Coupland, Simon
2006-01-01
There has recently been a significant increase in academic interest in the field oftype-2 fuzzy sets and systems. Type-2 fuzzy systems offer the ability to model and reason with uncertain concepts. When faced with uncertainties type-2 fuzzy systems should, theoretically, give an increase in performance over type-l fuzzy systems. However, the computational complexity of generalised type-2 fuzzy systems is significantly higher than type-l systems. A direct consequence of this is that, prior to ...
Learning fuzzy logic control system
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
Fuzzy Logic and Neuro-fuzzy Systems: A Systematic Introduction
Yue Wu; Biaobiao Zhang; Jiabin Lu; K. -L. Du
2011-01-01
Fuzzy logic is a rigorous mathematical field, and it provides an effective vehicle for modeling the uncertainty in human reasoning. In fuzzy logic, the knowledge of experts is modeled by linguistic rules represented in the form of IF-THEN logic. Like neural network models such as the multilayer perceptron (MLP) and the radial basis function network (RBFN), some fuzzy inference systems (FISs) have the capability of universal approximation. Fuzzy logic can be used in most areas where neural net...
DESIGN POWER SYSTEM STABILIZER MENGGUNAKAN FUZZY LOGIC
Directory of Open Access Journals (Sweden)
Ivo Salvador Soares Miranda
2014-10-01
Full Text Available Stabiltas merupakan kemampuan sistem untuk menjaga kondisi operasi seimbang dan kembali kekondisi operasi normal ketika terjadi gangguan. Penerapan power system stabilizer pada sistem tenaga mampu memberikan sinyal respon yang cepat atas berbagai kondisi gangguan dan mengupayakan tidak meluasnya jangkauan gangguan. Dalam mendesign power system stabilizer menggunakan robust fuzzy logic, menggunakan satu sinyal input yaitu kecepatan deviasi rotor. Hasil simulasinya dibandingkan dengan metode fuzzy logic dan kovensional. Studi simulasi menunjukan, design power system stabilizer menggunakan robust fuzzy logic memiliki nilai sinyal peak time dan settling time relatif kecil dibandingkan dengan metode fuzzy logic dan konvensional.
Fuzzy Logic Based Automatic Door Control System
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Harun SUMBUL
2017-12-01
Full Text Available In this paper, fuzzy logic based an automatic door control system is designed to provide for heat energy savings. The heat energy loss usually occurs in where outomotic doors are used. Designed fuzzy logic system’s Input statuses (WS: Walking Speed and DD: Distance Door and the output status (DOS: Door Opening Speed is determined. According to these cases, rule base (25 rules is created; the rules are processed by a fuzzy logic and by appyled to control of an automatic door. An interface program is prepared by using Matlab Graphical User Interface (GUI programming language and some sample results are checked on Matlab using fuzzy logic toolbox. Designed fuzzy logic controller is tested at different speed cases and the results are plotted. As a result; in this study, we have obtained very good results in control of an automatic door with fuzzy logic. The results of analyses have indicated that the controls performed with fuzzy logic provided heat energy savings, less heat energy loss and reliable, consistent controls and that are feasible to in real.
Fuzzy Logic Based Autonomous Traffic Control System
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Muhammad ABBAS
2012-01-01
Full Text Available The aim of this paper is to design and implement fuzzy logic based traffic light Control system to solve the traffic congestion issues. In this system four input parameters: Arrival, Queue, Pedestrian and Emergency Vehicle and two output parameters: Extension in Green and Pedestrian Signals are used. Using Fuzzy Rule Base, the system extends or terminates the Green Signal according to the Traffic situation at the junction. On the presence of emergency vehicle, the system decides which signal(s should be red and how much an extension should be given to Green Signal for Emergency Vehicle. The system also monitors the density of people and makes decisions accordingly. In order to verify the proposed design algorithm MATLAB simulation is adopted and results obtained show concurrency to the calculated values according to the Mamdani Model of the Fuzzy Control System.
Fuzzy logic controller optimization
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.
Smets, P
1995-01-01
We start by describing the nature of imperfect data, and giving an overview of the various models that have been proposed. Fuzzy sets theory is shown to be an extension of classical set theory, and as such has a proeminent role or modelling imperfect data. The mathematic of fuzzy sets theory is detailled, in particular the role of the triangular norms. The use of fuzzy sets theory in fuzzy logic and possibility theory,the nature of the generalized modus ponens and of the implication operator for approximate reasoning are analysed. The use of fuzzy logic is detailled for application oriented towards process control and database problems.
Metamathematics of fuzzy logic
Hájek, Petr
1998-01-01
This book presents a systematic treatment of deductive aspects and structures of fuzzy logic understood as many valued logic sui generis. Some important systems of real-valued propositional and predicate calculus are defined and investigated. The aim is to show that fuzzy logic as a logic of imprecise (vague) propositions does have well-developed formal foundations and that most things usually named `fuzzy inference' can be naturally understood as logical deduction.
Fuzzy logic system for BBT based fertility prediction | Yazed | Journal ...
African Journals Online (AJOL)
... been obtained with the accuracy of 95 % and 80 respectively. Besides, this prediction system using fuzzy logic could improve the current practice in the FAM technique by integrating it with an Internet of Things (IoT) technology for automatic BBT charting and monitoring. Keywords: family planning; fertility; BBT; fuzzy logic.
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.
Fuzzy logic control and optimization system
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.
Advanced Concepts in Fuzzy Logic and Systems with Membership Uncertainty
Starczewski, Janusz T
2013-01-01
This book generalizes fuzzy logic systems for different types of uncertainty, including - semantic ambiguity resulting from limited perception or lack of knowledge about exact membership functions - lack of attributes or granularity arising from discretization of real data - imprecise description of membership functions - vagueness perceived as fuzzification of conditional attributes. Consequently, the membership uncertainty can be modeled by combining methods of conventional and type-2 fuzzy logic, rough set theory and possibility theory. In particular, this book provides a number of formulae for implementing the operation extended on fuzzy-valued fuzzy sets and presents some basic structures of generalized uncertain fuzzy logic systems, as well as introduces several of methods to generate fuzzy membership uncertainty. It is desirable as a reference book for under-graduates in higher education, master and doctor graduates in the courses of computer science, computational intelligence, or...
What Could Fuzzy Logic Bring to Statistical Information Systems?
Directory of Open Access Journals (Sweden)
Miroslav Hudec
2011-03-01
Full Text Available The aim of the paper is to present the applicability of the fuzzy logic for statistical information systems in order to improve work with statistical data. The improvement offers the approximate reasoning in order to solve problems in a way that more resembles human logic. The paper examines the fuzzy logic approach,emphasizes situations when the two-valued (crisp logic is not adequate and offers solutions based on fuzzy logic. The first step of using data is its selection from a database. Although the Structured Query Language (SQL is a very powerful tool, it is unable to satisfy needs for data selection based on linguistic expressions and degrees of truth. For this purpose the fuzzy generalised logicalcondition (GLC was developed. Later researches have shown that the GLC formula is suitable for other processes concerning data, namely data classification and data dissemination.
Carlsson, Christer; Fullér, Robert
2004-01-01
Fuzzy Logic in Management demonstrates that difficult problems and changes in the management environment can be more easily handled by bringing fuzzy logic into the practice of management. This explicit theme is developed through the book as follows: Chapter 1, "Management and Intelligent Support Technologies", is a short survey of management leadership and what can be gained from support technologies. Chapter 2, "Fuzzy Sets and Fuzzy Logic", provides a short introduction to fuzzy sets, fuzzy relations, the extension principle, fuzzy implications and linguistic variables. Chapter 3, "Group Decision Support Systems", deals with group decision making, and discusses methods for supporting the consensus reaching processes. Chapter 4, "Fuzzy Real Options for Strategic Planning", summarizes research where the fuzzy real options theory was implemented as a series of models. These models were thoroughly tested on a number of real life investments, and validated in 2001. Chapter 5, "Soft Computing Methods for Reducing...
Fuzzy logic for structural system control
Directory of Open Access Journals (Sweden)
Herbert Martins Gomes
Full Text Available This paper provides some information and numerical tests that aims to investigate the use of a Fuzzy Controller applied to control systems. Some advantages are reported regarding the use of this controller, such as the characteristic ease of implementation due to its semantic feature in the statement of the control rules. On the other hand, it is also hypothesized that these systems have a lower performance loss when the system to be controlled is nonlinear or has time varying parameters. Numerical tests are performed using modal LQR optimal control and Fuzzy control of non-collocated systems with full state feedback in a two-dimensional structure. The paper proposes a way of designing a controller that may be a supervisory Fuzzy controller for a traditional controller or even a fuzzy controller independent from the traditional control, consisting on individual mode controllers. Some comments are drawn regarding the performance of these proposals in a number of arrangements.
Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers
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Y. A. Al-Turki
2012-01-01
Full Text Available This paper presents a powerful supervisory power system stabilizer (PSS using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS. The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC driven by a fixed fuzzy set (FFS which has 49 rules. Both fuzzy logic controller (FLC algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study.
A fuzzy logic pitch angle controller for power system stabilization
Energy Technology Data Exchange (ETDEWEB)
Jauch, Clemens; Cronin, Tom; Sorensen, Poul [Wind Energy Department, Riso National Laboratory, PO Box 49, DK-4000 Roskilde, (Denmark); Jensen, Birgitte Bak [Institute of Energy Technology, Aalborg University, Pontoppidanstraede 101, DK-9220 Aalborg East, (Denmark)
2006-07-12
In this article the design of a fuzzy logic pitch angle controller for a fixed speed, active-stall wind turbine, which is used for power system stabilization, is presented. The system to be controlled, which is the wind turbine and the power system to which the turbine is connected, is described. The advantages of fuzzy logic control when applied to large-signal control of active-stall wind turbines are outlined. The general steps of the design process for a fuzzy logic controller, including definition of the controller inputs, set-up of the fuzzy rules and the method of defuzzification, are described. The performance of the controller is assessed by simulation, where the wind turbine's task is to dampen power system oscillations. In the scenario simulated for this work, the wind turbine has to ride through a transient short-circuit fault and subsequently contribute to the damping of the grid frequency oscillations that are caused by the transient fault. It is concluded that the fuzzy logic controller enables the wind turbine to dampen power system oscillations. It is also concluded that, owing to the inherent non-linearities in a wind turbine and the unpredictability of the whole system, the fuzzy logic controller is very suitable for this application. (Author).
Genetic Algorithm Based Design of Fuzzy Logic Power System Stabilizers in Multimachine Power System
Manisha Dubey; Aalok Dubey
2010-01-01
This paper presents an approach for the design of fuzzy logic power system stabilizers using genetic algorithms. In the proposed fuzzy expert system, speed deviation and its derivative have been selected as fuzzy inputs. In this approach the parameters of the fuzzy logic controllers have been tuned using genetic algorithm. Incorporation of GA in the design of fuzzy logic power system stabilizer will add an intelligent dimension to the stabilizer and significantly reduces ...
Fuzzy Logic Temperature Control System For The Induction Furnace
Directory of Open Access Journals (Sweden)
Lei Lei Hnin
2015-08-01
Full Text Available This research paper describes the fuzzy logic temperature control system of the induction furnace. Temperature requirement of the heating system varies during the heating process. In the conventional control schemes the switching losses increase with the change in the load. A closed loop control is required to have a smooth control on the system. In this system pulse width modulation based power control scheme for the induction heating system is developed using the fuzzy logic controller. The induction furnace requires a good voltage regulation to have efficient response. The controller controls the temperature depending upon weight of meat water and time. This control system is implemented in hardware system using microcontroller. Here the fuzzy logic controller is designed and simulated in MATLAB to get the desire condition.
Fuzzy logic applications to expert systems and control
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.
The design of thermoelectric footwear heating system via fuzzy logic.
Işik, Hakan; Saraçoğlu, Esra
2007-12-01
In this study, Heat Control of Thermoelectric Footwear System via Fuzzy Logic has been implemented in order to use efficiently in cold weather conditions. Temperature control is very important in domestic as well as in many industrial applications. The final product is seriously affected from the changes in temperature. So it is necessary to reach some desired temperature points quickly and avoid large overshoot. Here, fuzzy logic acts an important role. PIC 16F877 microcontroller has been designed to act as fuzzy logic controller. The designed system provides energy saving and has better performance than proportional control that was implemented in the previous study. The designed system takes into consideration so appropriate parameters that it can also be applied to the people safely who has illnesses like diabetes, etc.
Performance evaluation of the distance education system with fuzzy logic
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.
Malhas, Othman Qasim
1993-10-01
The concept of “abacus logic” has recently been developed by the author (Malhas, n.d.). In this paper the relation of abacus logic to the concept of fuzziness is explored. It is shown that if a certain “regularity” condition is met, concepts from fuzzy set theory arise naturally within abacus logics. In particular it is shown that every abacus logic then has a “pre-Zadeh orthocomplementation”. It is also shown that it is then possible to associate a fuzzy set with every proposition of abacus logic and that the collection of all such sets satisfies natural conditions expected in systems of fuzzy logic. Finally, the relevance to quantum mechanics is discussed.
Model Reduction of Fuzzy Logic Systems
Directory of Open Access Journals (Sweden)
Zhandong Yu
2014-01-01
Full Text Available This paper deals with the problem of ℒ2-ℒ∞ model reduction for continuous-time nonlinear uncertain systems. The approach of the construction of a reduced-order model is presented for high-order nonlinear uncertain systems described by the T-S fuzzy systems, which not only approximates the original high-order system well with an ℒ2-ℒ∞ error performance level γ but also translates it into a linear lower-dimensional system. Then, the model approximation is converted into a convex optimization problem by using a linearization procedure. Finally, a numerical example is presented to show the effectiveness of the proposed method.
Fuzzy logic based variable speed wind generation system
Energy Technology Data Exchange (ETDEWEB)
Simoes, M.G. [Sao Paulo Univ., SP (Brazil). Escola Politecnica. PMC - Mecatronica; Bose, B.K. [Tennessee Univ., Knoxville, TN (United States). Dept. of Electrical Engineering; Spiegel, Ronal J. [Environmental Protection Agency, Research Triangle Park, NC (United States). Air and Energy Engineering Research Lab.
1996-12-31
This work demonstrates the successful application of fuzzy logic to enhance the performance and control of a variable speed wind generation system. A maximum power point tracker control is performed with three fuzzy controllers, without wind velocity measurement, and robust to wind vortex and turbine torque ripple. A squirrel cage induction generator feeds the power to a double-sided PWM converter system which pumps the power to a utility grid or supplies to an autonomous system. The fuzzy logic controller FLC-1 searches on-line the generator speed so that the aerodynamic efficiency of the wind turbine is optimized. A second fuzzy controller FLC-2 programs the machine flux by on-line search so as to optimize the machine-converter system wind vortex. Detailed analysis and simulation studies were performed for development of the control strategy and fuzzy algorithms, and a DSP TMS320C30 based hardware with C control software was built for the performance evaluation of a laboratory experimental set-up. The theoretical development was fully validated and the system is ready to be reproduced in a higher power installation. (author) 7 refs., 3 figs., 1 tab.
Fuzzy Logic Applied to an Oven Temperature Control System
Directory of Open Access Journals (Sweden)
Nagabhushana KATTE
2011-10-01
Full Text Available The paper describes the methodology of design and development of fuzzy logic based oven temperature control system. As simple fuzzy logic controller (FLC structure with an efficient realization and a small rule base that can be easily implemented in existing underwater control systems is proposed. The FLC has been designed using bell-shaped membership function for fuzzification, 49 control rules in its rule base and centre of gravity technique for defuzzification. Analog interface card with 16-bits resolution is designed to achieve higher precision in temperature measurement and control. The experimental results of PID and FLC implemented system are drawn for a step input and presented in a comparative fashion. FLC exhibits fast response and it has got sharp rise time and smooth control over conventional PID controller. The paper scrupulously discusses the hardware and software (developed using ‘C’ language features of the system.
Switch Reluctance Motor Control Based on Fuzzy Logic System
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S. V. Aleksandrovsky
2012-01-01
Full Text Available Due to its intrinsic simplicity and reliability, the switched reluctance motor (SRM has now become a promising candidate for variable-speed drive applications as an alternative induction motor in various industrial application. However, the SRM has the disadvantage of nonlinear characteristic and control. It is suggested to use controller based on fuzzy logic system. Design of FLS controller and simulation model presented.
A Fuzzy Logic System to Analyze a Student's Lifestyle
Ghosh, Sourish; Boob, Aaditya Sanjay; Nikhil, Nishant; Vysyaraju, Nayan Raju; Kumar, Ankit
2016-01-01
A college student's life can be primarily categorized into domains such as education, health, social and other activities which may include daily chores and travelling time. Time management is crucial for every student. A self realisation of one's daily time expenditure in various domains is therefore essential to maximize one's effective output. This paper presents how a mobile application using Fuzzy Logic and Global Positioning System (GPS) analyzes a student's lifestyle and provides recom...
FUZZY LOGIC BASED TEMPERATURE CONTROL SYSTEM USING A MICROCONTROLLER
FİDAN, Uğur; BAY, Ö.FARUK
2002-01-01
This paper is aimed to illustrate the design and the implementation of a fuzzy logic controller(FLC) for an incubator using an AT89C205 microcontroller. The basis for fuzzy control and the general structure of the fuzzy logic controllers are illustrated. Then design and implementation steps of the FLC are explained. Experimental results are also included. The incubator temperature can be adjusted at any point between 25oC – 40 oC . The use of fuzzy logic controller in this application has pot...
Nguyen, Hung T
2005-01-01
THE CONCEPT OF FUZZINESS Examples Mathematical modeling Some operations on fuzzy sets Fuzziness as uncertainty Exercises SOME ALGEBRA OF FUZZY SETS Boolean algebras and lattices Equivalence relations and partitions Composing mappings Isomorphisms and homomorphisms Alpha-cuts Images of alpha-level sets Exercises FUZZY QUANTITIES Fuzzy quantities Fuzzy numbers Fuzzy intervals Exercises LOGICAL ASPECTS OF FUZZY SETS Classical two-valued logic A three-valued logic Fuzzy logic Fuzzy and Lukasiewi
Fuzzy Logic Control of a Ball on Sphere System
Directory of Open Access Journals (Sweden)
Seyed Alireza Moezi
2014-01-01
Full Text Available The scope of this paper is to present a fuzzy logic control of a class of multi-input multioutput (MIMO nonlinear systems called “system of ball on a sphere,” such an inherently nonlinear, unstable, and underactuated system, considered truly to be two independent ball and wheel systems around its equilibrium point. In this work, Sugeno method is investigated as a fuzzy controller method, so it works in a good state with optimization and adaptive techniques, which makes it very attractive in control problems, particularly for such nonlinear dynamic systems. The system’s dynamic is described and the equations are illustrated. The outputs are shown in different figures so as to be compared. Finally, these simulation results show the exactness of the controller’s performance.
Applying Performance-Controlled Systems, Fuzzy Logic, and Fly-by-Wire Controls to General Aviation
National Research Council Canada - National Science Library
Beringer, Dennis
2002-01-01
A fuzzy-logic 'performance control' system, providing envelope protection and direct command of airspeed, vertical velocity, and turn rate, was evaluated in a reconfigurable general aviation simulator...
Geo-Spatial Tactical Decision Aid Systems: Fuzzy Logic for Supporting Decision Making
National Research Council Canada - National Science Library
Grasso, Raffaele; Giannecchini, Simone
2006-01-01
.... This paper describes a tactical decision aid system based on fuzzy logic reasoning for data fusion and on current Open Geospatial Consortium specifications for interoperability, data dissemination...
An Adaptive Fuzzy-Logic Traffic Control System in Conditions of Saturated Transport Stream
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
On-line tuning of a fuzzy-logic power system stabilizer
International Nuclear Information System (INIS)
Hossein-Zadeh, N.; Kalam, A.
2002-01-01
A scheme for on-line tuning of a fuzzy-logic power system stabilizer is presented. firstly, a fuzzy-logic power system stabilizer is developed using speed deviation and accelerating power as the controller input variables. The inference mechanism of fuzzy-logic controller is represented by a decision table, constructed of linguistic IF-THEN rules. The Linguistic rules are available from experts and the design procedure is based on these rules. It assumed that an exact model of the plant is not available and it is difficult to extract the exact parameters of the power plant. Thus, the design procedure can not be based on an exact model. This is an advantage of fuzzy logic that makes the design of a controller possible without knowing the exact model of the plant. Secondly, two scaling parameters are introduced to tune the fuzzy-logic power system stabilizer. These scaling parameters are the outputs of another fuzzy-logic system, which gets the operating conditions of power system as inputs. These mechanism of tuning the fuzzy-logic power system stabilizer makes the fuzzy-logic power system stabilizer adaptive to changes in the operating conditions. Therefore, the degradation of the system response, under a wide range of operating conditions, is less compared to the system response with a fixed-parameter fuzzy-logic power system stabilizer and a conventional (linear) power system stabilizer. The tuned stabilizer has been tested by performing nonlinear simulations using a synchronous machine-infinite bus model. The responses are compared with a fixed parameters fuzzy-logic power system stabilizer and a conventional (linear) power system stabilizer. It is shown that the tuned fuzzy-logic power system stabilizer is superior to both of them
Application of fuzzy logic control system for reactor feed-water control
International Nuclear Information System (INIS)
Iijima, T.; Nakajima, Y.
1994-01-01
The successful actual application of a fuzzy logic control system to the a nuclear Fugen nuclear power reactor is described. Fugen is a heavy-water moderated, light-water cooled reactor. The introduction of fuzzy logic control system has enabled operators to control the steam drum water level more effectively in comparison to a conventional proportional-integral (PI) control system
A fuzzy logic based network intrusion detection system for predicting the TCP SYN flooding attack
CSIR Research Space (South Africa)
Mkuzangwe, Nenekazi NP
2017-04-01
Full Text Available presents a fuzzy logic based network intrusion detection system to predict neptune which is a type of a Transmission Control Protocol Synchronized (TCP SYN) flooding attack. The performance of the proposed fuzzy logic based system is compared to that of a...
Fuzzy Logic Based Autonomous Parallel Parking System with Kalman Filtering
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.
Fuzzy Logic Controller based on geothermal recirculating aquaculture system
Directory of Open Access Journals (Sweden)
Hanaa M. Farghally
2014-01-01
Full Text Available One of the most common uses of geothermal heat is in recirculation aquaculture systems (RAS where the water temperature is accurately controlled for optimum growing conditions for sustainable and intensive rearing of marine and freshwater fish. This paper presents a design for RAS rearing tank and brazed heat exchanger to be used with geothermal energy as a source of heating water. The heat losses from the RAS tank are calculated using Geo Heat Center Software. Then a plate type heat exchanger is designed using the epsilon – NTU analysis method. For optimal growth and abundance of production, a Fuzzy Logic control (FLC system is applied to control the water temperature (29 °C. A FLC system has several advantages over conventional techniques; relatively simple, fast, adaptive, and its response is better and faster at all atmospheric conditions. Finally, the total system is built in MATLAB/SIMULINK to study the overall performance of control unit.
Forest fire autonomous decision system based on fuzzy logic
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.
Formal Systems of Fuzzy Logic and their Fragments
Czech Academy of Sciences Publication Activity Database
Cintula, Petr; Hájek, Petr; Horčík, Rostislav
2007-01-01
Roč. 150, č. 1-3 (2007), s. 40-65 ISSN 0168-0072 R&D Projects: GA MŠk(CZ) 1M0545; GA AV ČR 1ET100300517 Institutional research plan: CEZ:AV0Z10300504 Keywords : mathematical fuzzy logic * BCK-algebras * BCK * FBCK * monoidal t-norm based logic Subject RIV: BA - General Mathematics Impact factor: 0.613, year: 2007
Fuzzy logic particle tracking velocimetry
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.
Design of a Tele-Control Electrical Vehicle System Using a Fuzzy Logic Control
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M. Boukhnifer
2012-11-01
Full Text Available This paper presents a fuzzy logic design of a tele-control electrical vehicle system. We showed that the application of fuzzy logic control allows the stability of tele-vehicle system in spite of communication delays between the operator and the vehicle. A robust bilateral controller design using fuzzy logic frameworks was proposed. This approach allows a convenient means to trade off robustness and stability for a pre-specified time-delay margin. Both the performance and robustness of the proposed method were demonstrated by simulation results for a constant time delay between the operator and the electrical vehicle system.
Control of an air conditional system with fuzzy logic and PIC using
ERKAYMAZ, Hande; ÇAYIROĞLU, İbrahim
2010-01-01
In this study an air conditioner system was put into practice as programming PIC by fuzzy logic system. The system keeps temperature of atmosphere between 19-23oC. As input variable damp and heat values are taken by sensor called SHT11 and they are transmitted to PIC 16F876 which programmed by fuzzy logic system. Heater and cooler fans work as required climate.
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Zoltan Erdei
2011-12-01
Full Text Available In this paper the authors present the usefulness of fuzzy logic in controlling engineering processes or applications. Although fuzzy logic does not represent a novelty for the scientific and engineering field, it enjoys a great appreciation from those involved in the two domains. The fact that fuzzy logic uses sentences kindred with the natural language make it easier to comprehend that a complex mathematical model required by the classic control theory. In MatLab software there are dedicated toolboxes to this subject that make the design of a fuzzy controller a facile one. In the paper design methods of a fuzzy controller are being presented both in Simulink and MatLab.
Feasibility analysis of fuzzy logic control for ITER Poloidal field (PF) AC/DC converter system
Energy Technology Data Exchange (ETDEWEB)
Hassan, Mahmood Ul; Fu, Peng [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Song, Zhiquan, E-mail: zhquansong@ipp.ac.cn [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Chen, Xiaojiao [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); University of Science and Technology of China (China); Zhang, Xiuqing [Institute of Plasma Physics, Chinese Academy of Sciences, Hefei 230031 (China); Humayun, Muhammad [Shanghai Jiaotong University (China)
2017-05-15
Highlights: • The implementation of the Fuzzy controller for the ITER PF converter system is presented. • The comparison of the FLC and PI simulation are investigated. • The FLC single and parallel bridge operation are presented. • Fuzzification and Defuzzification algorithms are presented using FLC controller. - Abstract: This paper describes the feasibility analysis of the fuzzy logic control to increase the performance of the ITER poloidal field (PF) converter systems. A fuzzy-logic-based controller is designed for ITER PF converter system, using the traditional PI controller and Fuzzy controller (FC), the dynamic behavior and transient response of the PF converter system are compared under normal operation by analysis and simulation. The analysis results show that the fuzzy logic control can achieve better operation performance than PI control.
