virus from mechanical ventilation through a fuzzy-logic controller.

    PubMed

    Olliver, S; Davis, G M; Hatzakis, G E

    2003-01-01

    We have previously developed a fuzzy logic controller for weaning adults with chronic obstructive pulmonary disease using pressure support ventilation (PSV). We used the core of our fuzzy logic-based weaning platform and further developed parametrizable components for weaning newborns of differing body size and disease-state. The controller was validated on neonates recovering from congenital heart disease (CHD) while receiving synchronous intermittent mandatory ventilation (SIMV). We wished to compare the efficacy of this controller versus the bedside weaning protocol in children with respiratory syncytial virus pneumonitis/bronchiolitis (RSV) in the pediatric intensive care unit (PICU). The fuzzy controller evaluated the "current" and "trend" weaning status of the newborn to quantitatively determine the change in the SIMV integrated ventilatory setting. For the "current" status it used heart rate (HR), respiratory rate (RR), tidal volume (VT) and oxygen saturation (SaO2), while for the "trend" status the differences of deltaRR/ deltat, deltaHR/ deltat, and deltaSaO2/ deltat recorded between two subsequent time points were utilized. The enumerated vital signs were fuzzified and then probability levels of occurrence were assigned. Individualized "golden" goals for SaO2 were set for each newborn. We retrospectively assessed the charts of 19 newborns, 113+/-128 days old, 5,546+/-2,321 gr body weight, weaning for 99+/-46 days, at 2-hour intervals. The SIMV levels proposed by the fuzzy controller were matched to those levels actually applied. In 60% of the time both values coincided. For the remaining 40%, the controller was more aggressive suggesting lower values of SIMV than the applied ones. The Area under the SIMV curves over time was 1,969+/-1,044 for the applied vs 1,886+/-978 for the suggested levels, respectively. The fuzzy controller adjusted for body size and disease-pattern can approximate the actual weaning course of newborns with RSV.

  3. Determination of water quality index by fuzzy logic approach: a case of ground water in an Indian town.

    PubMed

    Jinturkar, A M; Deshmukh, S S; Agarkar, S V; Chavhan, G R

    2010-01-01

    The paper proposes fuzzy logic model that deals with the physico-chemical water analysis of ground water of Chikhli town for determination of Water Quality Index (WQI). The study was carried by collection of ground water samples from about eleven hand pumps located in this town. Ground water quality is studied by systematic collection and analysis of samples. The fuzzy logic is used for the deciding the water quality index on the basis of which, water quality rankings are given to determine the quality of water. The Water Quality Index presented here is a unitless number ranging from 1 to 10. A higher number is indicative of better water quality. Around 81% of samples were found suitable for drinking purpose. It is also observed that all the parameters fall within the permissible limits laid by WHO, ISI, and ICMR, except Total Hardness, Calcium and Magnesium. The quality parameters were compared with standards laid by the World Health Organization (WHO), Indian Standards Institute (ISI) and Indian Council of Medical Research (ICMR) for drinking water quality.

  4. A feasibility investigation for modeling and optimization of temperature in bone drilling using fuzzy logic and Taguchi optimization methodology.

    PubMed

    Pandey, Rupesh Kumar; Panda, Sudhansu Sekhar

    2014-11-01

    Drilling of bone is a common procedure in orthopedic surgery to produce hole for screw insertion to fixate the fracture devices and implants. The increase in temperature during such a procedure increases the chances of thermal invasion of bone which can cause thermal osteonecrosis resulting in the increase of healing time or reduction in the stability and strength of the fixation. Therefore, drilling of bone with minimum temperature is a major challenge for orthopedic fracture treatment. This investigation discusses the use of fuzzy logic and Taguchi methodology for predicting and minimizing the temperature produced during bone drilling. The drilling experiments have been conducted on bovine bone using Taguchi's L25 experimental design. A fuzzy model is developed for predicting the temperature during orthopedic drilling as a function of the drilling process parameters (point angle, helix angle, feed rate and cutting speed). Optimum bone drilling process parameters for minimizing the temperature are determined using Taguchi method. The effect of individual cutting parameters on the temperature produced is evaluated using analysis of variance. The fuzzy model using triangular and trapezoidal membership predicts the temperature within a maximum error of ±7%. Taguchi analysis of the obtained results determined the optimal drilling conditions for minimizing the temperature as A3B5C1.The developed system will simplify the tedious task of modeling and determination of the optimal process parameters to minimize the bone drilling temperature. It will reduce the risk of thermal osteonecrosis and can be very effective for the online condition monitoring of the process.

  5. Determination analaysis of the power losses of transformers with continuously transpored conductors (CTC) based fuzzy logic

    NASA Astrophysics Data System (ADS)

    Kaloko, Bambang Sri; Atsari, Erinna Dyah

    2017-03-01

    Electric motive force which flows into the iron core continuously on a plate - plate iron isolated may cause heat posed by current eddy (eddy current). No water loss occurs due to detainees on the circuit at the the flow of current load because this loss happened on the entanglement of the transformer is made of copper. Continuously Transposed Conductors (CTC) consist of a number of enameled rectangular wires (5-84 strands) made into an assembly. Each strand is transposed in turn to each position in the cable and is then covered with layers of insulation paper. Continuously Transposed Conductors are used in winding wires for medium and ultra high power transformers. CTC is manufactured by OFHC copper and indeed, is able to supply polyester roped. CTC which has been designed to reduce production cost, oil pocket and improve cooling efficiency. Hardened type CTC (CPR1, CPR2, and CPR3: BS1432) and Self-bonding CTC which can be used to improve mechanical and electrical strength are also available. This analysis is performed using the methods of fuzzy logic in taking account of the resources.