Development of Fuzzy Logic Control for Vehicle Air Conditioning System
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Henry Nasution
2008-08-01
Full Text Available A vehicle air conditioning system is experimentally investigated. Measurements were taken during the experimental period at a time interval of one minute for a set point temperature of 22, 23 and 24oC with internal heat loads of 0, 1 and 2 kW. The cabin temperature and the speed of the compressor were varied and the performance of the system, energy consumption and energy saving ware analyzed. The main objective of the experimental work is to evaluate the energy saving obtained when the fuzzy logic control (FLC algorithm, through an inverter, continuously regulates the compressor speed. It demonstrates better control of the compressor operation in terms of energy consumption as compared to the control by using a thermostat imposing On/Off cycles on the compressor at the nominal frequency of 50 Hz. The experimental set-up consists of original components from the air conditioning system of a compact passenger vehicle. The experimental results indicate that the proposed technique can save energy and improve indoor comfort significantly for vehicle air conditioning systems compared to the conventional (On/Off control technique.
A Fuzzy-Logic advisory system for lean manufacturing within SMEs
Achanga, Pius Coxwell; Shehab, Essam; Roy, Rajkumar; Nelder, Geoff
2012-01-01
This research paper presents the development of a fuzzy-logic advisory system to assist small-medium size companies (SMEs) as a decision support tool for implementing lean manufacturing. The system is developed using fuzzy logic rules, with a combination of research methodology approaches employed in the research study that included data collection from ten manufacturing SMEs through documentation analysis, observation of companies' practices and semi-structured interviews. The overall system...
Energy Analysis for Air Conditioning System Using Fuzzy Logic Control
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Henry Nasution
2011-04-01
Full Text Available Reducing energy consumption and to ensure thermal comfort are two important considerations for the designing an air conditioning system. An alternative approach to reduce energy consumption proposed in this study is to use a variable speed compressor. The control strategy will be proposed using the fuzzy logic controller (FLC. FLC was developed to imitate the performance of human expert operators by encoding their knowledge in the form of linguistic rules. The system is installed on a thermal environmental room with a data acquisition system to monitor the temperature of the room, coefficient of performance (COP, energy consumption and energy saving. The measurements taken during the two hour experimental periods at 5-minutes interval times for temperature setpoints of 20oC, 22oC and 24oC with internal heat loads 0, 500, 700 and 1000 W. The experimental results indicate that the proposed technique can save energy in comparison with On/Off and proportional-integral-derivative (PID control.
A Novel Fuzzy Logic Based Power System Stabilizer for a Multimachine System
Singh, Anup; Sen, Indraneel
2003-01-01
This paper describes the design of a Fuzzy logic based controller to counter the small signal oscillatory instability in power system. The stabilizing signal is computed in real time using suitable fuzzy membership functions depending upon the state of the generator on the speed-acceleration phase plane. The use of output membership function permits further fine tuning of the controller parameters for varied system configurations specially in multimachine environment. The efficacy of the p...
ThetKoKo; ZawMyoTun; Hla Myo Tun
2015-01-01
Abstract This research paper describes the design and simulation of the automatic wiper speed and headlight modes controllers using fuzzy logic. This proposed system consists of a fuzzy logic controller to control a cars wiper speed and headlight modes. The automatic wiper system detects the rain and its intensity. And according to the rain intensity the wiper speed is automatically controlled. Headlight modes automatically changes either from low beam mode to high beam mode or form high beam...
T Atanassov, Krassimir
2017-01-01
The book offers a comprehensive survey of intuitionistic fuzzy logics. By reporting on both the author’s research and others’ findings, it provides readers with a complete overview of the field and highlights key issues and open problems, thus suggesting new research directions. Starting with an introduction to the basic elements of intuitionistic fuzzy propositional calculus, it then provides a guide to the use of intuitionistic fuzzy operators and quantifiers, and lastly presents state-of-the-art applications of intuitionistic fuzzy sets. The book is a valuable reference resource for graduate students and researchers alike.
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Derrouazin, A., E-mail: derrsid@gmail.com [University Hassiba BenBouali of Chlef, LGEER,Chlef (Algeria); Université de Lorraine, LMOPS, EA 4423, 57070 Metz (France); CentraleSupélec, LMOPS, 57070 Metz (France); Aillerie, M., E-mail: aillerie@metz.supelec.fr; Charles, J. P. [Université de Lorraine, LMOPS, EA 4423, 57070 Metz (France); CentraleSupélec, LMOPS, 57070 Metz (France); Mekkakia-Maaza, N. [Université des sciences et de la Technologie d’Oran, Mohamed Boudiaf-USTO MB,LMSE, Oran Algérie (Algeria)
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.
International Nuclear Information System (INIS)
Derrouazin, A.; Aillerie, M.; Charles, J. P.; Mekkakia-Maaza, N.
2016-01-01
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.
Fuzzy logic based control system for fresh water aquaculture: A MATLAB based simulation approach
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Rana Dinesh Singh
2015-01-01
Full Text Available Fuzzy control is regarded as the most widely used application of fuzzy logic. Fuzzy logic is an innovative technology to design solutions for multiparameter and non-linear control problems. One of the greatest advantages of fuzzy control is that it uses human experience and process information obtained from operator rather than a mathematical model for the definition of a control strategy. As a result, it often delivers solutions faster than conventional control design techniques. The proposed system is an attempt to apply fuzzy logic techniques to predict the stress factor on the fish, based on line data and rule base generated using domain expert. The proposed work includes a use of Data acquisition system, an interfacing device for on line parameter acquisition and analysis, fuzzy logic controller (FLC for inferring the stress factor. The system takes stress parameters on the fish as inputs, fuzzified by using FLC with knowledge base rules and finally provides single output. All the parameters are controlled and calibrated by the fuzzy logic toolbox and MATLAB programming.
Interval Type-2 Fuzzy Logic Controller Based Maximum Power Point Tracking in Photovoltaic Systems
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ALTIN, N.
2013-08-01
Full Text Available In this paper, interval type-2 fuzzy logic controller based maximum power point tracking method is proposed for photovoltaic systems. The proposed interval type-2 fuzzy logic controller has two inputs and one output. Rate of change in photovoltaic system output power and rate of change in photovoltaic system terminal voltage are selected as input variables and change in duty cycle as output variable. Seven type-2 membership functions are used for determined input and output variables of fuzzy logic controller. Since type-2 fuzzy sets are used, effect of uncertainties on maximum power point tracking capability is removed. Operation point of the photovoltaic system is controlled via a boost type DC?DC converter. Simulation results show that the proposed maximum power point tracking method provides fast dynamic response, and it is also useful for rapidly changing atmospheric conditions.
Ito, K; Gunji, Y P
1997-01-01
Complex systems in which internal agents (observers) interact with each other with finite velocity of information propagation cannot be described with a single consistent logic. We have proposed the bootstrapping system of cellular automata for describing such complex systems using two types of complementary logic: Boolean and non-Boolean. We extend this in this paper to a system of time-discrete continuous maps using fuzzy logic in place of non-Boolean logic. Fuzziness implies the intrinsic ambiguity of internal measurement. The bootstrapping system evolves, changing the dynamics perpetually, so that the discrepancy between the two types of complementary logic may be minimized. The equilibration force defined from the strength of discrepancy forms a landscape for self-organization which is similar to the fitness landscape for evolution. Though they appear similar, the former is derived from the internal dynamics. The goal of evolution, when applied to the map of the Belousov-Zabochinsky reaction, is demonstrated to be near the border between periodicity and chaos. The behavior depends on the degree of fuzziness and the extent of noise. When fuzziness increases too much, the system becomes unstable. Near the boundary, it exhibits intermittent chaos with a background of 1/f noise.
Deng, Zhaohong; Choi, Kup-Sze; Cao, Longbing; Wang, Shitong
2014-04-01
A challenge in modeling type-2 fuzzy logic systems is the development of efficient learning algorithms to cope with the ever increasing size of real-world data sets. In this paper, the extreme learning strategy is introduced to develop a fast training algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy logic systems. The proposed algorithm, called type-2 fuzzy extreme learning algorithm (T2FELA), has two distinctive characteristics. First, the parameters of the antecedents are randomly generated and parameters of the consequents are obtained by a fast learning method according to the extreme learning mechanism. In addition, because the obtained parameters are optimal in the sense of minimizing the norm, the resulting fuzzy systems exhibit better generalization performance. The experimental results clearly demonstrate that the training speed of the proposed T2FELA algorithm is superior to that of the existing state-of-the-art algorithms. The proposed algorithm also shows competitive performance in generalization abilities.
Fuzzy logic of Aristotelian forms
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Perlovsky, L.I. [Nichols Research Corp., Lexington, MA (United States)
1996-12-31
Model-based approaches to pattern recognition and machine vision have been proposed to overcome the exorbitant training requirements of earlier computational paradigms. However, uncertainties in data were found to lead to a combinatorial explosion of the computational complexity. This issue is related here to the roles of a priori knowledge vs. adaptive learning. What is the a-priori knowledge representation that supports learning? I introduce Modeling Field Theory (MFT), a model-based neural network whose adaptive learning is based on a priori models. These models combine deterministic, fuzzy, and statistical aspects to account for a priori knowledge, its fuzzy nature, and data uncertainties. In the process of learning, a priori fuzzy concepts converge to crisp or probabilistic concepts. The MFT is a convergent dynamical system of only linear computational complexity. Fuzzy logic turns out to be essential for reducing the combinatorial complexity to linear one. I will discuss the relationship of the new computational paradigm to two theories due to Aristotle: theory of Forms and logic. While theory of Forms argued that the mind cannot be based on ready-made a priori concepts, Aristotelian logic operated with just such concepts. I discuss an interpretation of MFT suggesting that its fuzzy logic, combining a-priority and adaptivity, implements Aristotelian theory of Forms (theory of mind). Thus, 2300 years after Aristotle, a logic is developed suitable for his theory of mind.
Fuzzy Logic Reliability Centered Maintenance
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Felecia .
2014-01-01
Full Text Available Reliability Centered Maintenence (RCM is a systematic maintenence strategy based on system reliability. Application of RCM process will not always come out with a binary output of “yes” and “no”. Most of the time they are not supported with available detail information to calculate system reliability. The fuzzy logic method attempts to eliminate the uncertainty by providing “truth” in different degrees.Data and responses from maintenance department will be processed using the two methods (reliability centered maintenance and fuzzy logic to design maintenance strategy for the company. The results of the fuzzy logic RCM application are maintenance strategy which fit with current and future condition.
CONTROL SYSTEM DESIGN WITH FUZZY LOGIC PID-СONTROLLER TYPE 2
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A. Tунік
2011-04-01
Full Text Available This paper presents a fuzzy logic PID-controller synthesis method for solid body guidance. Formany nonlinear systems with nonlinearities and uncertainties, the performance of fuzzy controllertype 1 may not be satisfactory. Therefore, in this work, fuzzy logic type 2 controller design isintroduced. These controllers capture the advantage of a linear controller in terms of simplicity andalso can handle nonlinearity because of their inference mechanism.The main feature of the proposedmethod constitutes in a membership functions type 2 applications. The membership function type 2is represented by upper and lower membership functions of type 1. The interval between these twofunctions represent the footprint of uncertainty, which give an opportunity to synthesize commonregulator for set of a models. The structure of fuzzy logic controller for solid body control isgrounded. Simulation results confirm the effectiveness of the proposed approach.
Fuzzy logic based anaesthesia monitoring systems for the detection of absolute hypovolaemia.
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.
Using fuzzy logic to integrate neural networks and knowledge-based systems
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.
Fuzzy Logic Based MPPT Controller for a PV System
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Carlos Robles Algarín
2017-12-01
Full Text Available The output power of a photovoltaic (PV module depends on the solar irradiance and the operating temperature; therefore, it is necessary to implement maximum power point tracking controllers (MPPT to obtain the maximum power of a PV system regardless of variations in climatic conditions. The traditional solution for MPPT controllers is the perturbation and observation (P&O algorithm, which presents oscillation problems around the operating point; the reason why improving the results obtained with this algorithm has become an important goal to reach for researchers. This paper presents the design and modeling of a fuzzy controller for tracking the maximum power point of a PV System. Matlab/Simulink (MathWorks, Natick, MA, USA was used for the modeling of the components of a 65 W PV system: PV module, buck converter and fuzzy controller; highlighting as main novelty the use of a mathematical model for the PV module, which, unlike diode based models, only needs to calculate the curve fitting parameter. A P&O controller to compare the results obtained with the fuzzy control was designed. The simulation results demonstrated the superiority of the fuzzy controller in terms of settling time, power loss and oscillations at the operating point.
Realworld maximum power point tracking simulation of PV system based on Fuzzy Logic control
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Ahmed M. Othman
2012-12-01
Full Text Available 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.
Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations
DEFF Research Database (Denmark)
Trnka, Hjalte; Wu, Jian-Xiong; van de Weert, Marco
2013-01-01
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...... 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...... fuzzy logic-based system was found to give comparable quality scores with visual evaluation, making high-throughput classification of cake quality possible....
Adaptive Fuzzy Logic based MPPT Control for PV System Under Partial Shading Condition
Choudhury, Subhashree; Rout, Pravat Kumar
2016-01-01
Partial shading causes power loss, hotspots and threatens the reliability of the Photovoltaic generation system. Moreover characteristic curves exhibit multiple peaks. Conventional MPPT techniques under this condition often fail to give optimum MPP. Focusing on the afore mentioned problem an attempt has been made to design an Adaptive Takagi-Sugeno Fuzzy Inference System based Fuzzy Logic Control MPPT.The mathematical model of PV array is simulated using in MATLAB/Simulink environment.Various...
Modelling of the automatic stabilization system of the aircraft course by a fuzzy logic method
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.
Molecular processors: from qubits to fuzzy logic.
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.
Anaesthesia monitoring using fuzzy logic.
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.
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.
A novel fuzzy logic inference system for decision support in weaning from mechanical ventilation.
Kilic, Yusuf Alper; Kilic, Ilke
2010-12-01
Weaning from mechanical ventilation represents one of the most challenging issues in management of critically ill patients. Currently used weaning predictors ignore many important dimensions of weaning outcome and have not been uniformly successful. A fuzzy logic inference system that uses nine variables, and five rule blocks within two layers, has been designed and implemented over mathematical simulations and random clinical scenarios, to compare its behavior and performance in predicting expert opinion with those for rapid shallow breathing index (RSBI), pressure time index and Jabour' weaning index. RSBI has failed to predict expert opinion in 52% of scenarios. Fuzzy logic inference system has shown the best discriminative power (ROC: 0.9288), and RSBI the worst (ROC: 0.6556) in predicting expert opinion. Fuzzy logic provides an approach which can handle multi-attribute decision making, and is a very powerful tool to overcome the weaknesses of currently used weaning predictors.
Convergent method of and apparatus for distributed control of robotic systems using fuzzy logic
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.
A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines.
Sun, Zhan-Li; Au, Kin-Fan; Choi, Tsan-Ming
2007-10-01
This paper investigates the feasibility of applying a relatively novel neural network technique, i.e., extreme learning machine (ELM), to realize a neuro-fuzzy Takagi-Sugeno-Kang (TSK) fuzzy inference system. The proposed method is an improved version of the regular neuro-fuzzy TSK fuzzy inference system. For the proposed method, first, the data that are processed are grouped by the k-means clustering method. The membership of arbitrary input for each fuzzy rule is then derived through an ELM, followed by a normalization method. At the same time, the consequent part of the fuzzy rules is obtained by multiple ELMs. At last, the approximate prediction value is determined by a weight computation scheme. For the ELM-based TSK fuzzy inference system, two extensions are also proposed to improve its accuracy. The proposed methods can avoid the curse of dimensionality that is encountered in backpropagation and hybrid adaptive neuro-fuzzy inference system (ANFIS) methods. Moreover, the proposed methods have a competitive performance in training time and accuracy compared to three ANFIS methods.
Fuzzy logic based power-efficient real-time multi-core system
Ahmed, Jameel; Najam, Shaheryar; Najam, Zohaib
2017-01-01
This book focuses on identifying the performance challenges involved in computer architectures, optimal configuration settings and analysing their impact on the performance of multi-core architectures. Proposing a power and throughput-aware fuzzy-logic-based reconfiguration for Multi-Processor Systems on Chip (MPSoCs) in both simulation and real-time environments, it is divided into two major parts. The first part deals with the simulation-based power and throughput-aware fuzzy logic reconfiguration for multi-core architectures, presenting the results of a detailed analysis on the factors impacting the power consumption and performance of MPSoCs. In turn, the second part highlights the real-time implementation of fuzzy-logic-based power-efficient reconfigurable multi-core architectures for Intel and Leone3 processors. .
Possible use of fuzzy logic in database
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Vaclav Bezdek
2011-04-01
Full Text Available The article deals with fuzzy logic and its possible use in database systems. At first fuzzy thinking style is shown on a simple example. Next the advantages of the fuzzy approach to database searching are considered on the database of used cars in the Czech Republic.
FUZZY LOGIC BASED INTELLIGENT CONTROL OF A VARIABLE SPEED CAGE MACHINE WIND GENERATION SYSTEM
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...
Location-aware News Recommendation System with Using Fuzzy Logic
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Mehdi Nejati
2016-10-01
Full Text Available with release of a huge amount of news on the Internet and the trend of users to Web-based news services.it is necessary to have a recommendation system. To grab attentions to news, news services use a number of criteria that called news values and user location is an important factor for it. In this paper, LONEF is proposed as a tow stage recommendation system. In first stage news are ranked by user’s locations and in second stage news are recommended by location Preferences, recency, Trustworthiness, groups priorities and popularity. To reduce ambiguity these properties is used tow Mamdani fuzzy interference and case-based decision systems. In Mamdani fuzzy interference system, it is tried to increase the system speed by optimizing selection of rules and membership functions and because of ambiguous feedback implementation, a decision making system is used to enable better simulation of user’s activities. Performance of our proposed approach is demonstrated in the experiments on different news groups.
Fuzzy Versions of Epistemic and Deontic Logic
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.
A VIRTUAL REALITY EXPOSURE THERAPY FOR PTSD PATIENTS CONTROLLED BY A FUZZY LOGIC SYSTEM
Directory of Open Access Journals (Sweden)
Rosa Maria Esteves Moreira da Costa
2014-06-01
Full Text Available This paper describes the main characteristics of two integrated systems that explore Virtual Reality technology and Fuzzy Logic to support and to control the assessment of people with Post-Traumatic Stress Disorder during the Virtual Reality Exposure Therapy. The integration of different technologies, the development methodology and the test procedures are described throughout the paper.
Fuzzy Logic for Incidence Geometry
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
Evaluation of Fuzzy Logic Subsets Effects on Maximum Power Point Tracking for Photovoltaic System
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Shahrooz Hajighorbani
2014-01-01
Full Text Available Photovoltaic system (PV has nonlinear characteristics which are affected by changing the climate conditions and, in these characteristics, there is an operating point in which the maximum available power of PV is obtained. Fuzzy logic controller (FLC is the artificial intelligent based maximum power point tracking (MPPT method for obtaining the maximum power point (MPP. In this method, defining the logical rule and specific range of membership function has the significant effect on achieving the best and desirable results. This paper presents a detailed comparative survey of five general and main fuzzy logic subsets used for FLC technique in DC-DC boost converter. These rules and specific range of membership functions are implemented in the same system and the best fuzzy subset is obtained from the simulation results carried out in MATLAB. The proposed subset is able to track the maximum power point in minimum time with small oscillations and the highest system efficiency (95.7%. This investigation provides valuable results for all users who want to implement the reliable fuzzy logic subset for their works.
Design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization
Castillo, Oscar; Kacprzyk, Janusz
2015-01-01
This book presents recent advances on the design of intelligent systems based on fuzzy logic, neural networks and nature-inspired optimization and their application in areas such as, intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in eight main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on fuzzy systems. The second part contains papers with the main theme of neural networks theory, which are basically papers dealing with new concepts and algorithms in neural networks. The third part contains papers describing applications of neural networks in diverse areas, such as time series prediction and pattern recognition. The fourth part contains papers describing new nature-inspired optimization algorithms. The fifth part presents div...
A fuzzy-logic-based approach to qualitative safety modelling for marine systems
International Nuclear Information System (INIS)
Sii, H.S.; Ruxton, Tom; Wang Jin
2001-01-01
Safety assessment based on conventional tools (e.g. probability risk assessment (PRA)) may not be well suited for dealing with systems having a high level of uncertainty, particularly in the feasibility and concept design stages of a maritime or offshore system. By contrast, a safety model using fuzzy logic approach employing fuzzy IF-THEN rules can model the qualitative aspects of human knowledge and reasoning processes without employing precise quantitative analyses. A fuzzy-logic-based approach may be more appropriately used to carry out risk analysis in the initial design stages. This provides a tool for working directly with the linguistic terms commonly used in carrying out safety assessment. This research focuses on the development and representation of linguistic variables to model risk levels subjectively. These variables are then quantified using fuzzy sets. In this paper, the development of a safety model using fuzzy logic approach for modelling various design variables for maritime and offshore safety based decision making in the concept design stage is presented. An example is used to illustrate the proposed approach
A first course in fuzzy logic, fuzzy dynamical systems, and biomathematics theory and applications
de Barros, Laécio Carvalho; Lodwick, Weldon Alexander
2017-01-01
This book provides an essential introduction to the field of dynamical models. Starting from classical theories such as set theory and probability, it allows readers to draw near to the fuzzy case. On one hand, the book equips readers with a fundamental understanding of the theoretical underpinnings of fuzzy sets and fuzzy dynamical systems. On the other, it demonstrates how these theories are used to solve modeling problems in biomathematics, and presents existing derivatives and integrals applied to the context of fuzzy functions. Each of the major topics is accompanied by examples, worked-out exercises, and exercises to be completed. Moreover, many applications to real problems are presented. The book has been developed on the basis of the authors’ lectures to university students and is accordingly primarily intended as a textbook for both upper-level undergraduates and graduates in applied mathematics, statistics, and engineering. It also offers a valuable resource for practitioners such as mathematical...
FUZZY LOGIC CONTROLLED SWITCHED RELUCTANCE MOTOR DRIVER DESIGNING FOR A LIFT SYSTEM
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Mahir DURSUN
2006-02-01
Full Text Available In this study, a 8/6 poles, four phases, 3.44 kW switched reluctance motor driver was used for a elavator load. For this aim, it has been designed a swithed reluctance motor driver for a lift system. At the driver system was used a buck konverter. The speed was controlled by motor phase voltage control. The voltage value has been controlled with fuzzy logic controller by using TMS320LF2407 controller. Fuzzy controlled switched reluctance motor was used for a elavator load by using designed driver system.
A Fuzzy Logic-Based Video Subtitle and Caption Coloring System
Directory of Open Access Journals (Sweden)
Mohsen Davoudi
2012-01-01
Full Text Available An approach has been proposed for automatic adaptive subtitle coloring using fuzzy logic-based algorithm. This system changes the color of the video subtitle/caption to “pleasant” color according to color harmony and the visual perception of the image background colors. In the fuzzy analyzer unit, using RGB histograms of background image, the R, G, and B values for the color of the subtitle/caption are computed using fixed fuzzy IF-THEN rules fully driven from the color harmony theories to satisfy complementary color and subtitle-background color harmony conditions. A real-time hardware structure has been proposed for implementation of the front-end processing unit as well as the fuzzy analyzer unit.
A hybrid algorithm and its applications to fuzzy logic modeling of nonlinear systems
Wang, Zhongjun
System models allow us to simulate and analyze system dynamics efficiently. Most importantly, system models allow us to make prediction about system behaviors and to perform system parametric variation analysis without having to build the actual systems. The fuzzy logic modeling technique has been successfully applied in complex nonlinear system modeling such as unsteady aerodynamics modeling etc. recently. However, the current forward search algorithm to identify fuzzy logic model structures is very time-consuming. It is not unusual to spend several days or even a few weeks in computer CPU time to obtain better nonlinear system model structures by this forward search. Moreover, how to speed up the fuzzy logic model parameter identification process is also challenging when the number of influencing variables of nonlinear systems is large. To solve these problems, a hybrid algorithm for the nonlinear system modeling is proposed, formalized, implemented, and evaluated in this dissertation. By combining the fuzzy logic modeling technique with genetic algorithms, the developed hybrid algorithm is applied to both fuzzy logic model structure identification and model parameter identification. In the model structure identification process, the hybrid algorithm has the ability to find feasible structures more efficiently and effectively than the forward search. In the model parameter identification process (by using Newton gradient descent algorithm), the proposed hybrid algorithm incorporates genetic search algorithm to dynamically select convergence factors. It has the advantages of quick search yet maintains the monotonically convergent properties of the Newton gradient descent algorithm. To evaluate the properties of the developed hybrid algorithm, a nonlinear, unsteady aerodynamic normal force model with a complex system involving fourteen influencing variables is established from flight data. The results show that this hybrid algorithm can identify the aerodynamic
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.
Single axis control of ball position in magnetic levitation system using fuzzy logic control
Sahoo, Narayan; Tripathy, Ashis; Sharma, Priyaranjan
2018-03-01
This paper presents the design and real time implementation of Fuzzy logic control(FLC) for the control of the position of a ferromagnetic ball by manipulating the current flowing in an electromagnet that changes the magnetic field acting on the ball. This system is highly nonlinear and open loop unstable. Many un-measurable disturbances are also acting on the system, making the control of it highly complex but interesting for any researcher in control system domain. First the system is modelled using the fundamental laws, which gives a nonlinear equation. The nonlinear model is then linearized at an operating point. Fuzzy logic controller is designed after studying the system in closed loop under PID control action. The controller is then implemented in real time using Simulink real time environment. The controller is tuned manually to get a stable and robust performance. The set point tracking performance of FLC and PID controllers were compared and analyzed.
Many-valued Logic and Fuzzy Logic
Czech Academy of Sciences Publication Activity Database
Hájek, Petr
2011-01-01
Roč. 27, č. 2 (2011), s. 315-324 ISSN 0970-7794 R&D Projects: GA ČR GEICC/08/E018 Institutional research plan: CEZ:AV0Z10300504 Keywords : many valued logic * fuzzy logic Subject RIV: BA - General Mathematics
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.