  6. Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic.

    PubMed

    Keramitsoglou, Iphigenia; Kiranoudis, Chris T; Maiheu, Bino; De Ridder, Koen; Daglis, Ioannis A; Manunta, Paolo; Paganini, Marc

    2013-10-01

    The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.

  7. Assessment of ultrafine particles and noise measurements using fuzzy logic and data mining techniques.

    PubMed

    Fernández-Camacho, R; Brito Cabeza, I; Aroba, J; Gómez-Bravo, F; Rodríguez, S; de la Rosa, J

    2015-04-15

    This study focuses on correlations between total number concentrations, road traffic emissions and noise levels in an urban area in the southwest of Spain during the winter and summer of 2009. The high temporal correlation between sound pressure levels, traffic intensity, particle number concentrations related to traffic, black carbon and NOx concentrations suggests that noise is linked to traffic emissions as a main source of pollution in urban areas. First, the association of these different variables was studied using PreFuRGe, a computational tool based on data mining and fuzzy logic. The results showed a clear association between noise levels and road-traffic intensity for non-extremely high wind speed levels. This behaviour points, therefore, to vehicular emissions being the main source of urban noise. An analysis for estimating the total number concentration from noise levels is also proposed in the study. The high linearity observed between particle number concentrations linked to traffic and noise levels with road traffic intensity can be used to calculate traffic related particle number concentrations experimentally. At low wind speeds, there are increases in noise levels of 1 dB for every 100 vehicles in circulation. This is equivalent to 2000 cm(-3) per vehicle in winter and 500 cm(-3) in summer. At high wind speeds, wind speed could be taken into account. This methodology allows low cost sensors to be used as a proxy for total number concentration monitoring in urban air quality networks.

  8. Robust Fault Detection for Aircraft Using Mixed Structured Singular Value Theory and Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Collins, Emmanuel G.

    2000-01-01

    The purpose of fault detection is to identify when a fault or failure has occurred in a system such as an aircraft or expendable launch vehicle. The faults may occur in sensors, actuators, structural components, etc. One of the primary approaches to model-based fault detection relies on analytical redundancy. That is the output of a computer-based model (actually a state estimator) is compared with the sensor measurements of the actual system to determine when a fault has occurred. Unfortunately, the state estimator is based on an idealized mathematical description of the underlying plant that is never totally accurate. As a result of these modeling errors, false alarms can occur. This research uses mixed structured singular value theory, a relatively recent and powerful robustness analysis tool, to develop robust estimators and demonstrates the use of these estimators in fault detection. To allow qualitative human experience to be effectively incorporated into the detection process fuzzy logic is used to predict the seriousness of the fault that has occurred.

  9. Robust Fault Detection for Aircraft Using Mixed Structured Singular Value Theory and Fuzzy Logic

    NASA Technical Reports Server (NTRS)

    Collins, Emmanuel G.

    2000-01-01

    The purpose of fault detection is to identify when a fault or failure has occurred in a system such as an aircraft or expendable launch vehicle. The faults may occur in sensors, actuators, structural components, etc. One of the primary approaches to model-based fault detection relies on analytical redundancy. That is the output of a computer-based model (actually a state estimator) is compared with the sensor measurements of the actual system to determine when a fault has occurred. Unfortunately, the state estimator is based on an idealized mathematical description of the underlying plant that is never totally accurate. As a result of these modeling errors, false alarms can occur. This research uses mixed structured singular value theory, a relatively recent and powerful robustness analysis tool, to develop robust estimators and demonstrates the use of these estimators in fault detection. To allow qualitative human experience to be effectively incorporated into the detection process fuzzy logic is used to predict the seriousness of the fault that has occurred.

  10. A new methodology of extraction, optimization and application of crisp and fuzzy logical rules.

    PubMed

    Duch, W; Adamczak, R; Grabczewski, K

    2001-01-01

    A new methodology of extraction, optimization, and application of sets of logical rules is described. Neural networks are used for initial rule extraction, local or global minimization procedures for optimization, and Gaussian uncertainties of measurements are assumed during application of logical rules. Algorithms for extraction of logical rules from data with real-valued features require determination of linguistic variables or membership functions. Contest-dependent membership functions for crisp and fuzzy linguistic variables are introduced and methods of their determination described. Several neural and machine learning methods of logical rule extraction generating initial rules are described, based on constrained multilayer perceptron, networks with localized transfer functions or on separability criteria for determination of linguistic variables. A tradeoff between accuracy/simplicity is explored at the rule extraction stage and between rejection/error level at the optimization stage. Gaussian uncertainties of measurements are assumed during application of crisp logical rules, leading to "soft trapezoidal" membership functions and allowing to optimize the linguistic variables using gradient procedures. Numerous applications of this methodology to benchmark and real-life problems are reported and very simple crisp logical rules for many datasets provided.

  11. Assessment of safety and health in the tea industry of Barak valley, Assam: a fuzzy logic approach.

    PubMed

    Gupta, Rajat; Dey, Sanjoy Kumar

    2013-01-01

    Traditional safety and health system measurement procedures, practiced in various industries produce qualitative results with a degree of uncertainty. This paper presents a fuzzy-logic-based approach to developing a fuzzy model for assessing the safety and health status in the tea industry. For this, the overall safety and health status at a tea estate has been considered as a function of 4 inputs: occupational safety, occupational health, behavioral safety and competency. A set of fuzzy rules based on expert human judgment has been used to correlate different fuzzy inputs and output. Fuzzy set operations are used to calculate the safety and health status of the tea industry. Application of the developed model at a tea estate showed that the safety and health status belongs to the fuzzy class of good with a crisp value of 7.2.

  12. On the stability of interval type-2 TSK fuzzy logic control systems.

    PubMed

    Biglarbegian, Mohammad; Melek, William W; Mendel, Jerry M

    2010-06-01

    Type-2 fuzzy logic systems have recently been utilized in many control processes due to their ability to model uncertainties. This paper proposes a novel inference mechanism for an interval type-2 Takagi-Sugeno-Kang fuzzy logic control system (IT2 TSK FLCS) when antecedents are type-2 fuzzy sets and consequents are crisp numbers (A2-C0). The proposed inference mechanism has a closed form which makes it more feasible to analyze the stability of this FLCS. This paper focuses on control applications for the following cases: 1) Both plant and controller use A2-C0 TSK models, and 2) the plant uses type-1 Takagi-Sugeno (TS) and the controller uses IT2 TS models. In both cases, sufficient stability conditions for the stability of the closed-loop system are derived. Furthermore, novel linear-matrix-inequality-based algorithms are developed for satisfying the stability conditions. Numerical analyses are included which validate the effectiveness of the new inference methods. Case studies reveal that an IT2 TS FLCS using the proposed inference engine clearly outperforms its type-1 TSK counterpart. Moreover, due to the simple nature of the proposed inference engine, it is easy to implement in real-time control systems. The methods presented in this paper lay the mathematical foundations for analyzing the stability and facilitating the design of stabilizing controllers of IT2 TSK FLCSs and IT2 TS FLCSs with significantly improved performance over type-1 approaches.

  13. Design of stability-guaranteed fuzzy logic controller for nuclear steam generators

    SciTech Connect

    Cho, B.H.; No, H.C.

    1996-04-01

    A fuzzy logic controller (FLC) and a fuzzy logic filter (FLF), which have a special type of fuzzifier, inference engine, and defuzzifier, are applied to the water level control of a nuclear steam generator (S/G). It is shown that arbitrary two-input, single-output linear controllers can be adequately expressed by this FLC. A procedure to construct stability-guaranteed FLC rules is proposed. It contains the following steps: (1) the stable sector of linear feedback gains is obtained from the suboptimal concept based on LQR theory and the Lyapunov`s stability criteria; (2) the stable sector of linear gains is mapped into two linear rule tables that are used as limits for the FLC rules; and (3) the construction of an FLC rule table is done by choosing certain rules that lie between these limits. This type of FLC guarantees asymptotic stability of the control system. The FLF generates a feedforward signal of S/G feedwater from the steam flow measurement using a fuzzy concept. Through computer simulation, it is found that the FLC with the FLF works better than a well-tuned PID controller with variable gains to reduce swell/shrink phenomena, especially for the water level deviation and abrupt steam flow disturbances that are typical in the existing power plants.

  14. Fuzzy logic model to describe anesthetic effect and muscular influence on EEG Cerebral State Index.

    PubMed

    Brás, S; Gouveia, S; Ribeiro, L; Ferreira, D A; Antunes, L; Nunes, C S

    2013-06-01

    The well-known Cerebral State Index (CSI) quantifies depth of anesthesia and is traditionally modeled with Hill equation and propofol effect-site concentration (Ce). This work brings out two novelties: introduction of electromyogram (EMG) and use of fuzzy logic models with ANFIS optimized parameters. The data were collected from dogs (n=27) during routine surgery considering two propofol administration protocols: constant infusion (G1, n=14) and bolus (G2, n=13). The median modeling error of the fuzzy logic model with Ce and EMG was lower or similar than that of the Hill with Ce (p=0.012-G1, p=0.522-G2). Furthermore, there was no significant performance impact due to model structure alteration (p=0.288-G1, p=0.330-G2) and EMG introduction increased or maintained the performance (p=0.036-G1, p=0.798-G2). Therefore, the new model can achieve higher performance than Hill model, mostly due to EMG information and not due to changes in the model structure. In conclusion, the fuzzy models adequately describe CSI data with advantages over traditional Hill models. Copyright © 2012 Elsevier Ltd. All rights reserved.