The Medical Microrobot Control System Design via Fuzzy Logic Application
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A. S. Yuschenko
2014-01-01
Full Text Available The aim of the investigation is the development of the instruments and technologies for diagnostics and treatment of tube-like human’s organs such as blood vessels and intestines. The medical microrobots may be applied to move along the tube-like organs by the same way as a worm. Such microrobots had been presented in some works in Russia and abroad among them is a project of BMSTU. The control system of the robot has to adapt the movement process to the peculiarity of the biology environment. The safety of the application of robotic device inside the human body is the main requirement to the construction.An experimental model of microrobot has three segments which contracting successively to ensure progressive movement of the device. The diameter of the robot is smaller than the same of the blood vessel. So it is pressed to the internal cover of the vessel by the special planes to avoid the thrombosis of the vessel. Every segment of robot contain three contact elements, pressure sensors and a regulator to control the pressure of the elements to the internal surface of the vessel. Aboard the robot is a micro-video camera has been mounted to inform the surgeon of the situation inside the vessel and other micro-devices. The supporting plates carry tens metric sensors to control the contact forces. The driver of the robot is of hydraulic type with physiologic solution to avoid the danger of embolism.Microrobot is a part of the robotic system including also a hydro-driver mounted in the stationary part of the system and intelligent interface of the operator. The surgeon-operator has opportunity to observe the inner surface of the vessel by the sensors mounted aboard the robot and to control the robot movement along the vessel. The construction of the microrobot has to guarantee the stable position of the robot in the moving blood flow and its movement inside the vessel without any damage of the inner surface.The peculiarity of the microrobot
An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems
Chun-Liang Liu; Jing-Hsiao Chen; Yi-Hua Liu; Zong-Zhen Yang
2014-01-01
In this paper, a fuzzy-logic-control (FLC) based maximum power point tracking (MPPT) algorithm for photovoltaic (PV) systems is proposed. The power variation and output voltage variation are chosen as inputs of the proposed FLC, which simplifies the calculation. Compared with the conventional perturb and observe (P&O) method, the proposed FLC-based MPPT can simultaneously improve the dynamic and steady state performance of the PV system. To further improve the performance of the proposed ...
Design of a fuzzy-logic-control-based robust power system stabilizer
Zhao, Hui; Liu, Lu-yuan; Wang, Hong-jun; Yue, You-jun
2008-10-01
This paper presents a design procedure for a robust power system stabilizer(RPSS) based on fuzzy logic control techniques. Speed deviation of a synchronous generator and its derivative are chosen as the input signals of RPSS. A normalized sum-squared deviation(NSSD) index is used to design the RPSS and investigate the robustness of the RPSS for a multi-machine power system. Nonlinear simulation tests under different disturbances are given and their results are discussed.
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.
A fuzzy logic intelligent diagnostic system for spacecraft integrated vehicle health management
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.
Fuzzy logic electric vehicle regenerative antiskid braking and traction control system
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.
Directory of Open Access Journals (Sweden)
ThetKoKo
2015-07-01
Full Text Available Abstract This research paper describes the design and simulation of the automatic wiper speed and headlight modes controllers using fuzzy logic. This proposed system consists of a fuzzy logic controller to control a cars wiper speed and headlight modes. The automatic wiper system detects the rain and its intensity. And according to the rain intensity the wiper speed is automatically controlled. Headlight modes automatically changes either from low beam mode to high beam mode or form high beam mode to low beam mode depending on the light intensity from the other vehicle coming from the opposite direction. The system comprises of PIC impedance sensor piezoelectric vibration sensor LDR headlamps and a DC motor to accurate the windshield wiper. Piezoelectric sensor is used to detect the rain intensity which is based on the piezoelectric effect. MATLAB software is used to achieve the designed goal.
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
Fuzzy logic based robotic controller
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.
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.
Fuzzy Logic-based expert system for evaluating cake quality of freeze-dried formulations.
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.
Platz, M.; Rapp, J.; Groessler, M.; Niehaus, E.; Babu, A.; Soman, B.
2014-11-01
A Spatial Decision Support System (SDSS) provides support for decision makers and should not be viewed as replacing human intelligence with machines. Therefore it is reasonable that decision makers are able to use a feature to analyze the provided spatial decision support in detail to crosscheck the digital support of the SDSS with their own expertise. Spatial decision support is based on risk and resource maps in a Geographic Information System (GIS) with relevant layers e.g. environmental, health and socio-economic data. Spatial fuzzy logic allows the representation of spatial properties with a value of truth in the range between 0 and 1. Decision makers can refer to the visualization of the spatial truth of single risk variables of a disease. Spatial fuzzy logic rules that support the allocation of limited resources according to risk can be evaluated with measure theory on topological spaces, which allows to visualize the applicability of this rules as well in a map. Our paper is based on the concept of a spatial fuzzy logic on topological spaces that contributes to the development of an adaptive Early Warning And Response System (EWARS) providing decision support for the current or future spatial distribution of a disease. It supports the decision maker in testing interventions based on available resources and apply risk mitigation strategies and provide guidance tailored to the geo-location of the user via mobile devices. The software component of the system would be based on open source software and the software developed during this project will also be in the open source domain, so that an open community can build on the results and tailor further work to regional or international requirements and constraints. A freely available EWARS Spatial Fuzzy Logic Demo was developed wich enables a user to visualize risk and resource maps based on individual data in several data formats.
A fuzzy logic based closed-loop control system for blood glucose level regulation in diabetics.
Ibbini, M S; Masadeh, M A
2005-01-01
In this study, a closed-loop system to control the plasma glucose level in patients with diabetes mellitus type 1 is proposed. This control scheme is based on fuzzy logic control theory to maintain a normoglycaemic average of 4.5 mmol 1(-1) and the normal conditions for free plasma insulin concentration in severe initial state; in particular, when the diabetic patient is subjected to a glucose meal disturbance or fluctuations in the measured glucose level due to error in the measuring instrument. The proposed controller has demonstrated superiority over other conventional controlling therapies. While fuzzy logic controllers have shown promising results in many fields, a comparative study is presented with well-known conventional controllers such as Proportional-Integral-Derivative (PID) and continuous insulin infusion control strategies. The simulated results, for the proposed controller, are presented and discussed.
Hardware simulation of automatic braking system based on fuzzy logic control
Directory of Open Access Journals (Sweden)
Noor Cholis Basjaruddin
2016-07-01
Full Text Available In certain situations, a moving or stationary object can be a barrier for a vehicle. People and vehicles crossing could potentially get hit by a vehicle. Objects around roads as sidewalks, road separator, power poles, and railroad gates are also a potential source of danger when the driver is inattentive in driving the vehicle. A device that can help the driver to brake automatically is known as Automatic Braking System (ABS. ABS is a part of the Advanced Driver Assistance Systems (ADAS, which is a device designed to assist the driver in driving the process. This device was developed to reduce human error that is a major cause of traffic accidents. This paper presents the design of ABS based on fuzzy logic which is simulated in hardware by using a remote control car. The inputs of fuzzy logic are the speed and distance of the object in front of the vehicle, while the output of fuzzy logic is the intensity of braking. The test results on the three variations of speed: slow-speed, medium-speed, and high-speed shows that the design of ABS can work according to design.
Fuzzy logic and neural network technologies
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.
Interval type-2 fuzzy logic system for diagnosis coronary artery disease
Directory of Open Access Journals (Sweden)
Adha Mashur Sajiah
2016-12-01
Full Text Available Coronary artery disease (CAD is a disease that has been the deadliest disease in Indonesia. The ratio of cardiologists over potential patients is not appropriate either. Intelligent system which can help doctors or patients for cheap and efficient diagnosing CAD is needed. Medical record data, acquisition of cardiologist knowledge and computing technology can be utilized for developing fuzzy logic based intelligent system. Type-1 fuzzy logic system (T1 FLS has been widely used in various fields. T1 FS has limitation in representing and modelling uncertainty and minimize the impact. Whereas, type-2 fuzzy set (T2 FS was also introduced as fuzzy set that can model uncertainty more sophisticated. T2 FLS does have a higher degree of freedom when modeling uncertainty but it is quite difficult to make the membership function. An interval T2 FS is a T2 FS in which the membership grade on third dimension is the same everywhere so it is simpler than T2 FS. This paper aims to clarify the better capability of IT2 FLS over T1 FLS on the development of CAD diagnosis system. Rules and membership function were formulated with the help of fuzzy c-means. This study illustrated the causes of CAD risk factors, fuzzification, type reduction and defuzzification. The resulted system was tested with percentage split method (50%-50% to produce training data and testing data. This test is performed ten times with random seed to separate the data set. The resulted system generates an average of 73.78% accuracy, 71.94% sensitivity and 76.52% specificity.
Mathematics of Fuzzy Sets and Fuzzy Logic
Bede, Barnabas
2013-01-01
This book presents a mathematically-based introduction into the fascinating topic of Fuzzy Sets and Fuzzy Logic and might be used as textbook at both undergraduate and graduate levels and also as reference guide for mathematician, scientists or engineers who would like to get an insight into Fuzzy Logic. Fuzzy Sets have been introduced by Lotfi Zadeh in 1965 and since then, they have been used in many applications. As a consequence, there is a vast literature on the practical applications of fuzzy sets, while theory has a more modest coverage. The main purpose of the present book is to reduce this gap by providing a theoretical introduction into Fuzzy Sets based on Mathematical Analysis and Approximation Theory. Well-known applications, as for example fuzzy control, are also discussed in this book and placed on new ground, a theoretical foundation. Moreover, a few advanced chapters and several new results are included. These comprise, among others, a new systematic and constructive approach for fuzzy infer...
A voltage-frequency fuzzy logic controller for large scale power systems
Energy Technology Data Exchange (ETDEWEB)
Sabry, W. [Egyptian Armed Forces (Egypt)
1999-07-01
This paper presents a novel control operation of fuzzy logic power system stabilizer (FLPSS) for stability enhancement of large scale power systems. The FLPSS is applied for each machine in the system. In order to accomplish best damping characteristics for rotor speed and terminal voltage of each machine in the system, two signals from each machine are chosen as inputs to the FLPSS of this machine; deviation of rotor speed and deviation of terminal voltage. This technique proved its efficiency in damping oscillations in the frequency and variations in the terminal voltage signals of each machine in the system. Hence, a better system stability is achieved. (author)
Fuzzy logic control for improved pressurizer systems in nuclear power plants
International Nuclear Information System (INIS)
Brown, Chris; Gabbar, Hossam A.
2014-01-01
Highlights: • Improved performance of the pressurizer system in a CANDU nuclear power plant (NPP). • Inventory control for the pressurizer system in NPP. • Compare fuzzy logic with PID in pressurizer system in NPP. • Develop a fuzzy controller to regulate the pressurizer inventory control. • Compare control performance with current proportional controller used at NPP. - Abstract: The pressurizer system in a CANDU nuclear power plant is responsible for maintaining the pressure of the primary heat transport system to ensure the plant is operated within its safe operating envelope. The inventory control for the pressurizer system use a combination of level sensors, feed valves and bleed valves to ensure that there is adequate room in the pressurizer to accommodate any swell or shrinkage in the PHT system. The Darlington Nuclear Generating Station (DNGS) in Ontario, Canada currently uses a proportional controller for the bleed and feed valves to regulate the pressurizer inventory control which can result in large coolant level overshoot along with excessive settling times. The purpose of this paper is to develop a fuzzy controller to regulate the pressurizer inventory control and compare its performance to the current proportional controller used at DNGS. The simulation of the pressurizer inventory control system shows the fuzzy controller performs better than the proportional controller in terms of settling time and overshoot
Expert systems with fuzzy logic for intelligent diagnosis and control of nuclear power plants
International Nuclear Information System (INIS)
Abdelhai, M.I.; Upadhyaya, B.R.
1990-01-01
A model-based production-rule analysis system was developed for the tracking and diagnosis of the condition of a reactor coolant system (RCS) using a fuzzy logic algorithm. Since nuclear power plants are large and complex systems, it is natural that vagueness, uncertainty, and imprecision are introduced to such systems. Even in fully automated power plants, the critical diagnostic and control changes must be made by operators who usually express their diagnostic and control strategies linguistically as sets of heuristic decision rules. Therefore, additional imprecisions are introduced into the systems because of the imprecise nature of such qualitative strategies when they are converted into quantitative rules. Such problems, in which the source of imprecision is the absence of sharply defined criteria of class membership, could be dealt with using fuzzy set theory. Hence, a fuzzy logic algorithm could be initiated to implement a known heuristic whenever the given information is vague and qualitative, and it will allow operators to introduce certain linguistic assertions and commands to diagnose and control the system
Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization.
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.
DEFF Research Database (Denmark)
Hassan, Saima; Ahmadieh Khanesar, Mojtaba; Hajizadeh, Amin
2017-01-01
Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic sy...... to the prediction of solar photovoltaic output. It is observed that MOEA/D outperforms MOPSO in this case in terms of quality of results and its diversity. Finally, one point is selected from the obtained Pareto front and its performance is illustrated....
Fault current reduction by SFCL in a distribution system with PV using fuzzy logic technique
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.
A proposal for off-grid photovoltaic systems with non-controllable loads using fuzzy logic
International Nuclear Information System (INIS)
Yahyaoui, Imene; Sallem, Souhir; Kamoun, M.B.A.; Tadeo, Fernando
2014-01-01
Highlights: • An energy management system is proposed for off-grid PV systems, based on fuzzy logic. • The proposal guarantees the energy balance and battery protection. • The approach is demonstrated using data measured at the target location. - Abstract: A fuzzy-logic based methodology is proposed and evaluated for energy management in off-grid installations with photovoltaic panels as the source of energy and a limited storage capacity in batteries. The decision on the connection or disconnection of components is based on fuzzy rules on the basis of the Photovoltaic Panel Generation measurement, the measured power required by the load, and the estimation of the stored energy in the batteries (this last is obtained from the estimation of the Depth-of-Discharge). The algorithm aims to ensure the system’s autonomy by controlling the switches linking the system components with respect to a multi-objective management criterion developed from the requirements (supply of the load, protection of the battery, etc.). Detailed tests of the proposed system are carried out using data (irradiation, temperature, power consumption, etc.) measured in a household at the target area at several days of the year. The results demonstrate that the proposed approach achieves the objectives of system autonomy, battery protection and power supply stability. Compared with a basic algorithm, the proposed algorithm is not sensitive to sudden changes in atmospheric parameters and avoids overcharging the battery
Directory of Open Access Journals (Sweden)
Majid Jamil
2015-09-01
Full Text Available In this proposed work a fuzzy logic based algorithm using discrete wavelet transform is developed for identifying the various faults in the electrical distribution system for an unbalanced distribution electrical power system. This technique is capable to identify the ten different types of faults with negligible effect of variation in fault inception angle, loading and other parameters of the power distribution system. The proposed method is tested on IEEE 13 bus electrical distribution system and on an Indian scenario of distribution system. The current of respective three phases is used as input signal for fault identification and the results obtained from the proposed method are more than satisfactory.
Integrated development environment for fuzzy logic applications
Pagni, Andrea; Poluzzi, Rinaldo; Rizzotto, GianGuido; Lo Presti, Matteo
1993-12-01
During the last five years, Fuzzy Logic has gained enormous popularity, both in the academic and industrial worlds, breaking up the traditional resistance against changes thanks to its innovative approach to problems formalization. The success of this new methodology is pushing the creation of a brand new class of devices, called Fuzzy Machines, to overcome the limitations of traditional computing systems when acting as Fuzzy Systems and adequate Software Tools to efficiently develop new applications. This paper aims to present a complete development environment for the definition of fuzzy logic based applications. The environment is also coupled with a sophisticated software tool for semiautomatic synthesis and optimization of the rules with stability verifications. Later it is presented the architecture of WARP, a dedicate VLSI programmable chip allowing to compute in real time a fuzzy control process. The article is completed with two application examples, which have been carried out exploiting the aforementioned tools and devices.
Structural Completeness in Fuzzy Logics
Czech Academy of Sciences Publication Activity Database
Cintula, Petr; Metcalfe, G.
2009-01-01
Roč. 50, č. 2 (2009), s. 153-183 ISSN 0029-4527 R&D Projects: GA MŠk(CZ) 1M0545 Institutional research plan: CEZ:AV0Z10300504 Keywords : structral logics * fuzzy logics * structural completeness * admissible rules * primitive variety * residuated lattices Subject RIV: BA - General Mathematics
Directory of Open Access Journals (Sweden)
Pimonov Alexander
2017-01-01
Full Text Available This paper presents the multipurpose approach to evaluation of research and innovation projects based on the method of analysis of hierarchies and fuzzy logics for the mining industry. The approach, implemented as part of a decision support system, can reduce the degree of subjectivity during examinations by taking into account both quantitative and qualitative characteristics of the compared innovative alternatives; it does not depend on specific conditions of examination and allows engagement of experts of various fields of knowledge. The system includes the mechanism of coordination of several experts’ views. Using of fuzzy logics allows evaluating the qualitative characteristics of innovations in the form of formalized logical conclusions.
Photovoltaic System Modeling with Fuzzy Logic Based Maximum Power Point Tracking Algorithm
Directory of Open Access Journals (Sweden)
Hasan Mahamudul
2013-01-01
Full Text Available This paper represents a novel modeling technique of PV module with a fuzzy logic based MPPT algorithm and boost converter in Simulink environment. The prime contributions of this work are simplification of PV modeling technique and implementation of fuzzy based MPPT system to track maximum power efficiently. The main highlighted points of this paper are to demonstrate the precise control of the duty cycle with respect to various atmospheric conditions, illustration of PV characteristic curves, and operation analysis of the converter. The proposed system has been applied for three different PV modules SOLKAR 36 W, BP MSX 60 W, and KC85T 87 W. Finally the resultant data has been compared with the theoretical prediction and company specified value to ensure the validity of the system.
Implementation of Power System Stabilizer Based on Conventional and Fuzzy Logic Controllers
Directory of Open Access Journals (Sweden)
Hanan Mikhael Habbi
2018-03-01
Full Text Available To damp the low-frequency oscillations which occurred due to the disturbances in the electrical power system, the generators are equipped with Power System Stabilizer (PSS that provide supplementary feedback stabilizing signals. The low-frequency oscillations in power system are classified as local mode oscillations, intra-area mode oscillation, and interarea mode oscillations. A suitable PSS model was selected considering the low frequencies oscillation in the inter-area mode based on conventional PSS and Fuzzy Logic Controller. Two types of (FIS Mamdani and suggeno were considered in this paper. The software of the methods was executed using MATLAB R2015a package.
DEFF Research Database (Denmark)
Zhao, Haoran; Wu, Qiuwei; Wang, Chengshan
2015-01-01
Microgrid is an efficient solution to integraterenewable energy sources (RES) into power systems. Inorder to deal with the intermittent characteristics of therenewable energy based distributed generation (DG) units,a fuzzy-logic based coordinated control strategy of thebattery energy storage system...... (BESS) and dispatchableDG units is proposed in this paper for the microgridmanagement system (MMS). In the proposed coordinatedcontrol strategy, the BESS is used to mitigate the activepower exchange at the point of common coupling of themicrogrid for the grid-connected operation, and is used forthe...... frequency control for the island operation. Theeffectiveness of the proposed control strategy was verifiedby case studies using DIgSILENT/PowerFactroy....
Expert system for fault diagnosis in process control valves using fuzzy-logic
International Nuclear Information System (INIS)
Carneiro, Alvaro L.G.; Porto Junior, Almir C.S.
2013-01-01
The models of asset maintenance of a process plant basically are classified in corrective maintenance, preventive, predictive and proactive (online). The corrective maintenance is the elementary and most obvious way of the maintenance models. The preventive maintenance consists in a fault prevention work, based on statistical studies that can lead to low efficiency or even an unexpected shutdown of the plant. Predictive maintenance aims to prevent equipment or systems failures through monitoring and tracking of parameters, allowing continuous operation as long as possible. The proactive maintenance usually includes predictive maintenance, emphasizing the root cause analysis of the failure. The maintenance predictive/proactive planning frequently uses software that integrates data from different systems, which facilitates a quick and effective decision- making. In nuclear plants this model has an important role regarding the reliability of equipment and systems. The main focus of this work is to study the development of a model of non-intrusive monitoring and diagnosis applied to process control valves using artificial intelligence by fuzzy logic technique, contributing in the development of predictive methodologies identifying faults in incipient state. The control valve analyzed belongs to a steam plant which simulates the secondary circuit of a PWR nuclear reactor - Pressurized Water Reactor. This study makes use of MATLAB language through the fuzzy logic toolbox which uses the method of inference Mamdani, acting by fuzzy conjunction, through Triangular Norms (t-norm) and Triangular Conorms (t-conorm). As input variables are used air pressure and displacement of the valve stem. Input data coming into the fuzzy system by graph of the automation system Delta V ® available in the plant, which receives a signal of electric current from an 'intelligent' positioned installed on the valve. The output variable is the 'status' of the valve. Through a rule base
Fault Diagnosis in Deaerator Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
S Srinivasan
2007-01-01
Full Text Available In this paper a fuzzy logic based fault diagnosis system for a deaerator in a power plant unit is presented. The system parameters are obtained using the linearised state space deaerator model. The fuzzy inference system is created and rule base are evaluated relating the parameters to the type and severity of the faults. These rules are fired for specific changes in system parameters and the faults are diagnosed.
control of a dc motor using fuzzy logic control algorithm
African Journals Online (AJOL)
user
This study sought to establish the impact of a fuzzy logic controller (FLC) and a Proportional-Integral-Derivative (PID) controller in the control performance of an industrial type DC motor using MATLAB. The fuzzy logic controller was developed on the basis of Mamdani type fuzzy inference system (FIS). The centroid method ...
Efficient fuzzy logic controller for magnetic levitation systems | Shu ...
African Journals Online (AJOL)
Magnetic levitation is a system of suspending a body or a complete system against gravity. Suspending a system in air against gravity without using fixed structure for supporting is highly unstable and complex. In the previous research many techniques of stabilizing magnetic levitation systems were discussed. In this paper ...
FUZZY LOGIC IN LEGAL EDUCATION
Directory of Open Access Journals (Sweden)
Z. Gonul BALKIR
2011-04-01
Full Text Available The necessity of examination of every case within its peculiar conditions in social sciences requires different approaches complying with the spirit and nature of social sciences. Multiple realities require different and various perceptual interpretations. In modern world and social sciences, interpretation of perception of valued and multi-valued have been started to be understood by the principles of fuzziness and fuzzy logic. Having the verbally expressible degrees of truthness such as true, very true, rather true, etc. fuzzy logic provides the opportunity for the interpretation of especially complex and rather vague set of information by flexibility or equivalence of the variables’ of fuzzy limitations. The methods and principles of fuzzy logic can be benefited in examination of the methodological problems of law, especially in the applications of filling the legal loopholes arising from the ambiguities and interpretation problems in order to understand the legal rules in a more comprehensible and applicable way and the efficiency of legal implications. On the other hand, fuzzy logic can be used as a technical legal method in legal education and especially in legal case studies and legal practice applications in order to provide the perception of law as a value and the more comprehensive and more quality perception and interpretation of value of justice, which is the core value of law. In the perception of what happened as it has happened in legal relationships and formations, the understanding of social reality and sociological legal rules with multi valued sense perspective and the their applications in accordance with the fuzzy logic’s methods could create more equivalent and just results. It can be useful for the young lawyers and law students as a facilitating legal method especially in the materialization of the perception and interpretation of multi valued and variables. Using methods and principles of fuzzy logic in legal
Directory of Open Access Journals (Sweden)
Mohsen Omidvar
2015-12-01
Full Text Available Background & objective: Due to the features such as intuitive graphical appearance, ease of perception and straightforward applicability, risk matrix has become as one of the most used risk assessment tools. On the other hand, features such as the lack of precision in the classification of risk index, as well as subjective computational process, has limited its use. In order to solve this problem, in the current study we used fuzzy logic inference systems and mathematical operators (interval numbers and mapping operator. Methods: In this study, first 10 risk scenarios in the excavation and piping process were selected, then the outcome of the risk assessment were studied using four types of matrix including traditional (ORM, displaced cells (RCM , extended (ERM and fuzzy (FRM risk matrixes. Results: The results showed that the use of FRM and ERM matrix have prority, due to the high level of " Risk Tie Density" (RTD and "Risk Level Density" (RLD in the ORM and RCM matrix, as well as more accurate results presented in FRM and ERM, in risk assessment. While, FRM matrix provides more reliable results due to the application of fuzzy membership functions. Conclusion: Using new mathematical issues such as fuzzy sets and arithmetic and mapping operators for risk assessment could improve the accuracy of risk matrix and increase the reliability of the risk assessment results, when the accurate data are not available, or its data are avaliable in a limit range.
A modular diagnosis system based on fuzzy logic for UASB reactors treating sewage.
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.
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.
A fuzzy logic decision support system for assessing clinical nutritional risk
Directory of Open Access Journals (Sweden)
Ali Mohammad Hadianfard
2015-04-01
Full Text Available Introduction: Studies have indicated a global high prevalence of hospital malnutrition on admission and during hospitalization. Clinical Nutritional Risk Screen (CNRS is a way to identify malnutrition and manage nutritional interventions. Several traditional and non-computer based tools have been suggested for screening nutritional risk levels. The present study was an attempt to employ a computer based fuzzy model decision support system as a nutrition-screening tool for inpatients. Method: This is an applied modeling study. The system architecture was designed based on the fuzzy logic model including input data, inference engine, and output. A clinical nutritionist entered nineteen input variables using a windows-based graphical user interface. The inference engine was involved with knowledge obtained from literature and the construction of ‘IF-THEN’ rules. The output of the system was stratification of patients into four risk levels from ‘No’ to ‘High’ where a number was also allocated to them as a nutritional risk grade. All patients (121 people admitted during implementing the system participated in testing the model. The classification tests were used to measure the CNRS fuzzy model performance. IBM SPSS version 21 was utilized as a tool for data analysis with α = 0.05 as a significance level. Results: Results showed that sensitivity, specificity, accuracy, and precision of the fuzzy model performance were 91.67% (±4.92, 76% (±7.6, 88.43% (±5.7, and 93.62% (±4.32, respectively. Instant performance on admission and very low probability of mistake in predicting malnutrition risk level may justify using the model in hospitals. Conclusion: To conclude, the fuzzy model-screening tool is based on multiple nutritional risk factors, having the capability of classifying inpatients into several nutritional risk levels and identifying the level of required nutritional intervention.
DURSUN, Mahir; ÖZDEN, Semih
2008-01-01
Switched reluctance motors (SRM) can be use at adjustable speed driver and these motors have high start up torque, high efficiency and simple construction. In this study, an SRM which have 8/6 poles, four phase and 3,44 kW power was used to drive an elevator load. Carrying capacity of elevator is 320 kg. Two PIC 18F452 microcontrollers was used to manage an elevator system and motor control. Motor speed was controlled by PI (proportional+integral) and Fuzzy Logic Control (FLC) method. Elevato...