  15. Fuzzy-logic modeling of land suitability for hybrid poplar across the Prairie Provinces of Canada.

    PubMed

    Joss, B N; Hall, R J; Sidders, D M; Keddy, T J

    2008-06-01

    Determining the feasibility of a large-scale afforestation program is one approach being investigated by the Government of Canada to increase Canada's potential to sequester carbon from the atmosphere. Large-scale afforestation, however, requires knowledge of where it is suitable to establish and grow trees. Spatial models based on Boolean logic and/or statistical models within a geographic information system may be used for this purpose, but empirical environmental data are often lacking, and the association of these data to land suitability is most often a subjective process. As a solution to this problem, this paper presents a fuzzy-logic modeling approach to assess land suitability for afforestation of hybrid poplar (Populus spp.) over the Prairie Provinces of Canada. Expert knowledge regarding the selection and magnitudes of environmental variables were integrated into fuzzy rule sets from which estimates of land suitability were generated and presented in map form. The environmental variables selected included growing season precipitation, climate moisture index, growing degree days, and Canada Land Inventory capability for agriculture and elevation. Approximately 150,000 km2, or 28% of the eligible land base within the Prairie Provinces was found to be suitable for afforestation. Accuracy assessments conducted with fuzzy accuracy methods provided a more descriptive assessment of the resulting land suitability map than figures generated from a more conventional Boolean-based accuracy measure. Modeling, mapping and accuracy assessment issues were identified for future extension of this work to map hybrid poplar land suitability over Canada.

  16. Knowledge-based control and case-based diagnosis based upon empirical knowledge and fuzzy logic for the SBR plant.

    PubMed

    Bae, H; Seo, H Y; Kim, S; Kim, Y

    2006-01-01

    Because biological wastewater treatment plants (WWTPs) involve a long time-delay and various disturbances, in general, skilled operators manually control the plant based on empirical knowledge. And operators usually diagnose the plant using similar cases experienced in the past. For the effective management of the plant, system automation has to be accomplished based upon operating recipes. This paper introduces automatic control and diagnosis based upon the operator's knowledge. Fuzzy logic was employed to design this knowledge-based controller because fuzzy logic can convert the linguistic information to rules. The controller can manage the influent and external carbon in considering the loading rate. The input of the controller is not the loading rate but the dissolved oxygen (DO) lag-time, which has a strong relation to the loading rate. This approach can replace an expensive sensor, which measures the loading rate and ammonia concentration in the reactor, with a cheaper DO sensor. The proposed controller can assure optimal operation and prevent the over-feeding problem. Case-based diagnosis was achieved by the analysis of profile patterns collected from the past. A new test profile was diagnosed by comparing it with template patterns containing normal and abnormal cases. The proposed control and diagnostic system will guarantee the effective and stable operation of WWTPs.

  17. Application of fuzzy logic to feature extraction from images of agricultural material

    NASA Astrophysics Data System (ADS)

    Thompson, Bruce T.

    1999-11-01

    Imaging technology has extended itself from performing gauging on machined parts, to verifying labeling on consumer products, to quality inspection of a variety of man-made and natural materials. Much of this has been made possible by faster computers and algorithms used to extract useful information from the image. In the application of agricultural material, specifically tobacco leaves, the tremendous amount of natural variability in color and texture creates new challenges to image feature extraction. As with many imaging applications, the problem can be expressed as `I see it in the image, how can I get the computer to recognize it?' In this application, the goal is to measure the amount of thick stem pieces in an image of tobacco leaves. By backlighting the leaf, the stems appear dark on a lighter background. The difference in lightness of leaf versus darkness of stem is dependent on the orientation of the leaf and the amount of folding. Because of this, any image thresholding approach must be adaptive. Another factor that allows us to identify the stem from the leaf is shape. The stem is long and narrow, while dark folded leaf is larger and more oblate. These criteria under the image collection limitations create a good application for fuzzy logic. Several generalized classification algorithms, such as fuzzy c-means and fuzzy learning vector quantization, are evaluated and compared. In addition, fuzzy thresholding based on image shape and compactness are applied to this application.

  18. Force control of a tri-layer conducting polymer actuator using optimized fuzzy logic control

    NASA Astrophysics Data System (ADS)

    Itik, Mehmet; Sabetghadam, Mohammadreza; Alici, Gursel

    2014-12-01

    Conducting polymers actuators (CPAs) are potential candidates for replacing conventional actuators in various fields, such as robotics and biomedical engineering, due to their advantageous properties, which includes their low cost, light weight, low actuation voltage and biocompatibility. As these actuators are very suitable for use in micro-nano manipulation and in injection devices in which the magnitude of the force applied to the target is of crucial importance, the force generated by CPAs needs to be accurately controlled. In this paper, a fuzzy logic (FL) controller with a Mamdani inference system is designed to control the blocking force of a trilayer CPA with polypyrrole electrodes, which operates in air. The particle swarm optimization (PSO) method is employed to optimize the controller’s membership function parameters and therefore enhance the performance of the FL controller. An adaptive neuro-fuzzy inference system model, which can capture the nonlinear dynamics of the actuator, is utilized in the optimization process. The optimized Mamdani FL controller is then implemented on the CPA experimentally, and its performance is compared with a non-optimized fuzzy controller as well as with those obtained from a conventional PID controller. The results presented indicate that the blocking force at the tip of the CPA can be effectively controlled by the optimized FL controller, which shows excellent transient and steady state characteristics but increases the control voltage compared to the non-optimized fuzzy controllers.