Ahmed Abdulghani Taha; Mohammad Abdulghani Taha
2016-01-01
This study aims at being acquainted with the using the body fat percentage (%BF) with body Mass Index (BMI) as input parameters in fuzzy logic decision support system to predict properly the lifted weight for students at weightlifting class lift according to his abilities instead of traditional manner. The sample included 53 male students (age = 21.38 ± 0.71 yrs, height (Hgt) = 173.17 ± 5.28 cm, body weight (BW) = 70.34 ± 7.87.6 kg, Body mass index (BMI) 23.42 ± 2.06 kg.m...
Fuzzy-Logic Subsumption Controller for Home Energy Management Systems
Energy Technology Data Exchange (ETDEWEB)
Ainsworth, Nathan; Johnson, Brian; Lundstrom, Blake
2015-10-06
Home Energy Management Systems (HEMS) are controllers that manage and coordinate the generation, storage, and loads in a home. These controllers are increasingly necessary to ensure that increasing penetrations of distributed energy resources are used effectively and do not disrupt the operation of the grid. In this paper, we propose a novel approach to HEMS design based on behavioral control methods, which do not require accurate models or predictions and are very responsive to changing conditions. We develop a proof-of-concept behavioral HEMS controller and show by simulation on an example home energy system that it capable of making context-dependent tradeoffs between goals under challenging conditions.
Efficient Fuzzy Logic Controller for Magnetic Levitation Systems
African Journals Online (AJOL)
Akorede
electromagnet coil is adequately modeled by a series resistor- inductor combination. The inductor includes that of the object when suspended as described. The circuit is shown in Figure. 3. The analysis can be simplify by assuming that the system is properly designed, such that the ball (object) remains closed.
Studi Eksperimental Pengontrolan Air Conditioning System Dengan Fuzzy Logic Control
Directory of Open Access Journals (Sweden)
Sudirman -
2012-11-01
Full Text Available Electrical energy available in Indonesia at this time is not yet sufficient for all existing activities, this can be proved byfrequent occurrence of blackouts in several areas in Indonesia. It is necessary for a saving in electrical energy consumptionin all sectors, it is one of the refrigeration system. Research was conducted by testing AC (3 HP / 3 phase using 2 differentcontrol systems, namely conventional control and FLC. Testing is done by placing the indoor units in cold storage room.Each test performed with varying load in the test room, ie no light burden, lamp 1000 Watt, and lamp 2000 Watt. Testingusing a conventional control system set point temperature 26 ° C and 3 variations of the differential is 1 , 2 and 3 , the FLCusing the temperature setting point 26 ° C. From this research we can conclude that the application of FLC system produceselectric energy consumption of the lowest compared to conventional control in this case is the differential 1. FLC applicationof electrical energy consumption at load 1000 Watt lower 11% and the load 2000 Watt 4% lower compared withconventional control in diffrensial 1.
Incorporating fuzzy data and logical relations in the design of expert systems for nuclear reactors
International Nuclear Information System (INIS)
Guth, M.A.S.
1987-01-01
This paper applies the method of assigning probability in Dempster-Shafer Theory (DST) to the components of rule-based expert systems used in the control of nuclear reactors. Probabilities are assigned to premises, consequences, and rules themselves. This paper considers how uncertainty can propagate through a system of Boolean equations, such as fault trees or expert systems. The probability masses assigned to primary initiating events in the expert system can be derived from observing a nuclear reactor in operation or based on engineering knowledge of the reactor parts. Use of DST mass assignments offers greater flexibility to the construction of expert systems in two important respects. First, DST mass assignments have the advantage over classical probability methods of accommodating when necessary uncommitted probability assignments. Thus the DST probability framework can incorporate expert system inputs from imprecise or fuzzy data. Second, DST applied to the Boolean rules themselves leads to a probabilistic logic, where a given rule may be valid with probability less than unity: fuzzy logical rules
Robust fuzzy logic stabilization with disturbance elimination.
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.
Fuzzy Logic and Arithmetical Hierarchy III
Czech Academy of Sciences Publication Activity Database
Hájek, Petr
2001-01-01
Roč. 68, č. 1 (2001), s. 129-142 ISSN 0039-3215 R&D Projects: GA AV ČR IAA1030004 Institutional research plan: AV0Z1030915 Keywords : fuzzy logic * basic fuzzy logic * Lukasiewicz logic * Godel logic * product logic * arithmetical hierarchy Subject RIV: BA - General Mathematics
Fuzzy logic guided inverse treatment planning
International Nuclear Information System (INIS)
Yan Hui; Yin Fangfang; Guan Huaiqun; Kim, Jae Ho
2003-01-01
A fuzzy logic technique was applied to optimize the weighting factors in the objective function of an inverse treatment planning system for intensity-modulated radiation therapy (IMRT). Based on this technique, the optimization of weighting factors is guided by the fuzzy rules while the intensity spectrum is optimized by a fast-monotonic-descent method. The resultant fuzzy logic guided inverse planning system is capable of finding the optimal combination of weighting factors for different anatomical structures involved in treatment planning. This system was tested using one simulated (but clinically relevant) case and one clinical case. The results indicate that the optimal balance between the target dose and the critical organ dose is achieved by a refined combination of weighting factors. With the help of fuzzy inference, the efficiency and effectiveness of inverse planning for IMRT are substantially improved
A Brief History of Fuzzy Logic
Directory of Open Access Journals (Sweden)
Angel Garrido
2012-04-01
Full Text Available
The problems of uncertainty, imprecision and vagueness have been discussed for many years. These problems have been major topics in philosophical circles with much debate, in particular, about the nature of vagueness and the ability of traditional Boolean logic to cope with concepts and perceptions that are imprecise or vague. The Fuzzy Logic (which is usually translated into Castilian by “Lógica Borrosa”, or “Lógica Difusa”, but also by “Lógica Heurística” can be considered a bypass-valued logics (Multi-valued Logic, MVL, its acronym in English. It is founded on, and is closely related to-Fuzzy Sets Theory, and successfully applied on Fuzzy Systems. You might think that fuzzy logic is quite recent and what has worked for a short time, but its origins date back at least to the Greek philosophers and especially Plato (428-347 B.C.. It even seems plausible
to trace their origins in China and India. Because it seems that they were the first to consider that all things need not be of a certain type or quit, but there are a stopover between. That is, be the pioneers in considering that there may be varying degrees of truth and falsehood. In case of colors, for example, between white and black there is a whole infinite scale: the shades of gray. Some recent theorems show that in principle fuzzy logic can be used to model any continuous system, be it based
in AI, or physics, or biology, or economics, etc. Investigators in many fields may find that fuzzy, commonsense models are more useful, and many more accurate than are standard mathematical ones. We analyze here the history and development of this problem: Fuzziness, or “Borrosidad” (in Castilian, essential to work with Uncertainty.
Fuzzy logic in autonomous orbital operations
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.
Directory of Open Access Journals (Sweden)
C. Boldisor
2009-12-01
Full Text Available A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed.
Fuzzy logic-based mobile computing system for hand rehabilitation after neurological injury.
Chiu, Yu-Hsien; Chen, Tien-Wen; Chen, Yenming J; Su, Ching-I; Hwang, Kao-Shing; Ho, Wen-Hsien
2018-01-01
Effective neurological rehabilitation requires long term assessment and treatment. The rapid progress of virtual reality-based assistive technologies and tele-rehabilitation has increased the potential for self-rehabilitation of various neurological injuries under clinical supervision. The objective of this study was to develop a fuzzy inference mechanism for a smart mobile computing system designed to support in-home rehabilitation of patients with neurological injury in the hand by providing an objective means of self-assessment. A commercially available tablet computer equipped with a Bluetooth motion sensor was integrated in a splint to obtain a smart assistive device for collecting hand motion data, including writing performance and the corresponding grasp force. A virtual reality game was also embedded in the smart splint to support hand rehabilitation. Quantitative data obtained during the rehabilitation process were modeled by fuzzy logic. Finally, the improvement in hand function was quantified with a fuzzy rule database of expert opinion and experience. Experiments in chronic stroke patients showed that the proposed system is applicable for supporting in-home hand rehabilitation. The proposed virtual reality system can be customized for specific therapeutic purposes. Commercial development of the system could immediately provide stroke patients with an effective in-home rehabilitation therapy for improving hand problems.
FPA Tuned Fuzzy Logic Controlled Synchronous Buck Converter for a Wave/SC Energy System
Directory of Open Access Journals (Sweden)
SAHIN, E.
2017-02-01
Full Text Available This paper presents a flower pollination algorithm (FPA tuned fuzzy logic controlled (FLC synchronous buck converter (SBC for an integrated wave/ supercapacitor (SC hybrid energy system. In order to compensate the irregular wave effects on electrical side of the wave energy converter (WEC, a SC unit charged by solar panels is connected in parallel to the WEC system and a SBC is controlled to provide more reliable and stable voltage to the DC load. In order to test the performance of the designed FLC, a classical proportional-integral-derivative (PID controller is also employed. Both of the controllers are optimized by FPA which is a pretty new optimization algorithm and a well-known optimization algorithm of which particle swarm optimization (PSO to minimize the integral of time weighted absolute error (ITAE performance index. Also, the other error-based objective functions are considered. The entire energy system and controllers are developed in Matlab/Simulink and realized experimentally. Real time applications are done through DS1104 Controller Board. The simulation and experimental results show that FPA tuned fuzzy logic controller provides lower value performance indices than conventional PID controller by reducing output voltage sags and swells of the wave/SC energy system.
Fuzzy Logic in Medicine and Bioinformatics
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Angela Torres
2006-01-01
Full Text Available 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.
Fuzzy logic control of stand-alone photovoltaic system with battery storage
Lalouni, S.; Rekioua, D.; Rekioua, T.; Matagne, E.
Photovoltaic energy has nowadays an increased importance in electrical power applications, since it is considered as an essentially inexhaustible and broadly available energy resource. However, the output power provided via the photovoltaic conversion process depends on solar irradiation and temperature. Therefore, to maximize the efficiency of the photovoltaic energy system, it is necessary to track the maximum power point of the PV array. The present paper proposes a maximum power point tracker (MPPT) method, based on fuzzy logic controller (FLC), applied to a stand-alone photovoltaic system. It uses a sampling measure of the PV array power and voltage then determines an optimal increment required to have the optimal operating voltage which permits maximum power tracking. This method carries high accuracy around the optimum point when compared to the conventional one. The stand-alone photovoltaic system used in this paper includes two bi-directional DC/DC converters and a lead-acid battery bank to overcome the scare periods. One converter works as an MPP tracker, while the other regulates the batteries state of charge and compensates the power deficit to provide a continuous delivery of energy to the load. The Obtained simulation results show the effectiveness of the proposed fuzzy logic controller.
Combining fuzzy mathematics with fuzzy logic to solve business management problems
Vrba, Joseph A.
1993-12-01
Fuzzy logic technology has been applied to control problems with great success. Because of this, many observers fell that fuzzy logic is applicable only in the control arena. However, business management problems almost never deal with crisp values. Fuzzy systems technology--a combination of fuzzy logic, fuzzy mathematics and a graphical user interface--is a natural fit for developing software to assist in typical business activities such as planning, modeling and estimating. This presentation discusses how fuzzy logic systems can be extended through the application of fuzzy mathematics and the use of a graphical user interface to make the information contained in fuzzy numbers accessible to business managers. As demonstrated through examples from actual deployed systems, this fuzzy systems technology has been employed successfully to provide solutions to the complex real-world problems found in the business environment.
From Fuzzy Logic to Fuzzy Mathematics: A Methodological Manifesto
Czech Academy of Sciences Publication Activity Database
Běhounek, Libor; Cintula, Petr
2006-01-01
Roč. 157, č. 5 (2006), s. 642-646 ISSN 0165-0114 R&D Projects: GA AV ČR KJB100300502 Institutional research plan: CEZ:AV0Z10300504 Keywords : non-classical logics * formal fuzzy logic * formal fuzzy mathematics * high-order fuzzy logic Subject RIV: BA - General Mathematics Impact factor: 1.181, year: 2006
MATLAB Graphical User Interface based Fuzzy Logic Controllers for Liquid Level Control System
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Immanuel J.
2013-01-01
Full Text Available This paper presents the design and development of MATLAB graphical user interface (GUI based fuzzy logic controller (FLC and integrated fuzzy logic controller (IFLC for liquid level control system. The main objective of this work is to design and develop a MATLAB based GUI for liquid level control system. In this application, the inflow of water to the tank is controlled. The necessary algorithm is developed in MATLAB. The liquid level in a cylindrical tank is measured by using differential-pressure sensor (SX05DN. The sensor converts change in water level into change in resistance which is further converted into voltage and applied to an instrumentation amplifier. The computer acquires voltage through Analog to Digital–Digital to Analog Converter (AD-DA board designed indigenously for this application. The Digital Input/ Output and Timer (DIOT card is used to interface AD-DA board with PC. To control liquid level, MATLAB/GUI based PIDC, FLC, and IFLC is developed. The performance of these controllers is tested for a step input of 15 cm. The results show that IFLC exhibits the best response in terms of less rise time and settling time, negligible overshoot and undershoot. The proposed MATLAB/GUI provides various graphical user interfaces for easy access, tuning, and visual display of performance of controllers implemented.
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.
Jha, Mohit; Shukla, Shailja
2014-01-01
Traffic is the chief puzzle problem which every country faces because of the enhancement in number of vehicles throughout the world, especially in large urban towns. Hence the need arises for simulating and optimizing traffic control algorithms to better accommodate this increasing demand. Fuzzy optimization deals with finding the values of input parameters of a complex simulated system which result in desired output. This paper presents a MATLAB simulation of fuzzy logic traffic controller f...
The first order fuzzy predicate logic (I)
International Nuclear Information System (INIS)
Sheng, Y.M.
1986-01-01
Some analysis tools of fuzzy measures, Sugeno's integrals, etc. are introduced into the semantic of the first order predicate logic to explain the concept of fuzzy quantifiers. The truth value of a fuzzy quantification proposition is represented by Sugeno's integral. With this framework, several important notions of formation rules, fuzzy valutions and fuzzy validity are discussed
Expert System for Competences Evaluation 360° Feedback Using Fuzzy Logic
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Alberto Alfonso Aguilar Lasserre
2014-01-01
Full Text Available Performance evaluation (PE is a process that estimates the employee overall performance during a given period, and it is a common function carried out inside modern companies. PE is important because it is an instrument that encourages employees, organizational areas, and the whole company to have an appropriate behavior and continuous improvement. In addition, PE is useful in decision making about personnel allocation, productivity bonuses, incentives, promotions, disciplinary measures, and dismissals. There are many performance evaluation methods; however, none is universal and common to all companies. This paper proposes an expert performance evaluation system based on a fuzzy logic model, with competences 360° feedback oriented to human behavior. This model uses linguistic labels and adjustable numerical values to represent ambiguous concepts, such as imprecision and subjectivity. The model was validated in the administrative department of a real Mexican manufacturing company, where final results and conclusions show the fuzzy logic method advantages in comparison with traditional 360° performance evaluation methodologies.
Fuzzy logic based sensor performance evaluation of vehicle mounted metal detector systems
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.
Morpho (?) phono (?) logical fuzzy edges
African Journals Online (AJOL)
Open Access DOWNLOAD FULL TEXT Subscription or Fee Access. Morpho (?) phono (?) logical fuzzy edges: The case of {-/}/{-/U/} semantic (?) contrast in Shona. K. G. Mkangwanwi. Abstract. (ZAMBEZIA: Journal of Humanities of the Univ of Zimbabwe, 2000 27(1): 47-54). Full Text: EMAIL FULL TEXT EMAIL FULL TEXT
Modeling and Control PV-Wind Hybrid System Based On Fuzzy Logic Control Technique
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Doaa M. Atia
2012-09-01
Full Text Available As energy demands around the world increase, the need for a renewable energy sources that will not harm the environment is increased. The overall objective of renewable energy systems is to obtain electricity that is cost competitive and even advantageous with respect to other energy sources. The optimal design of the renewable energy system can significantly improve the economical and technical performance of power supply. This paper presents the power management control using fuzzy logic control technique. Also, a complete mathematical modeling and MATLAB SIMULINK model for the proposed the electrical part of an aquaculture system is implemented to track the system performance. The simulation results show that, the feasibility of control technique.
Gentili, Pier Luigi; Rightler, Amanda L; Heron, B Mark; Gabbutt, Christopher D
2016-01-25
Photochromic fuzzy logic systems have been designed that extend human visual perception into the UV region. The systems are founded on a detailed knowledge of the activation wavelengths and quantum yields of a series of thermally reversible photochromic compounds. By appropriate matching of the photochromic behaviour unique colour signatures are generated in response differing UV activation frequencies.
Chen, Guanrong
2005-01-01
Introduction to Fuzzy Systems provides students with a self-contained introduction that requires no preliminary knowledge of fuzzy mathematics and fuzzy control systems theory. Simplified and readily accessible, it encourages both classroom and self-directed learners to build a solid foundation in fuzzy systems. After introducing the subject, the authors move directly into presenting real-world applications of fuzzy logic, revealing its practical flavor. This practicality is then followed by basic fuzzy systems theory. The book also offers a tutorial on fuzzy control theory, based mainly on th
Introduction to fuzzy logic using Matlab
Sivanandam, SN; Deepa, S N
2006-01-01
Fuzzy Logic, at present is a hot topic, among academicians as well various programmers. This book is provided to give a broad, in-depth overview of the field of Fuzzy Logic. The basic principles of Fuzzy Logic are discussed in detail with various solved examples. The different approaches and solutions to the problems given in the book are well balanced and pertinent to the Fuzzy Logic research projects. The applications of Fuzzy Logic are also dealt to make the readers understand the concept of Fuzzy Logic. The solutions to the problems are programmed using MATLAB 6.0 and the simulated results are given. The MATLAB Fuzzy Logic toolbox is provided for easy reference.
International Nuclear Information System (INIS)
Guth, M.A.S.
1987-01-01
This paper presents an expert system for diagnosing problems in the interface between the heat exchanger and the core of a nuclear power plant for a hypothetical pressurized water reactor (PWR). The expert system has a production rule backward-chaining-based architecture, and the knowledge base incorporates four kinds of information. First, the structural relationship between causes and consequences is given by nuclear engineering experts. Second, numerical values for the initiating events can be taken from observed performance of the reactor under normal conditions. Third, the causes of particular events are ranked in order of their likelihood based on a combination of a priori knowledge about the reactor design and actual data on the incidence of component failures. Fourth, Bellman-Zadeh Fuzzy Logic is introduced to maintain truth values for expert system rules that hold with varying degrees of certainty
An Embedded Fuzzy Logic Based Application for Density Traffic Control System
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Ajao Lukman Adewale
2018-02-01
Full Text Available The control of density traffic at cross junction road usually manned by human efforts or implementation of automatic traffic light system. This system seem and proves to be inefficient with some challenges. The major constraints of this traffic control are as a result of the inability of most traffic control systems to assign appropriate waiting time for vehicles based on the lane density. Also with little or no consideration for pedestrians, emergency and security agents priorities. In view of this, an intelligent density traffic control system using (fuzzy logic which is capable of providing priority to the road users based on the density and emergency situations was developed and presented in this paper. This system will obtain the approximate amount of vehicle and presence of pedestrians respectfully on each lane with help of Infrared Sensors (IR and siren detection system for emergency and security road users. The working principle of this system depending on the logic inputs rules given into the processing unit by the (sensors, S1 and S2 which helps the system to generates a timing sequence that best suit the number of vehicles and pedestrians available on the lane at point in time.
Neurocontrol and fuzzy logic: Connections and designs
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.
Logical Characterisation of Ontology Construction using Fuzzy Description Logics
DEFF Research Database (Denmark)
Badie, Farshad; Götzsche, Hans
Ontologies based on Description Logics (DLs) have proved to be effective in formally sharing knowledge across semantic technologies, e.g. Semantic Web, Natural Language Processing, Text Analytics, Business intelligence. Our main goal is analysing ontology construction considering vagueness. We have...... had the extension of ontologies with Fuzzy Logic capabilities which plan to make proper backgrounds for ontology driven reasoning and argumentation on vague and imprecise domains. This presentation conceptualises learning from fuzzy classes using the Inductive Logic Programming framework. Then......, employs Description Logics in characterising and analysing fuzzy statements. And finally, provides a conceptual framework describing fuzzy concept learning in ontologies using the Inductive Logic Programming....
Multi-Source Sensor Fusion for Small Unmanned Aircraft Systems Using Fuzzy Logic
Cook, Brandon; Cohen, Kelly
2017-01-01
As the applications for using small Unmanned Aircraft Systems (sUAS) beyond visual line of sight (BVLOS) continue to grow in the coming years, it is imperative that intelligent sensor fusion techniques be explored. In BVLOS scenarios the vehicle position must accurately be tracked over time to ensure no two vehicles collide with one another, no vehicle crashes into surrounding structures, and to identify off-nominal scenarios. Therefore, in this study an intelligent systems approach is used to estimate the position of sUAS given a variety of sensor platforms, including, GPS, radar, and on-board detection hardware. Common research challenges include, asynchronous sensor rates and sensor reliability. In an effort to realize these challenges, techniques such as a Maximum a Posteriori estimation and a Fuzzy Logic based sensor confidence determination are used.
POWER SYSTEM PLANNING USING ANN WITH FUZZY LOGIC AND WAVELET ANALYSIS
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V. Dharma Dharshin
2016-10-01
Full Text Available The electricity load required for the forthcoming years are predetermined by means of power system planning. Accuracy is the crucial factor that must be taken care of in the power system planning. Electricity is generally volatile, that is it changes and hence appropriate estimation must be done without leading to overestimation or underestimation. The aim of the project is to do appropriate power estimation with the help of the economic factors. The 9 input factors used are GDP, industry, imports, CO2 emission, exports, services, manufacturing, population, per capita consumption. The proposed methodology is done by means of Neural Network concept and Wavelet Analysis. Regression Analysis is also performed and the comparisons are done using Fuzzy Logic. The nonlinear model, Artificial Neural Network and the Wavelet Analysis are found to be more accurate and effective.
An Asymmetrical Fuzzy-Logic-Control-Based MPPT Algorithm for Photovoltaic Systems
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Chun-Liang Liu
2014-04-01
Full Text Available In this paper, a fuzzy-logic-control (FLC based maximum power point tracking (MPPT algorithm for photovoltaic (PV systems is proposed. The power variation and output voltage variation are chosen as inputs of the proposed FLC, which simplifies the calculation. Compared with the conventional perturb and observe (P&O method, the proposed FLC-based MPPT can simultaneously improve the dynamic and steady state performance of the PV system. To further improve the performance of the proposed method, an asymmetrical membership function (MF concept is also proposed. Two design procedures are proposed to determine the universe of discourse (UOD of the input MF. Comparing with the proposed symmetrical FLC-based MPPT method, the transient time and the MPPT tracking accuracy are further improved by 42.8% and 0.06%, respectively.
APPLICATION OF FUZZY LOGIC TOOLBOX FOR MODELLING FUZZY LOGIC CONTROLLERS
Olesiak, Krzysztof
2017-01-01
Computer technology, which has been developing very fast in the recent years, can be also fruitfully applied in teaching. For example, the software package Matlab is highly useful in teaching students at Bachelor Programs of Electrical Engineering and Automatics and Robotics. Fuzzy Logic Toolbox of the Matlab package can be used for designing and modelling controllers. Thanks to a large number of pre-defined elements available in the libraries, it is possible to create even highly complicated...
Sunal, Cynthia Szymanski; Karr, Charles L.; Sunal, Dennis W.
2003-01-01
Students' conceptions of three major artificial intelligence concepts used in the modeling of systems in science, fuzzy logic, neural networks, and genetic algorithms were investigated before and after a higher education science course. Students initially explored their prior ideas related to the three concepts through active tasks. Then,…
A study of fuzzy logic ensemble system performance on face recognition problem
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.
Fuzzy Logic Inference System for Determining The Quality Assesment of Student’s Learning ICT
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Agus Pamuji
2017-05-01
Full Text Available The Assesment that held in the school is one of the learning process in education who do it by teacher. One of the course that exemined is Computer Application. In the computer application have 3 topic, they are Microsoft Word, Microsoft Excel, Microsoft Power Point. The assesment for student’s at politecnic about learning computer application have 3 criteria in the selection. First of all, the students have ability to operate computer system generaly, it has understanding the formula on microsoft excel, the students have skill toward any application. In this study, fuzzy logic used for determining the quality assesment of stundent’s learning Information and Comunication Technology (ICT as a tools to analyze any constraint that are known as min-max method. As a result, we have found that the students have good for analyzing in the application from the each question or case of study when the course it has been examined.
Design and Construction of Intelligent Traffic Light Control System Using Fuzzy Logic
Lin, Htin; Aye, Khin Muyar; Tun, Hla Myo; Theingi, Naing, Zaw Min
2008-10-01
Vehicular travel is increasing throughout the world, particularly in large urban areas. Therefore the need arises for simulation and optimizing traffic control algorithms to better accommodate this increasing demand. This paper presents a microcontroller simulation of intelligent traffic light controller using fuzzy logic that is used to change the traffic signal cycles adaptively at a two-way intersection. This paper is an attempt to design an intelligent traffic light control systems using microcontrollers such as PIC 16F84A and PIC 16F877A. And then traffic signal can be controlled depending upon the densities of cars behind green and red lights of the two-way intersection by using sensors and detectors circuits.
Integrating System Dynamic and Fuzzy Logic for Economic Assessment of BOT Projects
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Farnad Nasirzadeh
2016-03-01
Full Text Available The selection of the most appropriate project for the investment is one of the most important decisions that should be made by the private investors. This problem is of vital importance in BOT projects, where the total investment as well as the investor's profit should be recovered by the project's income. There are several approaches proposed by the researchers to determine the best economical project in comparison to other projects. The previous researches, however, faced with some major defects. As an example, the effects of various factors affecting the project evaluation process as well as the existing risks and uncertainties are not taken into account. In this research, economic assessment of BOT projects is performed by integrating system dynamic simulation approach and fuzzy logic. For this purpose, first the project's NPV is modeled considering all the influencing factors qualitatively. The relationships that exist between different factors are then determined and the quantitative model is built. Using the developed model, the value of NPV is simulated considering the effects of all the influencing factors and the existing uncertainties. Finally, the value of project's NPV is determined as a triangular fuzzy number. Finally, the best alternative project is selected by comparing the simulated values of NPV. To evaluate the capabilities and performance of the proposed model, the project economical evaluation is performed for two highway projects and the best project is selected.