  19. Groundwater vulnerability assessment using fuzzy logic: a case study in the Zayandehrood aquifers, Iran.

    PubMed

    Rezaei, Farshad; Safavi, Hamid R; Ahmadi, Azadeh

    2013-01-01

    Groundwater is an important source of water, especially in arid and semi-arid regions where surface water is scarce. Groundwater pollution in these regions is consequently a major concern, especially as pollution control and removal in these resources are not only expensive but at times impossible. It is, therefore, essential to prevent their contamination in the first place by properly identifying vulnerable zones. One method most commonly used for evaluating groundwater pollution is the DRASTIC method, in which the Boolean logic is used to rank and classify the parameters involved. Problems arise, however, in the application of the Boolean logic. In this paper, the fuzzy logic has been used to avoid the problems. For this purpose, three critical cases of minimum, maximum, and mean values have been considered for the net recharge parameter. The process has been performed on the Zayandehrood river basin aquifers. The fuzzy-DRASTIC vulnerability map thus obtained indicates that the western areas of the basin generally have the maximum pollution potential followed by the areas located in the east. The central parts of the study area are found to have a low pollution potential. Finally, two sensitivity analyses are performed to show the significance of each value of the net recharge parameter in the calculation of vulnerability index.

  20. Fuzzy Logic Based Anomaly Detection for Embedded Network Security Cyber Sensor

    SciTech Connect

    Ondrej Linda; Todd Vollmer; Jason Wright; Milos Manic

    2011-04-01

    Resiliency and security in critical infrastructure control systems in the modern world of cyber terrorism constitute a relevant concern. Developing a network security system specifically tailored to the requirements of such critical assets is of a primary importance. This paper proposes a novel learning algorithm for anomaly based network security cyber sensor together with its hardware implementation. The presented learning algorithm constructs a fuzzy logic rule based model of normal network behavior. Individual fuzzy rules are extracted directly from the stream of incoming packets using an online clustering algorithm. This learning algorithm was specifically developed to comply with the constrained computational requirements of low-cost embedded network security cyber sensors. The performance of the system was evaluated on a set of network data recorded from an experimental test-bed mimicking the environment of a critical infrastructure control system.

  1. Taguchi based fuzzy logic optimization of multiple quality characteristics in laser cutting of Duralumin sheet

    NASA Astrophysics Data System (ADS)

    Pandey, Arun Kumar; Dubey, Avanish Kumar

    2012-03-01

    Capability of laser cutting mainly depends on optical and thermal properties of work material. Highly reflective and thermally conductive Duralumin sheets are difficult-to-laser-cut. Application of Duralumin sheets in aeronautic and automotive industries due to its high strength to weight ratio demand narrow and complex cuts with high geometrical accuracy. The present paper experimentally investigates the laser cutting of Duralumin sheet with the aim to improve geometrical accuracy by simultaneously minimizing the kerf width and kerf deviations at top and bottom sides. A hybrid approach, obtained by combining robust parameter design methodology and Fuzzy logic theory has been applied to compute the fuzzy multi-response performance index. This performance index is further used for multi-objective optimization. The predicted optimum results have been verified by performing the confirmation tests. The confirmation tests show considerable reduction in kerf deviations at top and bottom sides.

  2. GIS modeling using fuzzy logic approach in mineral prospecting based on geophysical data

    NASA Astrophysics Data System (ADS)

    Setyadi, Harman; Widodo, Lilik Eko; Notosiswoyo, Sudarto; Saptawati, Putri; Ismanto, Arief; Hardjana, Iip

    2016-02-01

    The geophysical exploration method is the superior over the project area due to the dense of vegetation and thick soil so very limited geological outcrops. Contrast of physical properties of every different rock type should be able to be distinguished by the geophysical data. Fuzzy logic approach and weight of evidence were used for geophysical data modeling. Posterior probability was used to calculate the weight of evidence (WofE) of every fuzzy map memberships. By combining each rock type model, the model provides better result compared from the model from mixed rock type on the data training. This method is able to eliminate the potential interference of different geophysical signature. So that, the understanding the geological feature of the area is key success for the mineral prosperity modeling. We verified the model by site visiting and drilling and it is estimated about 90% confident.

  3. Fuzzy logic control of steam generator water level in pressurized water reactors

    SciTech Connect

    Kuan, C.C.; Lin, C.; Hsu, C.C. . Dept. of Nuclear Engineering)

    1992-10-01

    In this paper a fuzzy logic controller is applied to control the steam generator water level in a pressurized water reactor. The method does not require a detailed mathematical mode of the object to be controlled. The design is based on a set of linguistic rules that were adopted from the human operator's experience. After off-line fuzzy computation, the controller is a lookup table, and thus, real-time control is achieved. Shrink-and-swell phenomena are considered in the linguistic rules, and the simulation results show that their effect is dramatically reduced. The performance of the control system can also be improved by changing the input and output scaling factors, which is convenient for on-line tuning.

  4. A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.

    PubMed

    Borges, R M; Mattedi, A; Munaro, C J; Franci Gonçalves, R

    A modular diagnosis system (MDS), based on the framework of fuzzy logic, is proposed for upflow anaerobic sludge blanket (UASB) reactors treating sewage. In module 1, turbidity and rainfall information are used to estimate the influent organic content. In module 2, a dynamic fuzzy model is used to estimate the current biogas production from on-line measured variables, such as daily average temperature and the previous biogas flow rate, as well as the organic load. Finally, in module 3, all the information above and the residual value between the measured and estimated biogas production are used to provide diagnostic information about the operation status of the plant. The MDS was validated through its application to two pilot UASB reactors and the results showed that the tool can provide useful diagnoses to avoid plant failures.

  5. Chaotic queue-based genetic algorithm for design of a self-tuning fuzzy logic controller

    NASA Astrophysics Data System (ADS)

    Saini, Sanju; Saini, J. S.

    2012-11-01

    This paper employs a chaotic queue-based method using logistic equation in a non-canonical genetic algorithm for optimizing the performance of a self-tuning Fuzzy Logic Controller, used for controlling a nonlinear double-coupled system. A comparison has been made with a standard canonical genetic algorithm implemented on the same plant. It has been shown that chaotic queue-method brings an improvement in the performance of the FLC for wide range of set point changes by a more profound initial population spread in the search space.

  6. Fuzzy Logic Based Controller for a Grid-Connected Solid Oxide Fuel Cell Power Plant.

    PubMed

    Chatterjee, Kalyan; Shankar, Ravi; Kumar, Amit

    2014-10-01

    This paper describes a mathematical model of a solid oxide fuel cell (SOFC) power plant integrated in a multimachine power system. The utilization factor of a fuel stack maintains steady state by tuning the fuel valve in the fuel processor at a rate proportional to a current drawn from the fuel stack. A suitable fuzzy logic control is used for the overall system, its objective being controlling the current drawn by the power conditioning unit and meet a desirable output power demand. The proposed control scheme is verified through computer simulations.

  7. Study of defuzzification methods of fuzzy logic controller for speed control of a DC motor

    SciTech Connect

    Rao, D.H.; Saraf, S.S.

    1995-12-31

    A typical Fuzzy Logic Controller (FLC) has the following components: fuzzification, knowledge base, decision making and defuzzification. Various defuzzification techniques have been proposed in the literature. The efficacy of a FLC depends very much on the defuzzification process. This is so because the overall performance of the system under control is determined by the controlling signal (the defuzzified output of the FLC) the system receives. The aim of this paper is to evaluate qualitatively the performance of the different defuzzification techniques as applied to speed control of a DC motor.

  8. Type-2 fuzzy logic control of a 2-DOF helicopter (TRMS system)

    NASA Astrophysics Data System (ADS)

    Zeghlache, Samir; Kara, Kamel; Saigaa, Djamel

    2014-09-01

    The helicopter dynamic includes nonlinearities, parametric uncertainties and is subject to unknown external disturbances. Such complicated dynamics involve designing sophisticated control algorithms that can deal with these difficulties. In this paper, a type 2 fuzzy logic PID controller is proposed for TRMS (twin rotor mimo system) control problem. Using triangular membership functions and based on a human operator experience, two controllers are designed to control the position of the yaw and the pitch angles of the TRMS. Simulation results are given to illustrate the effectiveness of the proposed control scheme.

  9. Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block.

    PubMed

    Shieh, J S; Fan, S Z; Chang, L W; Liu, C C

    2000-01-01

    The important task for anaesthetists is to provide an adequate degree of neuromuscular block during surgical operations, so that it should not be difficult to antagonize at the end of surgery. Therefore, this study examined the application of a simple technique (i.e., fuzzy logic) to an almost ideal muscle relaxant (i.e., rocuronium) at general anaesthesia in order to control the system more easily, efficiently, intelligently and safely during an operation. The characteristics of neuromuscular blockade induced by rocuronium were studied in 10 ASA I or II adult patients anaesthetized with inhalational (i.e., isoflurane) anaesthesia. A Datex Relaxograph was used to monitor neuromuscular block. And, ulnar nerve was stimulated supramaximally with repeated train-of-four via surface electrodes at the wrist. Initially a notebook personal computer was linked to a Datex Relaxograph to monitor electromyogram (EMG) signals which had been pruned by a three-level hierarchical structure of filters in order to design a controller for administering muscle relaxants. Furthermore, a four-level hierarchical fuzzy logic controller using the fuzzy logic and rule of thumb concept has been incorporated into the system. The Student's test was used to compare the variance between the groups. p < 0.05 was considered significant. The system achieved stable control of muscle relaxation with a mean T1% error of -0.19 (SD 0.66) % accommodating a range in mean infusion rate (MIR) of 0.21-0.49 mg x kg(-1) x h(-1). When these results were compared with our previous ones using the same hierarchical structure applied to mivacurium, less variation in the T1% error (p < 0.05) but the same variation in infusion rate were observed. The controller activity of these two drugs showed no significant difference (p > 0.5). However, the consistent medium coefficient variance (CV) of the MIR of both rocuronium (i.e., 36.13 (SD 9.35) %) and mivacurium (i.e., 34.03 (SD 10.76) %) indicated a good controller

  10. Fuzzy logic enhanced speed control of an indirect field-oriented induction machine drive

    SciTech Connect

    Heber, B.; Xu, L.; Tang, Y.

    1997-09-01

    Field orientation control (FOC) of induction machines has permitted fast transient response by decoupled torque and flux control. However, field orientation detuning caused by parameter variations is a major difficulty for indirect FOC methods. Traditional probability density function (PID) controllers have trouble meeting a wide range of speed tracking performance even when proper field orientation is achieved. PID controller performance is severely degraded when detuning occurs. This paper presents a fuzzy logic design approach that can meet the speed tracking requirements even when detuning occurs. Computer simulations and experimental results obtained via a general-purpose digital signal processor (DSP) system are presented.