Gemitzi, Alexandra; Tsihrintzis, Vassilios A.; Voudrias, Evangelos; Petalas, Christos; Stravodimos, George
2007-01-01
This study presents a methodology for siting municipal solid waste landfills, coupling geographic information systems (GIS), fuzzy logic, and multicriteria evaluation techniques. Both exclusionary and non-exclusionary criteria are used. Factors, i.e., non-exclusionary criteria, are divided in two distinct groups which do not have the same level of trade off. The first group comprises factors related to the physical environment, which cannot be expressed in terms of monetary cost and, therefore, they do not easily trade off. The second group includes those factors related to human activities, i.e., socioeconomic factors, which can be expressed as financial cost, thus showing a high level of trade off. GIS are used for geographic data acquisition and processing. The analytical hierarchy process (AHP) is the multicriteria evaluation technique used, enhanced with fuzzy factor standardization. Besides assigning weights to factors through the AHP, control over the level of risk and trade off in the siting process is achieved through a second set of weights, i.e., order weights, applied to factors in each factor group, on a pixel-by-pixel basis, thus taking into account the local site characteristics. The method has been applied to Evros prefecture (NE Greece), an area of approximately 4,000 km2. The siting methodology results in two intermediate suitability maps, one related to environmental and the other to socioeconomic criteria. Combination of the two intermediate maps results in the final composite suitability map for landfill siting.
Application of Interval Type-2 Fuzzy Logic System in Short Term Load Forecasting on Special Days
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Agus Dharma
2011-05-01
Full Text Available This paper presents the application of Interval Type-2 fuzzy logic systems (Interval Type-2 FLS in short term load forecasting (STLF on special days, study case in Bali Indonesia. Type-2 FLS is characterized by a concept called footprint of uncertainty (FOU that provides the extra mathematical dimension that equips Type-2 FLS with the potential to outperform their Type-1 counterparts. While a Type-2 FLS has the capability to model more complex relationships, the output of a Type-2 fuzzy inference engine needs to be type-reduced. Type reduction is used by applying the Karnik-Mendel (KM iterative algorithm. This type reduction maps the output of Type-2 FSs into Type-1 FSs then the defuzzification with centroid method converts that Type-1 reduced FSs into a number. The proposed method was tested with the actual load data of special days using 4 days peak load before special days and at the time of special day for the year 2002-2006. There are 20 items of special days in Bali that are used to be forecasted in the year 2005 and 2006 respectively. The test results showed an accurate forecasting with the mean average percentage error of 1.0335% and 1.5683% in the year 2005 and 2006 respectively.
Mathematical Fuzzy Logic - State of Art 2001
Czech Academy of Sciences Publication Activity Database
Hájek, Petr
2003-01-01
Roč. 24, - (2003), s. 71-89 ISSN 0103-9059. [WOLLIC'2001. Brasília, 31.07.2001-03.08.2001] R&D Projects: GA MŠk LN00A056 Keywords : fuzzy logic * many valued logic * basic fuzzy logic BL Subject RIV: BA - General Mathematics http://www.mat.unb.br/~matcont/24_4.pdf
Modeling Nonlinear Systems by a Fuzzy Logic Neural Network Using Genetic Algorithms
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Abdel-Fattah Attia
2001-01-01
Full Text Available The main aim of this work is to optimize the parameters of the constrained membership function of the Fuzzy Logic Neural Network (FLNN. The constraints may be an indirect definition of the search ranges for every membership shape forming parameter based on 2nd order fuzzy set specifications. A particular method widely applicable in solving global optimization problems is introduced. This approach uses a Linear Adapted Genetic Algorithm (LAGA to optimize the FLNN parameters. In this paper the derivation of a 2nd order fuzzy set is performed for a membership function of Gaussian shape, which is assumed for the neuro-fuzzy approach. The explanation of the optimization method is presented in detail on the basis of two examples.
Modeling of Kefir Production with Fuzzy Logic
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Hüseyin Nail Akgül
2014-06-01
Full Text Available The fermentation is ended with pH 4.6 values in industrial production of kefir. In this study, the incubation temperature, the incubation time and inoculums of culture were chose as variable parameters of kefir. In conventional control systems, the value of pH can be found by trial method. In these systems, if the number of input parameters is greater, the method of trial and error creates a system dependent on the person as well as troublesome. Fuzzy logic can be used in such cases. Modeling studies with this fuzzy logic control are examined in two portions. The first part consists of fuzzy rules and membership functions, while the second part consists of clarify. Kefir incubation temperature between 20 and 25°C, the incubation period between 18 to 22 hours and the inoculum ratio of culture between 1-5% are selected for optimum production conditions. Three separate fuzzy sets (triangular membership function are used to blur the incubation temperature, the incubation time and the inoculum ratio of culture. Because the membership function numbers belonging to the the input parameters are 3 units, 3x3x3=27 line rule is obtained by multiplying these numbers. The table of fuzzy rules was obtained using the method of Mamdani. The membership function values were determined by the method of average weight using three trapezoidal area of membership functions created for clarification. The success of the system will be found, comparing the numerical values obtained with pH values that should be. Eventually, to achieve the desired pH value of 4.6 in the production of kefir, with the using of fuzzy logic, the workload of people will be decreased and the productivity of business can be increased. In this case, it can be provided savings in both cost and time.
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Julian Alexander Melo Rodriguez
2016-09-01
Full Text Available Objectives: This paper presents a new methodology for analyzing the vulnerability of power systems including uncertainty in some variables. Method: The methodology optimizes a Bi-level mixed integer model. Costs associated with power generation and load shedding are minimized at the lowest level whereas at the higher level the damage in the power system, represented by the load shedding, is maximized. Fuzzy logic type 2 is used to model the uncertainty in both linguistic variables and numeric variables. The linguistic variables model the factors of the geographical environment while numeric variables model parameters of the power system. Results: The methodology was validated by using a modified IEEE RTS-96 test system. The results show that by including particularities of the geographical environment different vulnerabilities are detected in the power system. Moreover, it was possible to identify that the most critical component is the line 112-123 because it had 16 attacks in 18 scenarios, and that the maximum load shedding of the system varies from 145 to 1258 MW. Conclusions: This methodology can be used to coordinate and refine protection plans of the power system infrastructure. Funding: EMC-UN research group.
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Márcio Mendonça
2015-10-01
Full Text Available In this work, it is analyzed a multivariate system control of an alcoholic fermentation process with no minimum phase. The control is made with PID classic controllers associated with a supervisory system based on Fuzzy Systems. The Fuzzy system, a priori, send set-points to PID controllers, but also adds protection functions, such as if the biomass valued is at zero or very close. The Fuzzy controller changes the campaign to prevent or mitigate the paralyzation of the process. Three control architectures based on Fuzzy Control Systems are presented and compared in performance with classic control in different campaigns. The third architecture, in particular, adds an adaptive function. A brief summary of Fuzzy theory and correlated works will be presented. And, finally simulations results, conclusions and future works end the article.
A fuzzy logic approach toward solving the analytic enigma of health system financing.
Chernichovsky, Dov; Bolotin, Arkady; de Leeuw, David
2003-09-01
Improved health, equity, macroeconomic efficiency, efficient provision of care, and client satisfaction are the common goals of any health system. The relative significance of these goals varies, however, across nations, communities and with time. As for health care finance, the attainment of these goals under varying circumstances involves alternative policy options for each of the following elements: sources of finance, allocation of finance, payment to providers, and public-private mix. The intricate set of multiple goals, elements and policy options defies human reasoning, and, hence, hinders effective policymaking. Indeed, "health system finance" is not amenable to a clear set of structural relationships. Neither is there a universe that can be subject to statistical scrutiny: each health system is unique. "Fuzzy logic" models human reasoning by managing "expert knowledge" close to the way it is handled by human language. It is used here for guiding policy making by a systematic analysis of health system finance. Assuming equal welfare weights for alternative goals and mutually exclusive policy options under each health-financing element, the exploratory model we present here suggests that a German-type health system is best. Other solutions depend on the welfare weights for system goals and mixes of policy options.
Fuzzy logic for business, finance, and management
Bojadziev, George
1997-01-01
This is an interdisciplinary book for knowledge workers in business, finance, management, and socio-economic sciences. It provides a guide to and techniques for forecasting, decision making, conclusions, and evaluations in an environment involving uncertainty, vagueness, and impression. Traditional modeling techniques do not capture the nature of complex systems especially when humans are involved. Fuzzy logic provides effective tools for dealing with such systems. Emphasis is on applications presented in case studies including Time Forecasting for Project Management, New Product Pricing, Clie
Fuzzy Logic Based Controller for Maintaining Human Comfort within Intelligent Building System
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Nasrodin .T. Mustapha, Momoh J. E. Salami, Nazim and M. Nasiri
2012-10-01
Full Text Available This paper presents an intelligent control approach for air handling unit (AHU which is an integral part of heat, ventilation, and air conditioning (HVAC system. In the past years various control design for HVAC have been proposed as this system remarkably consumes very high energy. But most of the proposed designs were focused on the control flow of heat-transfer medium such as chilled or heated water while the importance of the efficient mixture of outdoor and indoor enthalpies is sometimes ignored. These enthalpies invariably determine the best strategy to overcome thermal load in a controlled environment to satisfy human comfort, hence a control design strategy must be able to efficiently regulate the flow and mixture of outdoor and indoor enthalpies by a proper control of AHU dampers and fans. This approach requires sensors to measure temperature and relative humidity of both outdoor and indoor environments. However, unpredictable level of disturbances coming from many sources including heat generated by occupants, electrical items and air leaking and the continuous changes of outdoor enthalpy makes it difficult to model the process. Consequently, conventional controllers are not suitable, hence the use of fuzzy logic controller (FLC is proposed in this paper. This proposed controller operates in a master and slave control loop so as to control the AHU dampers and fans with adjustable output membership function whilst at the same time a scaling-factor method is used to drive the master operation. To implement the proposed system, a small scale prototype has been designed and fabricated. This prototype is an AHU model which consists of ductwork, temperature and humidity sensors, dampers, air cooling and heating systems. A small box is used as a conditioning space in which a room temperature is measured. The control algorithm is programmed using National Instrument (NI LabVIEW and executed using NI FieldPoint. Experimental results reveal that
Rohini, G; Jamuna, V
This work aims at improving the dynamic performance of the available photovoltaic (PV) system and maximizing the power obtained from it by the use of cascaded converters with intelligent control techniques. Fuzzy logic based maximum power point technique is embedded on the first conversion stage to obtain the maximum power from the available PV array. The cascading of second converter is needed to maintain the terminal voltage at grid potential. The soft-switching region of three-stage converter is increased with the proposed phase-locked loop based control strategy. The proposed strategy leads to reduction in the ripple content, rating of components, and switching losses. The PV array is mathematically modeled and the system is simulated and the results are analyzed. The performance of the system is compared with the existing maximum power point tracking algorithms. The authors have endeavored to accomplish maximum power and improved reliability for the same insolation of the PV system. Hardware results of the system are also discussed to prove the validity of the simulation results.
A fuzzy logic urea dosage controller design for two-cell selective catalytic reduction systems.
You, Kun; Wei, Lijiang; Jiang, Kai
2017-12-22
Diesel engines have dominated in the heavy-duty vehicular and marine power source. However, the induced air pollution is a big problem. As people's awareness of environmental protection increasing, the emission regulations of diesel-engine are becoming more stringent. In order to achieve the emission regulations, the after-treatment system is a necessary choice. Specifically, the selective catalytic reduction (SCR) system has been widely applied to reduce the NO X emissions of diesel engine. Different from single-cell SCR systems, the two-cell systems have various benefits from the modeling and control perspective. In this paper, the urea dosage controller design for two-cell SCR systems was investigated. Firstly, the two-cell SCR modeling was introduced. Based on the developed model, the design procedure for the fuzzy logic urea dosage controller was well addressed. Secondly, simulations and comparisons were employed via an experimental verification of the whole vehicle simulator. And the results showed that the designed controller simultaneously achieved high NO X reduction rate and low tail-pipe ammonia slip. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
G. Rohini
2016-01-01
Full Text Available This work aims at improving the dynamic performance of the available photovoltaic (PV system and maximizing the power obtained from it by the use of cascaded converters with intelligent control techniques. Fuzzy logic based maximum power point technique is embedded on the first conversion stage to obtain the maximum power from the available PV array. The cascading of second converter is needed to maintain the terminal voltage at grid potential. The soft-switching region of three-stage converter is increased with the proposed phase-locked loop based control strategy. The proposed strategy leads to reduction in the ripple content, rating of components, and switching losses. The PV array is mathematically modeled and the system is simulated and the results are analyzed. The performance of the system is compared with the existing maximum power point tracking algorithms. The authors have endeavored to accomplish maximum power and improved reliability for the same insolation of the PV system. Hardware results of the system are also discussed to prove the validity of the simulation results.
Fuzzy logic prioritization of failures in a system failure mode, effects and criticality analysis
International Nuclear Information System (INIS)
Bowles, John B.; Pelaez, C.E.
1995-01-01
This paper describes a new technique, based on fuzzy logic, for prioritizing failures for corrective actions in a Failure Mode, Effects and Criticality Analysis (FMECA). As in a traditional criticality analysis, the assessment is based on the severity, frequency of occurrence, and detectability of an item failure. However, these parameters are here represented as members of a fuzzy set, combined by matching them against rules in a rule base, evaluated with min-max inferencing, and then defuzzified to assess the riskiness of the failure. This approach resolves some of the problems in traditional methods of evaluation and it has several advantages compared to strictly numerical methods: 1) it allows the analyst to evaluate the risk associated with item failure modes directly using the linguistic terms that are employed in making the criticality assessment; 2) ambiguous, qualitative, or imprecise information, as well as quantitative data, can be used in the assessment and they are handled in a consistent manner; and 3) it gives a more flexible structure for combining the severity, occurrence, and detectability parameters. Two fuzzy logic based approaches for assessing criticality are presented. The first is based on the numerical rankings used in a conventional Risk Priority Number (RPN) calculation and uses crisp inputs gathered from the user or extracted from a reliability analysis. The second, which can be used early in the design process when less detailed information is available, allows fuzzy inputs and also illustrates the direct use of the linguistic rankings defined for the RPN calculations
Najafi, Shahriar; Flintsch, Gerardo W; Khaleghian, Seyedmeysam
2016-05-01
Minimizing roadway crashes and fatalities is one of the primary objectives of highway engineers, and can be achieved in part through appropriate maintenance practices. Maintaining an appropriate level of friction is a crucial maintenance practice, due to the effect it has on roadway safety. This paper presents a fuzzy logic inference system that predicts the rate of vehicle crashes based on traffic level, speed limit, and surface friction. Mamdani and Sugeno fuzzy controllers were used to develop the model. The application of the proposed fuzzy control system in a real-time slippery road warning system is demonstrated as a proof of concept. The results of this study provide a decision support model for highway agencies to monitor their network's friction and make appropriate judgments to correct deficiencies based on crash risk. Furthermore, this model can be implemented in the connected vehicle environment to warn drivers of potentially slippery locations. Published by Elsevier Ltd.
International Nuclear Information System (INIS)
Song, Jeong Hoon
2013-01-01
In this study, four integrated dynamics control (IDC) systems abbreviated as IDCB, IDCS, IDCF, and IDCR are developed, evaluated and compared. IDC systems were integrated with brake and steer control systems to enhance lateral stability and handling performance. To construct the IDC systems, a vehicle model with fourteen degrees of freedom, a fuzzy logic controller, and a sliding mode ABS controller were used. They were tested with various steering inputs when excessive full brake pressure or no brake pressure was applied on dry asphalt, wet asphalt, a snow-covered paved road, and a split-µ road. The results showed that an IDC-equipped vehicle improved lateral stability and controllability in every driving condition compared to an ABS-equipped vehicle. Under all road conditions, IDC controllers enabled the yaw rate to follow the reference yaw rate almost perfectly and reduced the body slip angle. On a split-µ road, IDCB, IDCS, IDCF, and IDCR vehicles drove straight ahead with only very small deviations.
On the Design of a Fuzzy Logic-Based Control System for Freeze-Drying Processes.
Fissore, Davide
2016-12-01
This article is focused on the design of a fuzzy logic-based control system to optimize a drug freeze-drying process. The goal of the system is to keep product temperature as close as possible to the threshold value of the formulation being processed, without trespassing it, in such a way that product quality is not jeopardized and the sublimation flux is maximized. The method involves the measurement of product temperature and a set of rules that have been obtained through process simulation with the goal to obtain a unique set of rules for products with very different characteristics. Input variables are the difference between the temperature of the product and the threshold value, the difference between the temperature of the heating fluid and that of the product, and the rate of change of product temperature. The output variables are the variation of the temperature of the heating fluid and the pressure in the drying chamber. The effect of the starting value of the input variables and of the control interval has been investigated, thus resulting in the optimal configuration of the control system. Experimental investigation carried out in a pilot-scale freeze-dryer has been carried out to validate the proposed system. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.
Analysis of maizena drying system using temperature control based fuzzy logic method
Arief, Ulfah Mediaty; Nugroho, Fajar; Purbawanto, Sugeng; Setyaningsih, Dyah Nurani; Suryono
2018-03-01
Corn is one of the rice subtitution food that has good potential. Corn can be processed to be a maizena, and it can be used to make type of food that has been made from maizena, viz. Brownies cake, egg roll, and other cookies. Generally, maizena obtained by drying process carried out 2-3 days under the sun. However, drying process not possible during the rainy season. This drying process can be done using an automatic drying tool. This study was to analyze the design result and manufacture of maizena drying system with temperature control based fuzzylogic method. The result show that temperature of drying system with set point 40°C - 60°C work in suitable condition. The level of water content in 15% (BSN) and temperatureat 50°C included in good drying process. Time required to reach the set point of temperature in 50°C is 7.05 minutes. Drying time for 500 gr samples with temperature 50°C and power capacity 127.6 watt was 1 hour. Based on the result, drying process using temperature control based fuzzy logic method can improve energy efficiency than the conventional method of drying using a direct sunlight source with a temperature that cannot be directly controlled by human being causing the quality of drying result of flour is erratic.
Logical Characterisation of Ontology Construction using Fuzzy Description Logics
DEFF Research Database (Denmark)
Badie, Farshad; Götzsche, Hans
Ontologies based on Description Logics (DLs) have proved to be effective in formally sharing knowledge across semantic technologies, e.g. Semantic Web, Natural Language Processing, Text Analytics, Business intelligence. Our main goal is analysing ontology construction considering vagueness. We have......, employs Description Logics in characterising and analysing fuzzy statements. And finally, provides a conceptual framework describing fuzzy concept learning in ontologies using the Inductive Logic Programming....
Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.
Directory of Open Access Journals (Sweden)
Majid Almaraashi
Full Text Available Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM and simulated annealing (SA algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.
Short-term prediction of solar energy in Saudi Arabia using automated-design fuzzy logic systems.
Almaraashi, Majid
2017-01-01
Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data.
Directory of Open Access Journals (Sweden)
Andrea K. Perez-Hernandez
2013-06-01
Full Text Available At this paper we shown the development of an algorithm to perform edges extraction based on fuzzy logic theory. This method allows recognizing landmarks on the game field for Humanoid League of RoboCup. The proposed algorithm describes the creation of a fuzzy inference system that permit evaluate the existent relationship between image pixels, finding variations on grey levels of related neighbor pixels. Subsequently, it shows an implementation of OTSU method to binarize an image that was obtained from fuzzy process and so generate an image containing only extracted edges, validating the algorithm with Humanoid League images. Later, we analyze obtained results that evidence a good performance of algorithm, considering that this proposal only takes an extra 35% processing time that will be required by traditional methods, whereas extracted edges are 52% less noise susceptible.
Fuzzy logic control of a nitrogen laser
Tam, Siu Chung; Tan, Siong-Chai; Neo, Wah-Peng; Foong, Sze-Chern; Chan, Choon-Hao; Ho, Anthony T.; Chua, Hock-Chuan; Lee, Sing
2001-02-01
Traditionally, the stability of the output of a laser is controlled through scientific means or by a simple feedback loop. For multiinput multioutput control and for medium- to high-power lasers, however, these control schemes may break down. We report on the use of a fuzzy logic control scheme to improve the stability of a pulsed nitrogen laser. Specifically, the nitrogen laser is modeled as a two-input two-output system. The two input parameters are the discharge voltage (V) and nitrogen pressure (P), and the two output parameters are the pulse energy (E) and pulse width (PW). The performance of the fuzzy logic controller is compared with a decoupled two-channel PID (proportional+integral+derivative) controller. In our experiment, the long-term stabilities of the open-loop system are 1.82% root mean square (rms) for pulse energy and 4.58% rms for pulse width. The PID controller achieves better performance with long-term stabilities of 1.46% rms for pulse energy and 4.46% rms for pulse width. The fuzzy logic controller performs the best with long-term stabilities of 1.02% rms for pulse energy and 4.24% rms for pulse width, respectively.
Modeling Academic Performance Evaluation Using Soft Computing Techniques: A Fuzzy Logic Approach
Ramjeet Singh Yadav; Vijendra Pratap Singh
2011-01-01
We have proposed a Fuzzy Expert System (FES) for student academic performance evaluation based on fuzzy logic techniques. A suitable fuzzy inference mechanism and associated rule has been discussed. It introduces the principles behind fuzzy logic and illustrates how these principles could be applied by educators to evaluating student academic performance. Several approaches using fuzzy logic techniques have been proposed to provide a practical method for evaluating student academic performanc...
Developing a Mobile Service-Based Customer Relationship Management System Using Fuzzy Logic
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Xiaobei Liang
2010-12-01
Full Text Available Customer relationship management (CRM has gained lately widespread popularity in many industries. With the development of economy and society, customers are unsatisfied with the stereotyped products. As customers usually describe their demands in nature language, the demands are often conflicting with each other and are often imprecise. The paper studies the operation process of handling customer demand for modern service systems based fuzzy logic methods. While in this mobile medium times, mobile service and CRM are rarely taken into unite study. This paper overviews the related theory likes business engineering, relationship marketing and mobile business, which can be used in mobile CRM (mCRM and in the implement of mobile CRM. The paper analyzes the underlying technology and marketing issues of the initiation of mCRM and integrates various issues of mCRM. Moreover, the paper discusses the characteristics of useful mCRM as the implement of mCRM will help the enterprise enhance the customer relationship and customers' loyalty will also gain more profit. A new stability criterion for the extended singular dynamic input-output model is given to ensure the stability of input-output model.
Type-2 Fuzzy Logic in Intelligent Control Applications
Castillo, Oscar
2012-01-01
We describe in this book, hybrid intelligent systems based mainly on type-2 fuzzy logic for intelligent control. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, and bio-inspired optimization algorithms, which can be used to produce powerful automatic control systems. The book is organized in three main parts, which contain a group of chapters around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which can be the basis for achieving intelligent control with interval type-2 fuzzy logic. The second part of the book is comprised of chapters with the main theme of evolutionary optimization of type-2 fuzzy systems in intelligent control with the aim of designing optimal type-2 fuzzy controllers for complex control problems in diverse areas of application, including mobile robotics, aircraft dynamics systems and hardware implementations. Th...
Fuzzy logic and its application in football team ranking.
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.
French-speaking meeting on fuzzy logic and its applications
International Nuclear Information System (INIS)
1997-01-01
The 1997 edition of LFA'97 meeting for fuzzy logic has been organized by the Pattern Recognition and Computer Vision Laboratory of the National Institute of Applied Sciences. The objective of the meeting was to provide a forum for researchers and users of fuzzy logic and possibility theory to present and discuss theoretical researches and concrete applications. The domains in concern are: the control decision theory, the pattern recognition and image analysis, the artificial intelligence and the information systems. From the 41 papers of this book, two were selected for ETDE and deal with fuzzy regulation systems for heating systems and with fuzzy controllers for gas refining plants, and one was selected for INIS and deal with real-time surveillance and fuzzy logic control systems for nuclear power plants. (J.S.)
Bicycle Frame Prediction Techniques with Fuzzy Logic Method
Rafiuddin Syam; La Ode Asman Muriman
2017-01-01
In general, an appropriate size bike frame would get comfort to the rider while biking. This study aims to predict the simulation system on the bike frame sizes with fuzzy logic. Testing method used is the simulation test. In this study, fuzzy logic will be simulated using Matlab language to test their performance. Mamdani fuzzy logic using 3 variables and 1 output variable intake. Triangle function for the input and output. The controller is designed in the type mamdani with max-min composit...
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.
Implementation of fuzzy logic control algorithm in embedded ...
African Journals Online (AJOL)
As a case study, hardware implementation of fuzzy control algorithm for online temperature control system is demonstrated using 8-bit microcontroller. The hardware implementation followed by software approach has been discussed. Real time result of fuzzy logic temperature control system is also presented.
Type-2 fuzzy logic uncertain systems’ modeling and control
Antão, Rómulo
2017-01-01
This book focuses on a particular domain of Type-2 Fuzzy Logic, related to process modeling and control applications. It deepens readers’understanding of Type-2 Fuzzy Logic with regard to the following three topics: using simpler methods to train a Type-2 Takagi-Sugeno Fuzzy Model; using the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on a locally linear n-step ahead predictor; and developing model-based control algorithms according to the Generalized Predictive Control principles using Type-2 Fuzzy Sets. Throughout the book, theory is always complemented with practical applications and readers are invited to take their learning process one step farther and implement their own applications using the algorithms’ source codes (provided). As such, the book offers avaluable referenceguide for allengineers and researchers in the field ofcomputer science who are interested in intelligent systems, rule-based systems and modeling uncertainty.
Directory of Open Access Journals (Sweden)
F.S. Alavipoor
2016-03-01
Full Text Available This study recommends a GIS-based (Geographic Information Systems and multi-criteria evaluation for site selection of gas power plant in Natanz City of Iran. The multi-criteria decision framework integrates legal requirements and physical constraints related to environmental and economic concerns. It also builds a hierarchy model for gas power plant suitability. The methodologies used for site selection include analytic hierarchy process (AHP, fuzzy set theory and weighted linear combination. The AHP (analytic hierarchy process is a multi-criteria approach which is used to establish the relative importance of criteria. The AHP makes pair-wise comparisons of relative importance between hierarchy elements categorized by environmental decision criteria. In the next step, the fuzzy set theory is used to standardize criteria through different fuzzy membership functions and fuzzy layers are formed by using fuzzy operators in ArcGIS environment. Subsequently, they are categorized into 6 classes using Reclassify Function. Weighted linear combination is used to combine the criteria layers. Finally, the two approaches are analyzed in order to locate the most suitable site to establish a gas power plant. According to the results, using GAMMA fuzzy operator is considered suitable for this site selection.