  11. Intelligent control of a multi-degree-of freedom reaction compensating platform system using fuzzy logic

    NASA Technical Reports Server (NTRS)

    Choi, Benjamin B.; Lawrence, Charles; Lin, Yueh-Jaw

    1994-01-01

    This paper presents the development of a general-purpose fuzzy logic (FL) control methodology for isolating the external vibratory disturbances of space-based devices. According to the desired performance specifications, a full investigation regarding the development of an FL controller was done using different scenarios, such as variances of passive reaction-compensating components and external disturbance load. It was shown that the proposed FL controller is robust in that the FL-controlled system closely follows the prespecified ideal reference model. The comparative study also reveals that the FL-controlled system achieves significant improvement in reducing vibrations over passive systems.

  12. Large motion tracking control for thrust magnetic bearings with fuzzy logic, sliding mode, and direct linearization

    NASA Astrophysics Data System (ADS)

    Minihan, T. P.; Lei, S.; Sun, G.; Palazzolo, A.; Kascak, A. F.; Calvert, T.

    2003-06-01

    Conventional use of magnetic bearings relies on a zero reference to keep the rotor centered in the radial and axial axes. This paper compares different control methods developed for the alternate control task of tracking an axial dynamic target. Controllers based on fuzzy logic, sliding mode, and direct linearization were designed to meet this task. Performance criteria, such as maximum axial displacement, minimum phase lag and I2R power losses were compared for each controller. The large motion, tracking problem for a rotor has utility in applications where dynamic seal clearances are required. This has a variety of potential applications in turbo-machinery, such as active stall control.

  13. Fractional order integration and fuzzy logic based filter for denoising of echocardiographic image.

    PubMed

    Saadia, Ayesha; Rashdi, Adnan

    2016-12-01

    Ultrasound is widely used for imaging due to its cost effectiveness and safety feature. However, ultrasound images are inherently corrupted with speckle noise which severely affects the quality of these images and create difficulty for physicians in diagnosis. To get maximum benefit from ultrasound imaging, image denoising is an essential requirement. To perform image denoising, a two stage methodology using fuzzy weighted mean and fractional integration filter has been proposed in this research work. In stage-1, image pixels are processed by applying a 3 × 3 window around each pixel and fuzzy logic is used to assign weights to the pixels in each window, replacing central pixel of the window with weighted mean of all neighboring pixels present in the same window. Noise suppression is achieved by assigning weights to the pixels while preserving edges and other important features of an image. In stage-2, the resultant image is further improved by fractional order integration filter. Effectiveness of the proposed methodology has been analyzed for standard test images artificially corrupted with speckle noise and real ultrasound B-mode images. Results of the proposed technique have been compared with different state-of-the-art techniques including Lsmv, Wiener, Geometric filter, Bilateral, Non-local means, Wavelet, Perona et al., Total variation (TV), Global Adaptive Fractional Integral Algorithm (GAFIA) and Improved Fractional Order Differential (IFD) model. Comparison has been done on quantitative and qualitative basis. For quantitative analysis different metrics like Peak Signal to Noise Ratio (PSNR), Speckle Suppression Index (SSI), Structural Similarity (SSIM), Edge Preservation Index (β) and Correlation Coefficient (ρ) have been used. Simulations have been done using Matlab. Simulation results of artificially corrupted standard test images and two real Echocardiographic images reveal that the proposed method outperforms existing image denoising techniques

  14. Development of a fuzzy logic based intelligent system for autonomous guidance of post-stroke rehabilitation exercise.

    PubMed

    Huq, Rajibul; Wang, Rosalie; Lu, Elaine; Hebert, Debbie; Lacheray, Hervé; Mihailidis, Alex

    2013-06-01

    This paper presents preliminary studies in developing a fuzzy logic based intelligent system for autonomous post-stroke upper-limb rehabilitation exercise. The intelligent system autonomously varies control parameters to generate different haptic effects on the robotic device. The robotic device is able to apply both resistive and assistive forces for guiding the patient during the exercise. The fuzzy logic based decision-making system estimates muscle fatigue of the patient using exercise performance and generates a combination of resistive and assistive forces so that the stroke survivor can exercise for longer durations with increasing control. The fuzzy logic based system is initially developed using a study with healthy subjects and preliminary results are also presented to validate the developed system with healthy subjects. The next stage of this work will collect data from stroke survivors for further development of the system.

  15. Fuzzy Logic Controller Based on Observed Signals and a Genetic Algorithm Application with STATCOM for Power System Stabilization

    NASA Astrophysics Data System (ADS)

    Hongesombut, Komsan; Mitani, Yasunori; Tsuji, Kiichiro

    Fuzzy logic control has been applied to various applications in power systems. Its control rules and membership functions are typically obtained by trial and error methods or experience knowledge. Proposed here is the application of a micro-genetic algorithm (micro-GA) to simultaneously design optimal membership functions and control rules for STATCOM. First, we propose a simple approach to extract membership functions and fuzzy logic control rules based on observed signals. Then a proposed GA will be applied to optimize membership functions and its control rules. To validate the effectiveness of the proposed approach, several simulation studies have been performed on a multimachine power system. Simulation results show that the proposed fuzzy logic controller with STATCOM can effectively and robustly enhance the damping of oscillations.

  16. Processing Binary and Fuzzy Logic by Chaotic Time Series Generated by a Hydrodynamic Photochemical Oscillator.

    PubMed

    Gentili, Pier Luigi; Giubila, Maria Sole; Heron, B Mark

    2017-07-05

    This work demonstrates the computational power of a hydrodynamic photochemical oscillator based on a photochromic naphthopyran generating aperiodic time series. The chaotic character of the time series is tested by calculating its largest Lyapunov exponent and the correlation dimension of its attractor after building its phase space through the Takens' theorem. Then, the chaotic dynamic is shown to be suitable to implement all the fundamental Boolean two-inputs-one-output logic gates. Finally, the strategy to implement fuzzy logic systems (FLSs) based on the time series is described. Such FLSs promise to be useful in the field of computational linguistics, which is concerned with the development of artificial intelligent systems able to transform collections of numerical data into natural language texts. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  17. A new robust control scheme using second order sliding mode and fuzzy logic of a DFIM supplied by two five-level SVPWM inverters

    NASA Astrophysics Data System (ADS)

    Boudjema, Zinelaabidine; Taleb, Rachid; Bounadja, Elhadj

    2017-02-01

    Traditional filed oriented control strategy including proportional-integral (PI) regulator for the speed drive of the doubly fed induction motor (DFIM) have some drawbacks such as parameter tuning complications, mediocre dynamic performances and reduced robustness. Therefore, based on the analysis of the mathematical model of a DFIM supplied by two five-level SVPWM inverters, this paper proposes a new robust control scheme based on super twisting sliding mode and fuzzy logic. The conventional sliding mode control (SMC) has vast chattering effect on the electromagnetic torque developed by the DFIM. In order to resolve this problem, a second order sliding mode technique based on super twisting algorithm and fuzzy logic functions is employed. The validity of the employed approach was tested by using Matlab/Simulink software. Interesting simulation results were obtained and remarkable advantages of the proposed control scheme were exposed including simple design of the control system, reduced chattering as well as the other advantages.

  18. Environmental impact assessment by means of a procedure based on fuzzy logic: A practical application

    SciTech Connect

    Peche, Roberto Rodriguez, Esther

    2011-03-15

    This study shows the practical application of the EIA method based on fuzzy logic proposed by the authors (Peche and Rodriguez, 2009) to a simplified case of study-the activity of a petrol station throughout its exploitation. The intensity (p{sub 1}), the extent (p{sub 2}) and the persistence (p{sub 3}) were the properties selected to describe the impacts and their respective assessment functions v-bar{sub i}=f(p-bar{sub i}) were determined. The main actions (A) and potentially affected environmental factors (F) were selected. Every impact was identified by a pair A-F and the values of the three impact properties were estimated for each of them by means of triangular fuzzy numbers. Subsequently, the fuzzy estimation of every impact was carried out, the estimation of the impact A{sub 1}-F{sub 2} (V-bar{sub 1}) being explained in detail. Every impact was simultaneously represented by its corresponding generalised confidence interval and membership function. Since the membership functions of all impacts were similar to triangular fuzzy numbers, a triangular approach (TA) was used to describe every impact. A triangular approach coefficient (TAC) was introduced to quantify the similarity of each fuzzy number and its corresponding triangular approach, where TAC (V-bar) element of (0, 1] and TAC being 1 when the fuzzy number is triangular. The TACs-ranging from 0.96 to 0.99-proved that TAs were valid in all cases. Next, the total positive and negative impacts-TV-bar{sup +} and TV-bar{sup -} were calculated and later, the fuzzy value of the total environmental impact TV-bar was determined from them. Finally, the defuzzification of TV-bar led to the punctual impact estimator TV{sup (1)} = -88.50 and its corresponding uncertainty interval [{delta}{sub l}(TV-bar),{delta}{sub r}(TV-bar)]=[6.52,6.96], which represent the total value of the EI. In conclusion, the EIA method enabled the integration of heterogeneous impacts, which exerted influence on environmental factors of a

  19. Interference Path Loss Prediction in A319/320 Airplanes Using Modulated Fuzzy Logic and Neural Networks

    NASA Technical Reports Server (NTRS)

    Jafri, Madiha J.; Ely, Jay J.; Vahala, Linda L.

    2007-01-01

    In this paper, neural network (NN) modeling is combined with fuzzy logic to estimate Interference Path Loss measurements on Airbus 319 and 320 airplanes. Interference patterns inside the aircraft are classified and predicted based on the locations of the doors, windows, aircraft structures and the communication/navigation system-of-concern. Modeled results are compared with measured data. Combining fuzzy logic and NN modeling is shown to improve estimates of measured data over estimates obtained with NN alone. A plan is proposed to enhance the modeling for better prediction of electromagnetic coupling problems inside aircraft.

  20. Energy Efficient Wireless Sensor Networks Using Fuzzy Logic

    DTIC Science & Technology

    2005-12-01

    CLASSIFICATION OF: 17. LIMITATION OF 18. NUMBER 19a. NAME OF RESPONSIBLE PERSON a. REPORT b. ABSTRACT c. THIS PAGE ABSTRACT OF Qilian Liang PAGES U U U U124...and Gaussian clutter as our first stage study. Particularly, we assume the radar when receiving, is a constant false alarm receiver (CFAR). CFAR...information. Considerring quality requirement and power constraint, we made analysis and classification on sources of imperfect information and