Application of Fuzzy Logic in Control of Switched Reluctance Motor
Directory of Open Access Journals (Sweden)
Pavel Brandstetter
2006-01-01
Full Text Available The flux linkage of switched reluctance motor (SRM depends on the stator current and position between the rotor and stator poles. The fact determines that during control of SRM current with the help of classical PI controllers in a wide regulation range unsatisfied results occur. The main reasons of the mentioned situation are big changes of the stator inductance depending on the stator current and rotor position. In a switched reluctance motor the stator phase inductance is a non-linear function of the stator phase current and rotor position. Fuzzy controller and fuzzy logic are generally non-linear systems; hence they can provide better performance in this case. Fuzzy controller is mostly presented as a direct fuzzy controller or as a system, which realizes continued changing parameters of other controller, so-called fuzzy supervisor. Referring to the usage of fuzzy logic as a supervisor of conventional PI controller in control of SRM possible improvement occurs.
Fuzzy Logic Unmanned Air Vehicle Motion Planning
Directory of Open Access Journals (Sweden)
Chelsea Sabo
2012-01-01
Full Text Available There are a variety of scenarios in which the mission objectives rely on an unmanned aerial vehicle (UAV being capable of maneuvering in an environment containing obstacles in which there is little prior knowledge of the surroundings. With an appropriate dynamic motion planning algorithm, UAVs would be able to maneuver in any unknown environment towards a target in real time. This paper presents a methodology for two-dimensional motion planning of a UAV using fuzzy logic. The fuzzy inference system takes information in real time about obstacles (if within the agent's sensing range and target location and outputs a change in heading angle and speed. The FL controller was validated, and Monte Carlo testing was completed to evaluate the performance. Not only was the path traversed by the UAV often the exact path computed using an optimal method, the low failure rate makes the fuzzy logic controller (FLC feasible for exploration. The FLC showed only a total of 3% failure rate, whereas an artificial potential field (APF solution, a commonly used intelligent control method, had an average of 18% failure rate. These results highlighted one of the advantages of the FLC method: its adaptability to complex scenarios while maintaining low control effort.
Czech Academy of Sciences Publication Activity Database
Horčík, Rostislav; Noguera, C.; Petrík, M.
2007-01-01
Roč. 53, č. 3 (2007), s. 268-288 ISSN 0942-5616 R&D Projects: GA AV ČR 1ET100300517 Institutional research plan: CEZ:AV0Z10300504 Keywords : algebraic logic * fuzzy logics * generalized contraction * generalized excluded middle * left-continuous t-norms * MTL-algebras * non-classical logics * residuated lattices * standard completeness * substructural logics * varieties * weak cancellation Subject RIV: BA - General Mathematics Impact factor: 0.317, year: 2007
Fuzzy logic applications in engineering science
Harris, J
2006-01-01
Fuzzy logic is a relatively new concept in science applications. Hitherto, fuzzy logic has been a conceptual process applied in the field of risk management. Its potential applicability is much wider than that, however, and its particular suitability for expanding our understanding of processes and information in science and engineering in our post-modern world is only just beginning to be appreciated. Written as a companion text to the author's earlier volume "An Introduction to Fuzzy Logic Applications", the book is aimed at professional engineers and students and those with an interest in exploring the potential of fuzzy logic as an information processing kit with a wide variety of practical applications in the field of engineering science and develops themes and topics introduced in the author's earlier text.
Redundant sensor validation by using fuzzy logic
International Nuclear Information System (INIS)
Holbert, K.E.; Heger, A.S.; Alang-Rashid, N.K.
1994-01-01
This research is motivated by the need to relax the strict boundary of numeric-based signal validation. To this end, the use of fuzzy logic for redundant sensor validation is introduced. Since signal validation employs both numbers and qualitative statements, fuzzy logic provides a pathway for transforming human abstractions into the numerical domain and thus coupling both sources of information. With this transformation, linguistically expressed analysis principles can be coded into a classification rule-base for signal failure detection and identification
Towards the future of fuzzy logic
Trillas, Enric; Kacprzyk, Janusz
2015-01-01
This book provides readers with a snapshot of the state-of-the art in fuzzy logic. Throughout the chapters, key theories developed in the last fifty years as well as important applications to practical problems are presented and discussed from different perspectives, as the authors hail from different disciplines and therefore use fuzzy logic for different purposes. The book aims at showing how fuzzy logic has evolved since the first theory formulation by Lotfi A. Zadeh in his seminal paper on Fuzzy Sets in 1965. Fuzzy theories and implementation grew at an impressive speed and achieved significant results, especially on the applicative side. The study of fuzzy logic and its practice spread all over the world, from Europe to Asia, America and Oceania. The editors believe that, thanks to the drive of young researchers, fuzzy logic will be able to face the challenging goals posed by computing with words. New frontiers of knowledge are waiting to be explored. In order to motivate young people to engage in the ...
FUZZY LOGIC STATIC SYNCHRONOUS COMPENSATOR (FLSTATCOM
Directory of Open Access Journals (Sweden)
I Made Mataram
2016-06-01
Full Text Available Penerapan teknik fuzzy membawa perubahan yang signifikan khusus pada perhitungan dan analisis sistem konvensional. Peranan peralatan FACTS (Flexible AC Transmission System untuk memperbaiki kualitas tegangan dari pembangkit menuju beban sangat besar. STATCOM merupakan peralatan paling berpengaruh untuk memperbaiki tegangan pada jaringan transmisi tenaga listrik. Pembahasan pada penelitian ini dikhususkan pada FLSTATCOM. Model Fuzzy Logic dengan dua input digunakan sebagai pengontrol IGBT, sehingga mampu meningkatkan unjuk kerja STATCOM konvensional. Sistem Single Machine Infinite Bus menjadi sistem uji coba penggunaan FLSTATCOM.Hasil simulasi menggunakan simulink MATLAB, diperoleh nilai tegangan pada tiap sisi terima tanpa menggunakan STATCOM menghasilkan tegangan sebesar 217,3 kV, menggunakan STATCOM menghasilkan tegangan sebesar 220 kV, dan penggunaan FLSTATCOM mampu meningkatkan tegangan menjadi 228,9 kV (5,34%
Directory of Open Access Journals (Sweden)
Souhila Rached Zine
2015-08-01
Full Text Available wind energy features prominently as a supplementary energy booster. It does not pollute and is inexhaustible. However, its high cost is a major constraint, especially on the less windy sites. The purpose of wind energy systems is to maximize energy efficiency, and extract maximum power from the wind speed. In This case, the MPPT control becomes important. To realize this control, strategy conventional Proportional and Integral (PI controller is usually used. However, this strategy cannot achieve better performance. This paper proposes other control methods of a turbine which optimizes its production such as fuzzy logic, sliding mode control. These methods improve the quality and energy efficiency. The proposed Sliding Mode Control (SMC strategy and the fuzzy controllers have presented attractive features such as robustness to parametric uncertainties of the turbine, simplicity of its design and good performances. The simulation result under Matlab\\Simulink has validated the performance of the proposed MPPT strategies.
A Fuzzy Logic Based System for Detection of Car Driver's Vigilance Level
Czech Academy of Sciences Publication Activity Database
Coufal, David
2008-01-01
Roč. 18, č. 6 (2008), s. 515-526 ISSN 1210-0552 R&D Projects: GA MŠk ME 949 Institutional research plan: CEZ:AV0Z10300504 Keywords : EEG spectrograms * implicative fuzzy system s * micro-sleeps detection Subject RIV: BA - General Mathematics Impact factor: 0.395, year: 2008
Lu, Thomas; Pham, Timothy; Liao, Jason
2011-01-01
This paper presents the development of a fuzzy logic function trained by an artificial neural network to classify the system noise temperature (SNT) of antennas in the NASA Deep Space Network (DSN). The SNT data were classified into normal, marginal, and abnormal classes. The irregular SNT pattern was further correlated with link margin and weather data. A reasonably good correlation is detected among high SNT, low link margin and the effect of bad weather; however we also saw some unexpected non-correlations which merit further study in the future.
Fuzzy Hypotheses Testing in the Framework of Fuzzy Logic
Czech Academy of Sciences Publication Activity Database
Holeňa, Martin
2004-01-01
Roč. 145, - (2004), s. 229-252 ISSN 0165-0114 R&D Projects: GA AV ČR IAA1030004; GA MŠk OC 274.001 Grant - others:COST(XE) Action 274 TARSKI Institutional research plan: CEZ:AV0Z1030915 Keywords : non-classical logics * fuzzy predicate calculus * basic fuzzy logic * generalized quantifiers * fuzzy statistics and data analysis * vague hypotheses * vague significance level * method Guha Subject RIV: BB - Applied Statistics , Operational Research Impact factor: 0.734, year: 2004
Directory of Open Access Journals (Sweden)
Saifullah Khalid
2016-09-01
Full Text Available Three conventional control constant instantaneous power control, sinusoidal current control, and synchronous reference frame techniques for extracting reference currents for shunt active power filters have been optimized using Fuzzy Logic control and Adaptive Tabu search Algorithm and their performances have been compared. Critical analysis of Comparison of the compensation ability of different control strategies based on THD and speed will be done, and suggestions will be given for the selection of technique to be used. The simulated results using MATLAB model are presented, and they will clearly prove the value of the proposed control method of aircraft shunt APF. The waveforms observed after the application of filter will be having the harmonics within the limits and the power quality will be improved.
de Bruin, Jeroen S; Adlassnig, Klaus-Peter; Blacky, Alexander; Koller, Walter
2016-05-01
Many electronic infection detection systems employ dichotomous classification methods, classifying patient data as pathological or normal with respect to one or several types of infection. An electronic monitoring and surveillance system for healthcare-associated infections (HAIs) known as Moni-ICU is being operated at the intensive care units (ICUs) of the Vienna General Hospital (VGH) in Austria. Instead of classifying patient data as pathological or normal, Moni-ICU introduces a third borderline class. Patient data classified as borderline with respect to an infection-related clinical concept or HAI surveillance definition signify that the data nearly or partly fulfill the definition for the respective concept or HAI, and are therefore neither fully pathological nor fully normal. Using fuzzy sets and propositional fuzzy rules, we calculated how frequently patient data are classified as normal, borderline, or pathological with respect to infection-related clinical concepts and HAI definitions. In dichotomous classification methods, borderline classification results would be confounded by normal. Therefore, we also assessed whether the constructed fuzzy sets and rules employed by Moni-ICU classified patient data too often or too infrequently as borderline instead of normal. Electronic surveillance data were collected from adult patients (aged 18 years or older) at ten ICUs of the VGH. All adult patients admitted to these ICUs over a two-year period were reviewed. In all 5099 patient stays (4120 patients) comprising 49,394 patient days were evaluated. For classification, a part of Moni-ICU's knowledge base comprising fuzzy sets and rules for ten infection-related clinical concepts and four top-level HAI definitions was employed. Fuzzy sets were used for the classification of concepts directly related to patient data; fuzzy rules were employed for the classification of more abstract clinical concepts, and for top-level HAI surveillance definitions. Data for each
Mostafa, Salama A; Mustapha, Aida; Mohammed, Mazin Abed; Ahmad, Mohd Sharifuddin; Mahmoud, Moamin A
2018-04-01
Autonomous agents are being widely used in many systems, such as ambient assisted-living systems, to perform tasks on behalf of humans. However, these systems usually operate in complex environments that entail uncertain, highly dynamic, or irregular workload. In such environments, autonomous agents tend to make decisions that lead to undesirable outcomes. In this paper, we propose a fuzzy-logic-based adjustable autonomy (FLAA) model to manage the autonomy of multi-agent systems that are operating in complex environments. This model aims to facilitate the autonomy management of agents and help them make competent autonomous decisions. The FLAA model employs fuzzy logic to quantitatively measure and distribute autonomy among several agents based on their performance. We implement and test this model in the Automated Elderly Movements Monitoring (AEMM-Care) system, which uses agents to monitor the daily movement activities of elderly users and perform fall detection and prevention tasks in a complex environment. The test results show that the FLAA model improves the accuracy and performance of these agents in detecting and preventing falls. Copyright © 2018 Elsevier B.V. All rights reserved.
de Franciscis, Stefano; Fregola, Salvatore; Gallo, Alessandro; Argirò, Giuseppe; Barbetta, Andrea; Buffone, Gianluca; Caliò, Francesco G; De Caridi, Giovanni; Amato, Bruno; Serra, Raffaele
2016-12-01
Chronic leg ulcers (CLUs) are a common occurrence in the western population and are associated with a negative impact on the quality of life of patients. They also cause a substantial burden on the health budget. The pathogenesis of leg ulceration is quite heterogeneous, and chronic venous ulceration (CVU) is the most common manifestation representing the main complication of chronic venous disease (CVD). Prevention strategies and early identification of the risk represent the best form of management. Fuzzy logic is a flexible mathematical system that has proved to be a powerful tool for decision-making systems and pattern classification systems in medicine. In this study, we have elaborated a computerised prediction system for chronic leg ulcers (PredyCLU) based on fuzzy logic, which was retrospectively applied on a multicentre population of 77 patients with CVD. This evaluation system produced reliable risk score patterns and served effectively as a stratification risk tool in patients with CVD who were at the risk of developing CVUs. © 2015 Medicalhelplines.com Inc and John Wiley & Sons Ltd.
Macian-Sorribes, Hector; Pulido-Velazquez, Manuel
2016-04-01
This contribution presents a methodology for defining optimal seasonal operating rules in multireservoir systems coupling expert criteria and stochastic optimization. Both sources of information are combined using fuzzy logic. The structure of the operating rules is defined based on expert criteria, via a joint expert-technician framework consisting in a series of meetings, workshops and surveys carried out between reservoir managers and modelers. As a result, the decision-making process used by managers can be assessed and expressed using fuzzy logic: fuzzy rule-based systems are employed to represent the operating rules and fuzzy regression procedures are used for forecasting future inflows. Once done that, a stochastic optimization algorithm can be used to define optimal decisions and transform them into fuzzy rules. Finally, the optimal fuzzy rules and the inflow prediction scheme are combined into a Decision Support System for making seasonal forecasts and simulate the effect of different alternatives in response to the initial system state and the foreseen inflows. The approach presented has been applied to the Jucar River Basin (Spain). Reservoir managers explained how the system is operated, taking into account the reservoirs' states at the beginning of the irrigation season and the inflows previewed during that season. According to the information given by them, the Jucar River Basin operating policies were expressed via two fuzzy rule-based (FRB) systems that estimate the amount of water to be allocated to the users and how the reservoir storages should be balanced to guarantee those deliveries. A stochastic optimization model using Stochastic Dual Dynamic Programming (SDDP) was developed to define optimal decisions, which are transformed into optimal operating rules embedding them into the two FRBs previously created. As a benchmark, historical records are used to develop alternative operating rules. A fuzzy linear regression procedure was employed to
Modelling and Control of the Qball X4 Quadrotor System based on Pid and Fuzzy Logic Structure
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.
Directory of Open Access Journals (Sweden)
Zaghba Layachi
2015-08-01
Full Text Available there is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature , differ from each other evidencing knowledge deficiency in PV systems [14]. Maximum power point tracking (MPPT methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP. Among all MPPT methods existing in the literature, perturb and observe (P&O is the most commonly used for its simplicity and ease of implementation; however, it presents drawbacks such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In order to allow a functioning around the optimal point Mopt, we have inserted a DC-DC converter (Buck–Boost for a better matching between the PV and the load. This paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&O control for stande-alone photovoltaic Array system .In particular, the performances of the controllers are analyzed under variation weather conditions with are constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization.
Control of a dc motor using fuzzy logic control algorithm | Usoro ...
African Journals Online (AJOL)
This study sought to establish the impact of a fuzzy logic controller (FLC) and a Proportional-Integral-Derivative (PID) controller in the control performance of an industrial type DC motor using MATLAB. The fuzzy logic controller was developed on the basis of Mamdani type fuzzy inference system (FIS). The centroid method ...
Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 2
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.
Directory of Open Access Journals (Sweden)
Chandra Babu Paduchuri
2014-01-01
Full Text Available This paper proposes the instantaneous p-q theory based fuzzy logic controller (FLC for multi converter unified power quality conditioner (MC-UPQC to mitigate power quality issues in two feeders three-phase four-wire distribution systems. The proposed system is extended system of the existing one feeder three-phase four-wire distribution system, which is operated with UPQC. This system is employed with three voltage source converters, which are connected commonly to two feeder distribution systems. The performance of this proposed system used to compensate voltage sag, neutral current mitigation and compensation of voltage and current harmonics under linear and nonlinear load conditions. The neutral current flowing in series transformers is zero in the implementation of the proposed system. The simulation performance analysis is carried out using MATLAB.
Perpetual Learning Framework based on Type-2 Fuzzy Logic System for a Complex Manufacturing Process
Baraka, A.; Panoutsos, G.; Cater, S.
2016-01-01
This paper introduces a perpetual type-2 Neuro-Fuzzy modelling structure for continuous learning and its application to the complex thermo-mechanical metal process of steel Friction Stir Welding (FSW). The ‘perpetual’ property refers to the capability of the proposed system to continuously learn from new process data, in an incremental learning fashion. This is particularly important in industrial/manufacturing processes, as it eliminates the need to retrain the model in the presence of new d...
Computational intelligence synergies of fuzzy logic, neural networks and evolutionary computing
Siddique, Nazmul
2013-01-01
Computational Intelligence: Synergies of Fuzzy Logic, Neural Networks and Evolutionary Computing presents an introduction to some of the cutting edge technological paradigms under the umbrella of computational intelligence. Computational intelligence schemes are investigated with the development of a suitable framework for fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems and evolutionary neural systems. Applications to linear and non-linear systems are discussed with examples. Key features: Covers all the aspect
Energy Technology Data Exchange (ETDEWEB)
Davoudi, Mehdi, E-mail: mehdi.davoudi@polimi.it [Department of Electrical and Computer Engineering, Buein Zahra Technical University, Buein Zahra, Qazvin (Iran, Islamic Republic of); Davoudi, Mohsen, E-mail: davoudi@eng.ikiu.ac.ir [Department of Electrical Engineering, Imam Khomeini International University, Qazvin, 34148-96818 (Iran, Islamic Republic of)
2017-06-15
Highlights: • A couple of algorithms to diagnose if Electron Cyclotron Heating (ECH) power is deposited properly on the expected deposition minor radius are proposed. • The algorithms are based on Bayesian theory and Fuzzy logic. • The algorithms are tested on the off-line experimental data acquired from Frascati Tokamak Upgrade (FTU), Frascati, Italy. • Uncertainties and evidences derived from the combination of online information formed by the measured diagnostic data and the prior information are also estimated. - Abstract: In the thermonuclear fusion systems, the new plasma control systems use some measured on-line information acquired from different sensors and prior information obtained by predictive plasma models in order to stabilize magnetic hydro dynamics (MHD) activity in a tokamak. Suppression of plasma instabilities is a key issue to improve the confinement time of controlled thermonuclear fusion with tokamaks. This paper proposes a couple of algorithms based on Bayesian theory and Fuzzy logic to diagnose if Electron Cyclotron Heating (ECH) power is deposited properly on the expected deposition minor radius (r{sub DEP}). Both algorithms also estimate uncertainties and evidences derived from the combination of the online information formed by the measured diagnostic data and the prior information. The algorithms have been employed on a set of off-line ECE channels data which have been acquired from the experimental shot number 21364 at Frascati Tokamak Upgrade (FTU), Frascati, Italy.
Energy Technology Data Exchange (ETDEWEB)
Lohse, M.; Boening, T.; Hegemann, G. [Fachhochschule Muenster (Germany). Inst. fuer Abfall- und Abwasserwirtschaft e.V.
1999-07-01
Within the framework of a project sponsored by EUREGIO, test series with the biological activation stages of a German and a Dutch sewage treatment plant each are carried out using different process concepts for the control of oxygen supply by fuzzy logic. As the currently available results demonstrate, the developed fuzzy-logic fields of characteristic curves permit establishing a stable and, thus, little energy-consuming process with optimum oxygen supply in comparison with conventional control. (orig.) [German] Im Rahmen eines von der EUREGIO gefoerderten Forschungsprojektes werden Versuchsreihen im Bereich der biologischen Belebungsstufen einer deutschen und einer niederlaendischen Abwasserreinigungsanlage (ARA) mit unterschiedlichen Verfahrenskonzepten hinsichtlich der Regelung der Sauerstoffzufuhr mit Hilfe der Fuzzy-Logik Technik durchgefuehrt. Die bisherigen Versuchsergebnisse zeigen, dass - im Vergleich zur konventionellen Regelung - durch die entwickelten Fuzzy-Logik Kennfelder ein stabiler und damit energiearmer Prozess mit optimaler Sauerstoffzufuhr erzeugt wird. (orig.)
Fuzzy logic control to be conventional method
International Nuclear Information System (INIS)
Eker, Ilyas; Torun, Yunis
2006-01-01
Increasing demands for flexibility and fast reactions in modern process operation and production methods result in nonlinear system behaviour of partly unknown systems, and this necessitates application of alternative control methods to meet the demands. Fuzzy logic (FL) control can play an important role because knowledge based design rules can easily be implemented in systems with unknown structure, and it is going to be a conventional control method since the control design strategy is simple and practical and is based on linguistic information. Computational complexity is not a limitation any more because the computing power of computers has been significantly improved even for high speed industrial applications. This makes FL control an important alternative method to the conventional PID control method for use in nonlinear industrial systems. This paper presents a practical implementation of the FL control to an electrical drive system. Such drive systems used in industry are composed of masses moving under the action of position and velocity dependent forces. These forces exhibit nonlinear behaviour. For a multi-mass drive system, the nonlinearities, like Coulomb friction and dead zone, significantly influence the operation of the systems. The proposed FL control configuration is based on speed error and change of speed error. The feasibility and effectiveness of the control method are experimentally demonstrated. The results obtained from conventional FL control, fuzzy PID and adaptive FL control are compared with traditional PID control for the dynamic responses of the closed loop drive system
Directory of Open Access Journals (Sweden)
Yanzi Wang
2016-01-01
Full Text Available Over the last few years; issues regarding the use of hybrid energy storage systems (HESSs in hybrid electric vehicles have been highlighted by the industry and in academic fields. This paper proposes a fuzzy-logic power management strategy based on Markov random prediction for an active parallel battery-UC HESS. The proposed power management strategy; the inputs for which are the vehicle speed; the current electric power demand and the predicted electric power demand; is used to distribute the electrical power between the battery bank and the UC bank. In this way; the battery bank power is limited to a certain range; and the peak and average charge/discharge power of the battery bank and overall loss incurred by the whole HESS are also reduced. Simulations and scaled-down experimental platforms are constructed to verify the proposed power management strategy. The simulations and experimental results demonstrate the advantages; feasibility and effectiveness of the fuzzy-logic power management strategy based on Markov random prediction.
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.
A Game Theoretic Sensor Resource Allocation Using Fuzzy Logic
Directory of Open Access Journals (Sweden)
Stephen C. Stubberud
2013-01-01
Full Text Available A sensor resource management system that employs fuzzy logic to provide the utility functions to a game theoretic approach is developed. The application looks at a virtual fence problem where several unattended ground sensors are placed in remote locations to act as virtual sentries. The goal of the approach is to maximize the battery life while tracking targets of interest. This research also considers the incorporation of uncertainty into the fuzzy membership functions. Both type-2 fuzzy logic and the use of conditional fuzzy membership function are employed. The type-2 fuzzy logic is employed in the case of acoustical sensor tracking accuracy degradation, while the condition-based membership functions are used to adapt to different conditions, such as environmental conditions and sensor performance degradation, over time. The resource management process uses fuzzy logic to determine which of the sensor systems on a sensor pod is used to provide initial classification of the target and which sensor or sensors are to be used in tracking and better classifying the target if it is determined to be of value to the mission. The three different approaches are compared to determine when the best times for the more complex approaches are warranted.
Fuzzy Logic Enhanced Digital PIV Processing Software
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.
Fuzzy Logic Controller Design for Intelligent Robots
Directory of Open Access Journals (Sweden)
Ching-Han Chen
2017-01-01
Full Text Available This paper presents a fuzzy logic controller by which a robot can imitate biological behaviors such as avoiding obstacles or following walls. The proposed structure is implemented by integrating multiple ultrasonic sensors into a robot to collect data from a real-world environment. The decisions that govern the robot’s behavior and autopilot navigation are driven by a field programmable gate array- (FPGA- based fuzzy logic controller. The validity of the proposed controller was demonstrated by simulating three real-world scenarios to test the bionic behavior of a custom-built robot. The results revealed satisfactorily intelligent performance of the proposed fuzzy logic controller. The controller enabled the robot to demonstrate intelligent behaviors in complex environments. Furthermore, the robot’s bionic functions satisfied its design objectives.
DEFF Research Database (Denmark)
Jarre, Astrid; Paterson, B.; Moloney, C.L.
2008-01-01
rule-based Boolean and fuzzy-logic models have been used successfully as knowledge-based decision support tools. This study compares two such systems relevant to fisheries management in an EAF developed for the southern Benguela. The first is a rule-based system for the prediction of anchovy...
International Nuclear Information System (INIS)
Thameem Ansari, M.Md.; Velusami, S.
2010-01-01
A design of dual mode linguistic hedge fuzzy logic controller for an isolated wind-diesel hybrid power system with superconducting magnetic energy storage unit is proposed in this paper. The design methodology of dual mode linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of linguistic hedges and hybrid genetic algorithm-simulated annealing algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically and can speed up the control result to fit the system demand. The hybrid genetic algorithm-simulated annealing algorithm is adopted to search the optimal linguistic hedge combination in the linguistic hedge module. Dual mode concept is also incorporated in the proposed controller because it can improve the system performance. The system with the proposed controller was simulated and the frequency deviation resulting from a step load disturbance is presented. The comparison of the proportional plus integral controller, fuzzy logic controller and the proposed dual mode linguistic hedge fuzzy logic controller shows that, with the application of the proposed controller, the system performance is improved significantly. The proposed controller is also found to be less sensitive to the changes in the parameters of the system and also robust under different operating modes of the hybrid power system.
Directory of Open Access Journals (Sweden)
Y. C. Lai
2015-05-01
Full Text Available This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS. There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system
Lai, Y. C.; Chang, C. C.; Tsai, C. M.; Lin, S. Y.; Huang, S. C.
2015-05-01
This paper presents a pedestrian indoor navigation system based on the multi-sensor fusion and fuzzy logic estimation algorithms. The proposed navigation system is a self-contained dead reckoning navigation that means no other outside signal is demanded. In order to achieve the self-contained capability, a portable and wearable inertial measure unit (IMU) has been developed. Its adopted sensors are the low-cost inertial sensors, accelerometer and gyroscope, based on the micro electro-mechanical system (MEMS). There are two types of the IMU modules, handheld and waist-mounted. The low-cost MEMS sensors suffer from various errors due to the results of manufacturing imperfections and other effects. Therefore, a sensor calibration procedure based on the scalar calibration and the least squares methods has been induced in this study to improve the accuracy of the inertial sensors. With the calibrated data acquired from the inertial sensors, the step length and strength of the pedestrian are estimated by multi-sensor fusion and fuzzy logic estimation algorithms. The developed multi-sensor fusion algorithm provides the amount of the walking steps and the strength of each steps in real-time. Consequently, the estimated walking amount and strength per step are taken into the proposed fuzzy logic estimation algorithm to estimates the step lengths of the user. Since the walking length and direction are both the required information of the dead reckoning navigation, the walking direction is calculated by integrating the angular rate acquired by the gyroscope of the developed IMU module. Both the walking length and direction are calculated on the IMU module and transmit to a smartphone with Bluetooth to perform the dead reckoning navigation which is run on a self-developed APP. Due to the error accumulating of dead reckoning navigation, a particle filter and a pre-loaded map of indoor environment have been applied to the APP of the proposed navigation system to extend its
Fuzzy Reasoning Based on First-Order Modal Logic,
Zhang, Xiaoru; Zhang, Z.; Sui, Y.; Huang, Z.
2008-01-01
As an extension of traditional modal logics, this paper proposes a fuzzy first-order modal logic based on believable degree, and gives out a description of the fuzzy first-order modal logic based on constant domain semantics. In order to make the reasoning procedure between the fuzzy assertions
Implementation of fuzzy logic control algorithm in embedded ...
African Journals Online (AJOL)
Fuzzy logic control algorithm solves problems that are difficult to address with traditional control techniques. This paper describes an implementation of fuzzy logic control algorithm using inexpensive hardware as well as how to use fuzzy logic to tackle a specific control problem without any special software tools. As a case ...
Genetic Algorithm Tuned Fuzzy Logic for Gliding Return Trajectories
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.
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.
Li, Liang; Ran, Xu; Wu, Kaihui; Song, Jian; Han, Zongqi
2015-06-01
The traction control system (TCS) might prevent excessive skid of the driving wheels so as to enhance the driving performance and direction stability of the vehicle. But if driven on an uneven low-friction road, the vehicle body often vibrates severely due to the drastic fluctuations of driving wheels, and then the vehicle comfort might be reduced greatly. The vibrations could be hardly removed with traditional drive-slip control logic of the TCS. In this paper, a novel fuzzy logic controller has been brought forward, in which the vibration signals of the driving wheels are adopted as new controlled variables, and then the engine torque and the active brake pressure might be coordinately re-adjusted besides the basic logic of a traditional TCS. In the proposed controller, an adjustable engine torque and pressure compensation loop are adopted to constrain the drastic vehicle vibration. Thus, the wheel driving slips and the vibration degrees might be adjusted synchronously and effectively. The simulation results and the real vehicle tests validated that the proposed algorithm is effective and adaptable for a complicated uneven low-friction road.
International Nuclear Information System (INIS)
Aziz, Nur Liyana Afiqah Abdul; Yap, Keem Siah; Bunyamin, Muhammad Afif
2013-01-01
This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of c omputing the word . The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.
Directory of Open Access Journals (Sweden)
Nga Le Thi Thuy
2017-12-01
Full Text Available A swarm robot is a collection of large numbers of simple robots used to perform complex tasks that a single robot cannot perform or only perform ineffectively. The swarm robot works successfully only when the cooperation mechanism among individual robots is satisfied. The cooperation mechanism studied in this article ensures the formation and the distance between each pair of individual robots while moving to their destination while avoiding obstacles. The solved problems in this article include; controlling the suction/thrust force between each pair of individual robots in the swarm based on the fuzzy logic structure of the Singer-Input-Singer-Output under Mamdani law; demonstrating the stability of the system based on the Lyapunov theory; and applying control to the multitasking system of the swarm robot based on Null-Space-Behavioral control. Finally, the simulation results make certain that all the individual robots assemble after moving and avoid obstacles.
Aziz, Nur Liyana Afiqah Abdul; Siah Yap, Keem; Afif Bunyamin, Muhammad
2013-06-01
This paper presents a new approach of the fault detection for improving efficiency of circulating water system (CWS) in a power generation plant using a hybrid Fuzzy Logic System (FLS) and Extreme Learning Machine (ELM) neural network. The FLS is a mathematical tool for calculating the uncertainties where precision and significance are applied in the real world. It is based on natural language which has the ability of "computing the word". The ELM is an extremely fast learning algorithm for neural network that can completed the training cycle in a very short time. By combining the FLS and ELM, new hybrid model, i.e., FLS-ELM is developed. The applicability of this proposed hybrid model is validated in fault detection in CWS which may help to improve overall efficiency of power generation plant, hence, consuming less natural recourses and producing less pollutions.
Development of an OLAP Based Fuzzy Logic System for Supporting Put Away Decision
Directory of Open Access Journals (Sweden)
S.H.Chung
2009-10-01
Full Text Available In today`s rapidly changing and globally volatile world, manufacturers pay strong efforts on conducting lean production, outsourcing their components, and management on the complex supply chain. Warehouse management plays a vital role to be a successful player in the any kinds of industry which put-away process is a key activity that brings significant influence and challenges to warehouse performance. In this dynamic operating environment, minimizing the operation mistakes and providing accurate real time inventory information to stakeholder become the basic requirements to be an order qualifier. An OLAP based intelligent system called Fuzzy Storage Assignment System (FSAS is proposed to increase availability of decision support data and convert the human knowledge into system for tackling the storage location assignment problem (SLAP. To validate the feasibility of this proposed system, a prototype will be worked out for a third party logistics company.
Experiments on neural network architectures for fuzzy logic
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.
CAC Algorithm Based on Fuzzy Logic
Directory of Open Access Journals (Sweden)
Ľubomír DOBOŠ
2009-05-01
Full Text Available Quality of Service (QoS represent one ofmajor parameters that describe mobile wirelesscommunication systems. Thanks growing popularity ofmobile communication in last years, there is anincreasing expansion of connection admission controlschemes (CAC that plays important role in QoSdelivering in terms of connection blocking probability,connection dropping probability, data loss rate andsignal quality.With expansion of services provided by the mobilenetworks growing the requirements to QoS andtogether growing requirements to CAC schemes.Therefore, still more sophisticated CAC schemes arerequired to guarantee the QoS. This paper containsshort introduction into division of connectionadmission control schemes and presents thresholdoriented CAC scheme with fuzzy logic used foradaptation of the threshold value.
Development of Fuzzy-Logic-Based Self Tuning PI Controller for Servomotor
Saad, Nordin; Wahyunggoro, Oyas
2010-01-01
This work discusses the modeling of a DC servomotor from gray box identification and performance evaluations of real time experiment using a fuzzy-logic-based self tuning PI controller as compared to fuzzy-logic-based self tuning PID controller, fuzzy logic controller, PID controller and PI controller on the DC servomotor system. Here, the s-model transfer function of a DC servomotor is identified as a third order transfer function without
Interaction analysis through fuzzy temporal logic
Ijsselmuiden, Joris; Dornheim, Johannes
2015-01-01
Interaction analysis is defined as the generation of semantic descriptions from machine perception. This can be achieved through a combination of fuzzy metric temporal logic (FMTL) and situation graph trees (SGTs). We extended the FMTL/SGT framework with modules for clustering and parameter
Complexity of Fuzzy Probability Logics II
Czech Academy of Sciences Publication Activity Database
Hájek, Petr
2007-01-01
Roč. 158, č. 23 (2007), s. 2605-2611 ISSN 0165-0114 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy logic * probability * computational complexity Subject RIV: BA - General Mathematics Impact factor: 1.373, year: 2007
Indeterminacy, linguistic semantics and fuzzy logic
Energy Technology Data Exchange (ETDEWEB)
Novak, V. [Univ. of Ostrava (Czech Republic)
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.
Mathematical Fuzzy Logic and Natural Numbers
Czech Academy of Sciences Publication Activity Database
Hájek, Petr
2007-01-01
Roč. 81, č. 1-3 (2007), s. 155-163 ISSN 0169-2968 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy logic * arithmetic * essential undecidability Subject RIV: BA - General Mathematics Impact factor: 0.693, year: 2007
Automating Software Development Process using Fuzzy Logic
Marcelloni, Francesco; Aksit, Mehmet; Damiani, Ernesto; Jain, Lakhmi C.; Madravio, Mauro
2004-01-01
In this chapter, we aim to highlight how fuzzy logic can be a valid expressive tool to manage the software development process. We characterize a software development method in terms of two major components: artifact types and methodological rules. Classes, attributes, operations, and inheritance
A logical approach to fuzzy truth hedges
Czech Academy of Sciences Publication Activity Database
Esteva, F.; Godo, L.; Noguera, Carles
2013-01-01
Roč. 232, č. 1 (2013), s. 366-385 ISSN 0020-0255 Institutional support: RVO:67985556 Keywords : Mathematical fuzzy logic * Standard completeness * Truth hedges Subject RIV: BA - General Mathematics Impact factor: 3.893, year: 2013 http://library.utia.cas.cz/separaty/2016/MTR/noguera-0469148.pdf
Can fuzzy logic make things more clear?
J.A. Hazelzet (Jan)
2009-01-01
textabstractIntensive 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.
Directory of Open Access Journals (Sweden)
Sofia Maria Dima
2016-01-01
Full Text Available Event detection in realistic WSN environments is a critical research domain, while the environmental monitoring comprises one of its most pronounced applications. Although efforts related to the environmental applications have been presented in the current literature, there is a significant lack of investigation on the performance of such systems, when applied in wireless environments. Aiming at addressing this shortage, in this paper an advanced multimodal approach is followed based on fuzzy logic. The proposed fuzzy inference system (FIS is implemented on TelosB motes and evaluates the probability of fire detection while aiming towards power conservation. Additionally to a straightforward centralized approach, a distributed implementation of the above FIS is also proposed, aiming towards network congestion reduction while optimally distributing the energy consumption among network nodes so as to maximize network lifetime. Moreover this work proposes an event based execution of the aforementioned FIS aiming to further reduce the computational as well as the communication cost, compared to a periodical time triggered FIS execution. As a final contribution, performance metrics acquired from all the proposed FIS implementation techniques are thoroughly compared and analyzed with respect to critical network conditions aiming to offer realistic evaluation and thus objective conclusions’ extraction.
Fuzzy logic and neural networks basic concepts & application
Alavala, Chennakesava R
2008-01-01
About the Book: The primary purpose of this book is to provide the student with a comprehensive knowledge of basic concepts of fuzzy logic and neural networks. The hybridization of fuzzy logic and neural networks is also included. No previous knowledge of fuzzy logic and neural networks is required. Fuzzy logic and neural networks have been discussed in detail through illustrative examples, methods and generic applications. Extensive and carefully selected references is an invaluable resource for further study of fuzzy logic and neural networks. Each chapter is followed by a question bank
Jarre, Astrid; Paterson, Barbara; Moloney, Coleen L.; Miller, David C. M.; Field, John G.; Starfield, Anthony M.
2008-10-01
In an ecosystem approach to fisheries (EAF), management must draw on information of widely different types, and information addressing various scales. Knowledge-based systems assist in the decision-making process by summarising this information in a logical, transparent and reproducible way. Both rule-based Boolean and fuzzy-logic models have been used successfully as knowledge-based decision support tools. This study compares two such systems relevant to fisheries management in an EAF developed for the southern Benguela. The first is a rule-based system for the prediction of anchovy recruitment and the second is a fuzzy-logic tool to monitor implementation of an EAF in the sardine fishery. We construct a fuzzy-logic counterpart to the rule-based model, and a rule-based counterpart to the fuzzy-logic model, compare their results, and include feedback from potential users of these two decision support tools in our evaluation of the two approaches. With respect to the model objectives, no method clearly outperformed the other. The advantages of numerically processing continuous variables, and interpreting the final output, as in fuzzy-logic models, can be weighed up against the advantages of using a few, qualitative, easy-to-understand categories as in rule-based models. The natural language used in rule-based implementations is easily understood by, and communicated among, users of these systems. Users unfamiliar with fuzzy-set theory must “trust” the logic of the model. Graphical visualization of intermediate and end results is an important advantage of any system. Applying the two approaches in parallel improved our understanding of the model as well as of the underlying problems. Even for complex problems, small knowledge-based systems such as the ones explored here are worth developing and using. Their strengths lie in (i) synthesis of the problem in a logical and transparent framework, (ii) helping scientists to deliberate how to apply their science to
Szulczyński, Bartosz; Gębicki, Jacek; Namieśnik, Jacek
2018-01-01
The paper presents the possibility of application of fuzzy logic to determine the odour intensity of model, ternary gas mixtures (α-pinene, toluene and triethylamine) using electronic nose prototype. The results obtained using fuzzy logic algorithms were compared with the values obtained using multiple linear regression (MLR) model and sensory analysis. As the results of the studies, it was found the electronic nose prototype along with the fuzzy logic pattern recognition system can be successfully used to estimate the odour intensity of tested gas mixtures. The correctness of the results obtained using fuzzy logic was equal to 68%.
Boiocchi, Riccardo; Gernaey, Krist V; Sin, Gürkan
2016-10-01
A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several constrained optimization problems corresponding to different qualitative operation states of the system are defined and solved to identify, in a consistent manner, the critical points of the membership functions for the input variables. The consistently identified critical points, together with the linguistic rules, determine the long term reachability of the control objectives by the fuzzy logic controller. The methodology is highlighted using a single-stage side-stream partial nitritation/Anammox reactor as a case study. As a result, a new fuzzy-logic controller for high and stable total nitrogen removal efficiency is designed. Rigorous simulations are carried out to evaluate and benchmark the performance of the controller. The results demonstrate that the novel control strategy is capable of rejecting the long-term influent disturbances, and can achieve a stable and high TN removal efficiency. Additionally, the controller was tested, and showed robustness, against measurement noise levels typical for wastewater sensors. A feedforward-feedback configuration using the present controller would give even better performance. In comparison, a previously developed fuzzy-logic controller using merely expert and intuitive knowledge performed worse. This proved the importance of using a systematic methodology for the derivation of the membership functions for multivariable systems. These results are promising for future applications of the controller in real full-scale plants. Furthermore, the methodology can be used as a tool to help systematically design fuzzy logic control applications for other biological processes. Copyright © 2016 Elsevier Ltd. All rights reserved.
Mauseth, Richard; Lord, Sandra M; Hirsch, Irl B; Kircher, Robert C; Matheson, Don P; Greenbaum, Carla J
2015-09-14
Under controlled conditions, the Dose Safety artificial pancreas (AP) system controller, which utilizes "fuzzy logic" (FL) methodology to calculate and deliver appropriate insulin dosages based on changes in blood glucose, successfully managed glycemic excursions. The aim of this study was to show whether stressing the system with pizza (high carbohydrate/high fat) meals and exercise would reveal deficits in the performance of the Dose Safety FL controller (FLC) and lead to improvements in the dosing matrix. Ten subjects with type 1 diabetes (T1D) were enrolled and participated in 30 studies (17 meal, 13 exercise) using 2 versions of the FLC. After conducting 13 studies with the first version (FLC v2.0), interim results were evaluated and the FLC insulin-dosing matrix was modified to create a new controller version (FLC v2.1) that was validated through regression testing using v2.0 CGM datasets prior to its use in clinical studies. The subsequent 17 studies were performed using FLC v2.1. Use of FLC v2.1 vs FLC v2.0 in the pizza meal tests showed improvements in mean blood glucose (205 mg/dL vs 232 mg/dL, P = .04). FLC v2.1 versus FLC v2.0 in exercise tests showed improvements in mean blood glucose (146 mg/dL vs 201 mg/dL, P = .004), percentage time spent >180 mg/dL (19.3% vs 46.7%, P = .001), and percentage time spent 70-180 mg/dL (80.0% vs 53.3%, P = .002). Stress testing the AP system revealed deficits in the FLC performance, which led to adjustments to the dosing matrix followed by improved FLC performance when retested. © 2015 Diabetes Technology Society.
Fuzzy logic control of vehicle suspensions with dry friction nonlinearity
Indian Academy of Sciences (India)
and a harsh ride. In order to overcome this practical difficulty a new FLC approach is proposed. The algorithm of the MIMO fuzzy logic controller for the vehicle suspension system uses the errors of the suspension end velocities of the front and rear, and their accelerations and suspension gap velocities as the input variables, ...
Self-learning fuzzy logic controllers based on reinforcement
International Nuclear Information System (INIS)
Wang, Z.; Shao, S.; Ding, J.
1996-01-01
This paper proposes a new method for learning and tuning Fuzzy Logic Controllers. The self-learning scheme in this paper is composed of Bucket-Brigade and Genetic Algorithm. The proposed method is tested on the cart-pole system. Simulation results show that our approach has good learning and control performance
Minimising tremor in a joystick using fuzzy logic
van der Zwaag, B.J.; Corbett, Dan; Jain, Lakhmi; Kappen, H.J.; Duin, R.P.W.; Krose, B.J.A.; Segeth, W.
We have designed and built a fuzzy logic controller which minimises the effect of Multiple Sclerosis (MS) hand tremors. The aim of our project has been to give people with Multiple Sclerosis better control of an electronic wheelchair by removing tremors from the joystick signal. The system
Capturing hand tremors with a fuzzy logic wheelchair joystick controller
van der Zwaag, B.J.; Corbett, Dan
We have designed and built a fuzzy logic wheelchair controller which minimizes the effect of Multiple Sclerosis and tremors. The aim of our project has been to give people with Multiple Sclerosis better control of an electric wheelchair by removing tremors from the joystick signal. The system
Fuzzy logic application for extruders replacement problem
Directory of Open Access Journals (Sweden)
Edison Conde Perez dos Santos
2017-03-01
Full Text Available In a scenario of uncertainty and imprecision, before taking the replacement analysis, a manager needs to consider the uncertain reality of a problem. In this scenario, the fuzzy logic makes an excellent option. Therefore, it is necessary to make a decision based on the fuzzy model. This study is based on the comparison of two methodologies used in the problem of asset replacement. The study, thus, was based on a comparison between two extruders for polypropylene yarn bibliopegy, comparing mainly the costs involved in maintaining the equipment.
Fuzzy Logic Approaches to Multi-Objective Decision-Making in Aerospace Applications
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.
Application of fuzzy logic to social choice theory
Mordeson, John N; Clark, Terry D
2015-01-01
Fuzzy social choice theory is useful for modeling the uncertainty and imprecision prevalent in social life yet it has been scarcely applied and studied in the social sciences. Filling this gap, Application of Fuzzy Logic to Social Choice Theory provides a comprehensive study of fuzzy social choice theory.The book explains the concept of a fuzzy maximal subset of a set of alternatives, fuzzy choice functions, the factorization of a fuzzy preference relation into the ""union"" (conorm) of a strict fuzzy relation and an indifference operator, fuzzy non-Arrowian results, fuzzy versions of Arrow's
Directory of Open Access Journals (Sweden)
Miguel Ramirez-Gonzalez
2015-01-01
Full Text Available In this paper, the effect of fuzzy logic-based robust power system stabilizers on the improvement of the dynamics of a large-scale power system is investigated. The study is particularly focused on the Mexican Interconnected System and on adding damping to two critical inter-area system oscillation modes: The north-south mode and the western-peninsular mode. The fuzzy power system stabilizers (FPSSs applied here are based on a significantly reduced rule base, small number of tuning parameters, and simple control algorithm and architecture, which makes their design and implementation easier and suitable for practical applications. Non-linear time-domain simulations for a set of test cases and results from Prony Analysis verify the robustness of the designed FPSSs, as compared to conventional PSSs.
Stock and option portfolio using fuzzy logic approach
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.
Directory of Open Access Journals (Sweden)
Yiming Jiang
2016-01-01
Full Text Available Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC and neural network (NN control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.
Searching the Arcane Origins of Fuzzy Logic
Directory of Open Access Journals (Sweden)
Angel Garrido
2011-05-01
Full Text Available It is well-known that Artificial Intelligence requires Logic. But its Classical version shows too many insufficiencies. So, it is very necessary to introduce more sophisticated tools, as may be
Fuzzy Logic, Modal Logic, Non-Monotonic Logic, and so on. When you are searching the possible precedent of such new ideas, we may found that they are not totally new, because some ancient thinkers have suggested many centuries ago similar concepts, certainly without adequate mathematical formulation, but in the same line: against the dogmatism and the dualistic vision of
the world: absolutely true vs. absolutely false, black vs. white, good or bad by nature, 0 vs.1, etc. We attempt to analyze here some of these greatly unexplored, and very interesting early origins.
Fuzzy logic model to quantify risk perception
International Nuclear Information System (INIS)
Bukh, Julia; Dickstein, Phineas
2008-01-01
The aim of this study is a quantification of public risk perception towards the nuclear field so as to be considered in decision making whenever the public involvement is sought. The proposed model includes both qualitative factors such as familiarity and voluntariness and numerical factors influencing risk perception, such as probability of occurrence and severity of consequence. Since part of these factors can be characterized only by qualitative expressions and the determination of them are linked with vagueness, imprecision and uncertainty, the most suitable method for the risk level assessment is Fuzzy Logic, which models qualitative aspects of knowledge and reasoning processes without employing precise quantitative analyses. This work, then, offers a Fuzzy-Logic based mean of representing the risk perception by a single numerical feature, which can be weighted and accounted for in decision making procedures. (author)
Heterogeneous fuzzy logic networks: fundamentals and development studies.
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
Use of fuzzy logic in signal processing and validation
International Nuclear Information System (INIS)
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
Bicycle Frame Prediction Techniques with Fuzzy Logic Method
Directory of Open Access Journals (Sweden)
Rafiuddin Syam
2015-03-01
Full Text Available In general, an appropriate size bike frame would get comfort to the rider while biking. This study aims to predict the simulation system on the bike frame sizes with fuzzy logic. Testing method used is the simulation test. In this study, fuzzy logic will be simulated using Matlab language to test their performance. Mamdani fuzzy logic using 3 variables and 1 output variable intake. Triangle function for the input and output. The controller is designed in the type mamdani with max-min composition and the method deffuzification using center of gravity method. The results showed that height, inseam and Crank Size generating appropriate frame size for the rider associated with comfort. Has a height range between 142 cm and 201 cm. Inseam has a range between 64 cm and 97 cm. Crank has a size range between 175 mm and 180 mm. The simulation results have a range of frame sizes between 13 inches and 22 inches. By using the fuzzy logic can be predicted the size frame of bicycle suitable for the biker.
Bicycle Frame Prediction Techniques with Fuzzy Logic Method
Directory of Open Access Journals (Sweden)
Rafiuddin Syam
2017-03-01
Full Text Available In general, an appropriate size bike frame would get comfort to the rider while biking. This study aims to predict the simulation system on the bike frame sizes with fuzzy logic. Testing method used is the simulation test. In this study, fuzzy logic will be simulated using Matlab language to test their performance. Mamdani fuzzy logic using 3 variables and 1 output variable intake. Triangle function for the input and output. The controller is designed in the type mamdani with max-min composition and the method deffuzification using center of gravity method. The results showed that height, inseam and Crank Size generating appropriate frame size for the rider associated with comfort. Has a height range between 142 cm and 201 cm. Inseam has a range between 64 cm and 97 cm. Crank has a size range between 175 mm and 180 mm. The simulation results have a range of frame sizes between 13 inches and 22 inches. By using the fuzzy logic can be predicted the size frame of bicycle suitable for the biker.
Determination Of Adaptive Control Parameter Using Fuzzy Logic Controller
Directory of Open Access Journals (Sweden)
Omur Can Ozguney
2017-08-01
Full Text Available The robot industry has developed along with the increasing the use of robots in industry. This has led to increase the studies on robots. The most important part of these studies is that the robots must be work with minimum tracking trajectory error. But it is not easy for robots to track the desired trajectory because of the external disturbances and parametric uncertainty. Therefore adaptive and robust controllers are used to decrease tracking error. The aim of this study is to increase the tracking performance of the robot and minimize the trajectory tracking error. For this purpose adaptive control law for robot manipulator is identified and fuzzy logic controller is applied to find the accurate values for adaptive control parameter. Based on the Lyapunov theory stability of the uncertain system is guaranteed. In this study robot parameters are assumed to be unknown. This controller is applied to a robot model and the results of simulations are given. Controller with fuzzy logic and without fuzzy logic are compared with each other. Simulation results show that the fuzzy logic controller has improved the results.
Modelling with ANIMO: between fuzzy logic and differential equations.
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.
FUZZY LOGIC CONTROLLED CATHODIC PROTECTION CIRCUIT DESIGN
AKÇAYOL, M. Ali
2010-01-01
In this study, output voltage of automatic transformer-rectifier (TR) unit of impressed current cathodic protection has been controlled by using fuzzy logic controller. To prevent corrosion, voltage between the protection metal and the auxiliary anode has to be controlled on a desired level. Because soil resistance in the environment changes with humidity and soil characteristics, TRs must control the output voltage between protection metal and auxiliary anode automatically. In this study, a ...
Intelligent control based on fuzzy logic and neural net theory
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.
Directory of Open Access Journals (Sweden)
Akhtar Hussain
2017-02-01
Full Text Available The resiliency of power systems can be enhanced during emergency situations by using microgrids, due to their capability to supply local loads. However, precise prediction of disturbance events is very difficult rather the occurrence probability can be expressed as, high, medium, or low, etc. Therefore, a fuzzy logic-based battery energy storage system (BESS operation controller is proposed in this study. In addition to BESS state-of-charge and market price signals, event occurrence probability is taken as crisp input for the BESS operation controller. After assessing the membership levels of all the three inputs, BESS operation controller decides the operation mode (subservient or resilient of BESS units. In subservient mode, BESS is fully controlled by an energy management system (EMS while in the case of resilient mode, the EMS follows the commands of the BESS operation controller for scheduling BESS units. Therefore, the proposed hybrid microgrid model can operate in normal, resilient, and emergency modes with the respective objective functions and scheduling horizons. Due to the consideration of resilient mode, load curtailment can be reduced during emergency operation periods. Numerical simulations have demonstrated the effectiveness of the proposed strategy for enhancing the resiliency of hybrid microgrids.
El-Sebakhy, Emad A.
2009-09-01
Pressure-volume-temperature properties are very important in the reservoir engineering computations. There are many empirical approaches for predicting various PVT properties based on empirical correlations and statistical regression models. Last decade, researchers utilized neural networks to develop more accurate PVT correlations. These achievements of neural networks open the door to data mining techniques to play a major role in oil and gas industry. Unfortunately, the developed neural networks correlations are often limited, and global correlations are usually less accurate compared to local correlations. Recently, adaptive neuro-fuzzy inference systems have been proposed as a new intelligence framework for both prediction and classification based on fuzzy clustering optimization criterion and ranking. This paper proposes neuro-fuzzy inference systems for estimating PVT properties of crude oil systems. This new framework is an efficient hybrid intelligence machine learning scheme for modeling the kind of uncertainty associated with vagueness and imprecision. We briefly describe the learning steps and the use of the Takagi Sugeno and Kang model and Gustafson-Kessel clustering algorithm with K-detected clusters from the given database. It has featured in a wide range of medical, power control system, and business journals, often with promising results. A comparative study will be carried out to compare their performance of this new framework with the most popular modeling techniques, such as neural networks, nonlinear regression, and the empirical correlations algorithms. The results show that the performance of neuro-fuzzy systems is accurate, reliable, and outperform most of the existing forecasting techniques. Future work can be achieved by using neuro-fuzzy systems for clustering the 3D seismic data, identification of lithofacies types, and other reservoir characterization.
Fuzzy Logic and Education: Teaching the Basics of Fuzzy Logic through an Example (By Way of Cycling)
Sobrino, Alejandro
2013-01-01
Fuzzy logic dates back to 1965 and it is related not only to current areas of knowledge, such as Control Theory and Computer Science, but also to traditional ones, such as Philosophy and Linguistics. Like any logic, fuzzy logic is concerned with argumentation, but unlike other modalities, which focus on the crisp reasoning of Mathematics, it deals…
Fuzzy Logic Decoupled Lateral Control for General Aviation Airplanes
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.
Proceedings of the Second Joint Technology Workshop on Neural Networks and Fuzzy Logic, volume 1
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.
Multi-valued and Fuzzy Logic Realization using TaOx Memristive Devices.
Bhattacharjee, Debjyoti; Kim, Wonjoo; Chattopadhyay, Anupam; Waser, Rainer; Rana, Vikas
2018-01-08
Among emerging non-volatile storage technologies, redox-based resistive switching Random Access Memory (ReRAM) is a prominent one. The realization of Boolean logic functionalities using ReRAM adds an extra edge to this technology. Recently, 7-state ReRAM devices were used to realize ternary arithmetic circuits, which opens up the computing space beyond traditional binary values. In this manuscript, we report realization of multi-valued and fuzzy logic operators with a representative application using ReRAM devices. Multi-valued logic (MVL), such as Łukasiewicz logic generalizes Boolean logic by allowing more than two truth values. MVL also permits operations on fuzzy sets, where, in contrast to standard crisp logic, an element is permitted to have a degree of membership to a given set. Fuzzy operations generally model human reasoning better than Boolean logic operations, which is predominant in current computing technologies. When the available information for the modelling of a system is imprecise and incomplete, fuzzy logic provides an excellent framework for the system design. Practical applications of fuzzy logic include, industrial control systems, robotics, and in general, design of expert systems through knowledge-based reasoning. Our experimental results show, for the first time, that it is possible to model fuzzy logic natively using multi-state memristive devices.
Rutkowski, L; Cpalka, K
2003-01-01
In this paper, we derive new neuro-fuzzy structures called flexible neuro-fuzzy inference systems or FLEXNFIS. Based on the input-output data, we learn not only the parameters of the membership functions but also the type of the systems (Mamdani or logical). Moreover, we introduce: 1) softness to fuzzy implication operators, to aggregation of rules and to connectives of antecedents; 2) certainty weights to aggregation of rules and to connectives of antecedents; and 3) parameterized families of T-norms and S-norms to fuzzy implication operators, to aggregation of rules and to connectives of antecedents. Our approach introduces more flexibility to the structure and design of neuro-fuzzy systems. Through computer simulations, we show that Mamdani-type systems are more suitable to approximation problems, whereas logical-type systems may be preferred for classification problems.
Qing Hu, Bao
2015-11-01
The fuzzy rough set model and interval-valued fuzzy rough set model have been introduced to handle databases with real values and interval values, respectively. Variable precision rough set was advanced by Ziarko to overcome the shortcomings of misclassification and/or perturbation in Pawlak rough sets. By combining fuzzy rough set and variable precision rough set, a variety of fuzzy variable precision rough sets were studied, which cannot only handle numerical data, but are also less sensitive to misclassification. However, fuzzy variable precision rough sets cannot effectively handle interval-valued data-sets. Research into interval-valued fuzzy rough sets for interval-valued fuzzy data-sets has commenced; however, variable precision problems have not been considered in interval-valued fuzzy rough sets and generalized interval-valued fuzzy rough sets based on fuzzy logical operators nor have interval-valued fuzzy sets been considered in variable precision rough sets and fuzzy variable precision rough sets. These current models are incapable of wide application, especially on misclassification and/or perturbation and on interval-valued fuzzy data-sets. In this paper, these models are generalized to a more integrative approach that not only considers interval-valued fuzzy sets, but also variable precision. First, we review generalized interval-valued fuzzy rough sets based on two fuzzy logical operators: interval-valued fuzzy triangular norms and interval-valued fuzzy residual implicators. Second, we propose generalized interval-valued fuzzy variable precision rough sets based on the above two fuzzy logical operators. Finally, we confirm that some existing models, including rough sets, fuzzy variable precision rough sets, interval-valued fuzzy rough sets, generalized fuzzy rough sets and generalized interval-valued fuzzy variable precision rough sets based on fuzzy logical operators, are special cases of the proposed models.
Directory of Open Access Journals (Sweden)
Radi Radi
2011-08-01
Full Text Available Constructive Back Propagation Neural Network (CBPNN is a kind of back propagation neural network trained with constructive algorithm. Training of CBPNN is mainly conducted by developing the network’s architecture which commonly done by adding a number of new neuron units on learning process. Training of the network usually implements fixed method to develop its structure gradually by adding new units constantly. Although this method is simple and able to create an adaptive network for data pattern complexity, but it is wasteful and inefficient for computing. New unit addition affects directly to the computational load of training, speed of convergence, and structure of the final neural network. While increases training load significantly, excessive addition of units also tends to generate a large size of final network. Moreover, addition pattern with small unit number tends to drop off the adaptability of the network and extends time of training. Therefore, there is important to design an adaptive structure development pattern for CBPNN in order to minimize computing load of training. This study proposes Fuzzy Logic (FL algorithm to manage and develop structure of CBPNN. FL method was implemented on two models of CBPNN, i.e. designed with one and two hidden layers, used to recognize aroma patterns on an electronic nose system. The results showed that this method is effective to be applied due to its capability to minimize time of training, to reduce load of computational learning, and generate small size of network.
An Intelligent Trading System with Fuzzy Rules and Fuzzy Capital Management
Naranjo, Rodrigo; Meco, Albert; Arroyo Gallardo, Javier; Santos Peñas, Matilde
2015-01-01
In this work we are proposing a trading system where fuzzy logic is applied not only for defining the trading rules, but also for managing the capital to invest. In fact, two fuzzy decision support systems are developed. The first one uses fuzzy logic to design the trading rules and to apply the stock market technical indicators. The second one enhances this fuzzy trading system adding a fuzzy strategy to manage the capital to trade. Additionally, a new technical market indicator that produce...
Mapping Shape Geometry And Emotions Using Fuzzy Logic
DEFF Research Database (Denmark)
Achiche, Sofiane; Ahmed, Saeema
2008-01-01
and the intended emotion using fuzzy logic. To achieve this; 3D objects (shapes) created by design engineering students to match a set of words/emotions were analyzed. The authors identified geometric information as inputs of the fuzzy model and developed a set of fuzzy if/then rules to map the relationships...... between the fuzzy sets on each input premise and the output premise. In our case the output premise of the fuzzy logic model is the level of belonging to the design context (emotion). An evaluation of how users perceived the shapes was conducted to validate the fuzzy logic model and showed a high...... correlation between the fuzzy logic model and user perception....
SISTEM PENGEMBANGAN KENDALI FUZZY LOGIC BERBASIS MIKROKONTROLER KELUARGA MCS51 (PetraFuz
Directory of Open Access Journals (Sweden)
Thiang Thiang
1999-01-01
Full Text Available This paper presents a Fuzzy Logic Development Tool called PetraFuz which has been developed at Control System Laboratory, Electrical Engineering Department, Petra Christian University. The system consists of a hardware target based on MCS51 microcontroller and a software support running under PC Windows. The system is targeted for developing fuzzy logic based systems. It supports fuzzy logic design, evaluation, assembly language generator and downloading process to the target hardware to perform on-line fuzzy process. Process action and fuzzy parameters could be transferred to PC monitor via RS-232 serial communication, this on-line process parameters is used for fuzzy tuning, i.e. fuzzy if-then rules and fuzzy membership functions. The PetraFuz tool helps very much for Fuzzy system developments, it could reduce development time significantly. The tool could spur the development of fuzzy systems based on microcontroller systems such as fuzzy control systems, fuzzy information processing, etc. Abstract in Bahasa Indonesia : Makalah ini menyajikan sebuah sistem pengembangan kendali fuzzy logic (PetraFuz, Petra Fuzzy Development System yang dikembangkan oleh laboratorium Sistem Kontrol, Jurusan Teknik Elektro, Universitas Kristen Petra Surabaya. Sistem ini terdiri dari perangkat keras sistem mikrokontroler MCS51 dan perangkat lunak pendukung yang berjalan pada PC. Sistem PetraFuz digunakan untuk mengembangkan sistem berbasis fuzzy logic utamanya pada bidang kendali. Kemampuan sistem meliputi pengembangan pada fase perancangan kendali, evaluasi kendali, pembentukan program bahasa assembly MCS51 dan proses downloading program menuju target sistem mikrokontroler MCS51 untuk dieksekusi melakukan kendali pada plant yang nyata. Aksi kendali dapat diakuisi oleh program PC melalui komunikasi serial RS232 sehingga respon kendali dapat digambarkan pada layar monitor untuk dilakukan analisis lebih lanjut yang diperlukan pada proses tuning if-then fuzzy rules
Paraconsistency properties in degree-preserving fuzzy logics
Czech Academy of Sciences Publication Activity Database
Ertola, R.; Esteva, F.; Flaminio, T.; Godo, L.; Noguera, Carles
2015-01-01
Roč. 19, č. 3 (2015), s. 531-546 ISSN 1432-7643 R&D Projects: GA ČR GAP202/10/1826 Institutional support: RVO:67985556 Keywords : Mathematical fuzzy logic * degree-preserving fuzzy logics * paraconsistent logics * logics of formal inconsistency Subject RIV: BA - General Mathematics Impact factor: 1.630, year: 2015 http://library.utia.cas.cz/separaty/2016/MTR/noguera-0469166.pdf
Fuzzy Logic in Inverse Continuous Method
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Víťazoslav Krúpa
2004-12-01
Full Text Available In the field of geotechnics, certain vagueness and ambiquity appears. We might not be able to design a mathematically accuratedescription of rock, whose properties change during the excavation (rock strength, discontinuities direction, dislocations, rock type.Furthermore, the excavation regime (thrust, revolutions, torque changes too, as well as the edge angle of cutting tools (due to wear andworking ability of cutterhead as result of sequential exchanges of worn-out cutterhead discs. All of these facts cause that the cutterheadoperates using the discs with different wear stage. The above mentioned problems led us to the decision to use the fuzzy logic and fuzzy sets,e.g. techniques operating with vagueness and ambiguity.
Edge detection methods based on generalized type-2 fuzzy logic
Gonzalez, Claudia I; Castro, Juan R; Castillo, Oscar
2017-01-01
In this book four new methods are proposed. In the first method the generalized type-2 fuzzy logic is combined with the morphological gra-dient technique. The second method combines the general type-2 fuzzy systems (GT2 FSs) and the Sobel operator; in the third approach the me-thodology based on Sobel operator and GT2 FSs is improved to be applied on color images. In the fourth approach, we proposed a novel edge detec-tion method where, a digital image is converted a generalized type-2 fuzzy image. In this book it is also included a comparative study of type-1, inter-val type-2 and generalized type-2 fuzzy systems as tools to enhance edge detection in digital images when used in conjunction with the morphologi-cal gradient and the Sobel operator. The proposed generalized type-2 fuzzy edge detection methods were tested with benchmark images and synthetic images, in a grayscale and color format. Another contribution in this book is that the generalized type-2 fuzzy edge detector method is applied in the preproc...
Driver's Behavior Modeling Using Fuzzy Logic
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Sehraneh Ghaemi
2010-01-01
Full Text Available In this study, we propose a hierarchical fuzzy system for human in a driver-vehicle-environment system to model takeover by different drivers. The driver's behavior is affected by the environment. The climate, road and car conditions are included in fuzzy modeling. For obtaining fuzzy rules, experts' opinions are benefited by means of questionnaires on effects of parameters such as climate, road and car conditions on driving capabilities. Also the precision, age and driving individuality are used to model the driver's behavior. Three different positions are considered for driving and decision making. A fuzzy model called Model I is presented for modeling the change of steering angle and speed control by considering time distances with existing cars in these three positions, the information about the speed and direction of car, and the steering angle of car. Also we obtained two other models based on fuzzy rules called Model II and Model III by using Sugeno fuzzy inference. Model II and Model III have less linguistic terms than Model I for the steering angle and direction of car. The results of three models are compared for a driver who drives based on driving laws.
Fuzzy Logics with an Additional Involutive Negation
Czech Academy of Sciences Publication Activity Database
Cintula, Petr; Klement, E.P.; Mesiar, R.; Navara, M.
2010-01-01
Roč. 161, č. 3 (2010), s. 390-411 ISSN 0165-0114 R&D Projects: GA AV ČR KJB100300502; GA MŠk(CZ) 1M0545 Grant - others:Grantová agentura SR(SK) VEGA1/4209/07 Institutional research plan: CEZ:AV0Z10300504 Keywords : triangular norm * triangular conorm * involutive negation * mathematical fuzzy logic * lattice of varieties * compactness * computational complexity Subject RIV: BA - General Mathematics Impact factor: 1.875, year: 2010
FUZZY LOGIC CONTROLLER IMPLEMENTATION FOR PHOTOVOLTAIC STATION
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Imad Zein
2014-01-01
Full Text Available Solar panels have a nonlinear voltage-current characteristic, with a distinct maximum power point (MPP, which depends on the environmental factors, such as temperature and irradiation. In order to continuously harvest maximum power from the solar panels, they have to operate at their MPP despite the inevitable changes in the environment. This is why the controllers of all solar power electronic converters employ some method for maximum power point tracking (MPPT . Over the past years many MPPT techniques have been published and based on that the main paper’s objective is to analyze one of the most promising MPPT control algorithms: fuzzy logic controller.
Fuzzy-logic optical optimization of mainframe CPU and memory
Zalevsky, Zeev; Gur, Eran; Mendlovic, David
2006-07-01
The allocation of CPU time and memory resources is a familiar problem in organizations with a large number of users and a single mainframe. Usually the amount of resources allocated to a single user is based on the user's own statistics not on the statistics of the entire organization, therefore patterns are not well identified and the allocation system is prodigal. A fuzzy-logic-based algorithm to optimize the CPU and memory distribution among users based on their history is suggested. The algorithm works on heavy and light users separately since they present different patterns to be observed. The result is a set of rules generated by the fuzzy-logic inference engine that will allow the system to use its computing ability in an optimized manner. Test results on data taken from the Faculty of Engineering of Tel Aviv University demonstrate the capabilities of the new algorithm.
Classification of Children Intelligence with Fuzzy Logic Method
Syahminan; ika Hidayati, Permata
2018-04-01
Intelligence of children s An Important Thing To Know The Parents Early on. Typing Can be done With a Child’s intelligence Grouping Dominant Characteristics Of each Type of Intelligence. To Make it easier for Parents in Determining The type of Children’s intelligence And How to Overcome them, for It Created A Classification System Intelligence Grouping Children By Using Fuzzy logic method For determination Of a Child’s degree of intelligence type. From the analysis We concluded that The presence of Intelligence Classification systems Pendulum Children With Fuzzy Logic Method Of determining The type of The Child’s intelligence Can be Done in a way That is easier And The results More accurate Conclusions Than Manual tests.
Fuzzy logic, neural networks, and soft computing
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
Zaninelli, Mauro; Tangorra, Francesco Maria; Costa, Annamaria; Rossi, Luciana; Dell'Orto, Vittorio; Savoini, Giovanni
2016-07-13
The aim of this study was to develop and test a new fuzzy logic model for monitoring the udder health status (HS) of goats. The model evaluated, as input variables, the milk electrical conductivity (EC) signal, acquired on-line for each gland by a dedicated sensor, the bandwidth length and the frequency and amplitude of the first main peak of the Fourier frequency spectrum of the recorded milk EC signal. Two foremilk gland samples were collected from eight Saanen goats for six months at morning milking (lactation stages (LS): 0-60 Days In Milking (DIM); 61-120 DIM; 121-180 DIM), for a total of 5592 samples. Bacteriological analyses and somatic cell counts (SCC) were used to define the HS of the glands. With negative bacteriological analyses and SCC 1,000,000 cells/mL, glands were classified as not healthy (NH). For each EC signal, an estimated EC value was calculated and a relative deviation was obtained. Furthermore, the Fourier frequency spectrum was evaluated and bandwidth length, frequency and amplitude of the first main peak were identified. Before using these indexes as input variables of the fuzzy logic model a linear mixed-effects model was developed to evaluate the acquired data considering the HS, LS and LS × HS as explanatory variables. Results showed that performance of a fuzzy logic model, in the monitoring of mammary gland HS, could be improved by the use of EC indexes derived from the Fourier frequency spectra of gland milk EC signals recorded by on-line EC sensors.
SPEED CONTROL OF DC MOTOR ON LOAD USING FUZZY LOGIC ...
African Journals Online (AJOL)
This paper presents the development of a fuzzy logic controller for the driver DC motor in the lube oil system of the H25 Hitachi gas turbine generator. The turbine generator is required to run at an operating pressure of 1.5bar with the low and the high pressure trip points being 0.78 bar and 1.9 bar respectively. However, the ...
Development of fuzzy logic algorithm for water purification plant
SUDESH SINGH RANA; SUDESH SINGH RANA
2015-01-01
This paper propose the design of FLC algorithm for industrial application such application is water purification plant. In the water purification plant raw water or ground water is promptly purified by injecting chemical at rates related to water quality. The feed of chemical rates judged and determined by the skilled operator. Yagishita et al.[1] structured a system based on fuzzy logic so that the feed rate of the coagulant can be judged automatically without any skilled operator. We perfor...
Learning and tuning fuzzy logic controllers through reinforcements
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.
The Assessment of Ramp Metering Based on Fuzzy Logic
Taale, H.; Slager, Jan; Rosloot, Jeroen
1996-01-01
This paper deals with an assessment project and its results of an experiment with ramp metering based on fuzzy logic. In industrial processes and home appliances the control method based on fuzzy logic is being used more and
more. In traffic control however the use of this method is still in a
Comparison of Anti-Virus Programs using Fuzzy Logic
Directory of Open Access Journals (Sweden)
Vaclav Bezdek
2013-07-01
Full Text Available This work follows the previous author´s paper: Possible use of Fuzzy Logic in Database. It tries to show application of Fuzzy Logic in selecting the best anti-virus software based on testing made by AV-Comparatives.
A critical study of fuzzy logic as a scientific method in social sciences ...
African Journals Online (AJOL)
The logic of the social sciences, from its inception, has been certain and classic. By advent of Fuzzy logic, gradually making use of it was common because of frequent capabilities and applications that in resolving problems of this science was been attributed to it. Changing of logic in a science or epistemic system has many ...
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
Energy Technology Data Exchange (ETDEWEB)
Kish, Laszlo B. [Texas A and M University, Department of Electrical and Computer Engineering, College Station, TX 77843-3128 (United States)], E-mail: laszlo.kish@ece.tamu.edu
2009-03-02
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.
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
International Nuclear Information System (INIS)
Kish, Laszlo B.
2009-01-01
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
Noise-based logic: Binary, multi-valued, or fuzzy, with optional superposition of logic states
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.
Fuzzy Logic Decoupled Longitudinal Control for General Aviation Airplanes
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.
Directory of Open Access Journals (Sweden)
Izadbakhsh Maziar
2014-12-01
Full Text Available The microgrid (MG technology integrates distributed generations, energy storage elements and loads. In this paper, dynamic performance enhancement of an MG consisting of wind turbine was investigated using permanent magnet synchronous generation (PMSG, photovoltaic (PV, microturbine generation (MTG systems and flywheel under different circumstances. In order to maximize the output of solar arrays, maximum power point tracking (MPPT technique was used by an adaptive neuro-fuzzy inference system (ANFIS; also, control of turbine output power in high speed winds was achieved using pitch angle control technic by fuzzy logic. For tracking the maximum point, the proposed ANFIS was trained by the optimum values. The simulation results showed that the ANFIS controller of grid-connected mode could easily meet the load demand with less fluctuation around the maximum power point. Moreover, pitch angle controller, which was based on fuzzy logic with wind speed and active power as the inputs, could have faster responses, thereby leading to flatter power curves, enhancement of the dynamic performance of wind turbine and prevention of both frazzle and mechanical damages to PMSG. The thorough wind power generation system, PV system, MTG, flywheel and power electronic converter interface were proposed by using Mat-lab/Simulink.
Directory of Open Access Journals (Sweden)
Yu-Shan Cheng
2018-02-01
Full Text Available Self-consumption of household photovoltaic (PV storage systems has become profitable for residential owners under the trends of limited feed-in power and decreasing PV feed-in tariffs. For individual PV-storage systems, the challenge mainly lies in managing surplus generation of battery and grid power flow, ideally without relying on error-prone forecasts for both generation and consumption. Considering the large variation in power profiles of different houses in a neighborhood, the strategy is also supposed to be beneficial and applicable for the entire community. In this study, an adaptable battery charging control strategy is designed in order to obtain minimum costs for houses without any meteorological or load forecasts. Based on fuzzy logic control (FLC, battery state-of-charge (SOC and the variation of SOC (∆SOC are taken as input variables to dynamically determine output charging power with minimum costs. The proposed FLC-based algorithm benefits from the charging battery as much as possible during the daytime, and meanwhile properly preserves the capacity at midday when there is high possibility of curtailment loss. In addition, due to distinct power profiles in each individual house, input membership functions of FLC are improved by particle swarm optimization (PSO to achieve better overall performance. A neighborhood with 74 houses in Germany is set up as a scenario for comparison to prior studies. Without forecasts of generation and consumption power, the proposed method leads to minimum costs in 98.6% of houses in the community, and attains the lowest average expenses for a single house each year.
WARP: Weight Associative Rule Processor. A dedicated VLSI fuzzy logic megacell
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.
An architecture for designing fuzzy logic controllers using neural networks
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.
DC motor speed control using fuzzy logic controller
Ismail, N. L.; Zakaria, K. A.; Nazar, N. S. Moh; Syaripuddin, M.; Mokhtar, A. S. N.; Thanakodi, S.
2018-02-01
The automatic control has played a vital role in the advance of engineering and science. Nowadays in industries, the control of direct current (DC) motor is a common practice thus the implementation of DC motor controller speed is important. The main purpose of motor speed control is to keep the rotation of the motor at the present speed and to drive a system at the demand speed. The main purpose of this project is to control speed of DC Series Wound Motor using Fuzzy Logic Controller (FLC). The expectation of this project is the Fuzzy Logic Controller will get the best performance compared to dc motor without controller in terms of settling time (Ts), rise time (Tr), peak time (Tp) and percent overshoot (%OS).
SYSTEM OF PRECISE DOSING OF COAGULANT IN THE PULVERIZING AERATOR POWERED BY WIND USING FUZZY LOGIC
Directory of Open Access Journals (Sweden)
Andrzej Osuch
2017-06-01
Full Text Available One of the methods used to support land restoration lakes is the method of pulverizing aeration. Use of aerators powered exclusively by wind improves the condition of reservoirs, while not compromising the environment. The pulverizing aeration process drive is windy on the water aeration zone near bottom, while removing harmful gases anaerobic metabolism. Aerators of this type due to the unique method of operation also enable dosing of inactivation coagulants with oxygenated water to the depths of the lake. Mileage coagulant dosing can be made dependent on the speed of the wind, which has an impact on the performance of his work, because with the increase of wind speed dispensing valve coagulants should be stronger open. One of the methods for assessing the state of lakes is to measure water transparency. The softer visibility, the most likely state of the water is better. Dosage of coagulant so you can make the transparency of the water. Similarly, with increasing transparency water dispensing valve should be more covered up. Control of the drain valve dispenser coagulant can be simultaneously dependent on two factors. The study was designed method of control drain valve dispenser coagulant using fuzzy inference.
Direct Torque Control of Asynchronous Motor With Fuzzy Logic Swithching
KORKMAZ, Fatih; KORKMAZ, Yılmaz
2011-01-01
control method in asynchronous motors, are known as high speed and torque ripples. In this study, direct torque control with fuzzy logic based switching method have been studied in order to reduce the speed and torque ripples which occurs during the direct torque control of asynchronous motors. Hysteresis controllers and vector selector that used in conventional control were removed, and fuzzy logic based switching method was used instead of them. Conventional and fuzzy control methods were s...
Control Augmentation Using Fuzzy Logic Control
Kato, Akio; Inukai, Daisuke
Overall control to improve the control characteristics of an aircraft, CA (Control Augmentation), is used to realize the desirable motion of the aircraft in relation to the pilot’s control action. C∗ criterion is an important factor for the pilot’s preferred longitudinal motion. The time history of C∗ corresponding to the step input is specified within the upper and lower envelope, and it is desirable to be near the center of the envelope. In this research, the control laws of control augmentation for small supersonic aircraft were designed with the use of fuzzy logic control to obtain the C∗ response near the center of the envelope. The evaluation of the designed control laws showed good performance in all flight conditions. Here the control laws were varied by only one scaling factor for dynamic pressure. This means that virtually no gain schedules by the Mach number and the angle of attack are necessary. This paper shows that fuzzy logic control is an effective and flexible method when applied to control laws for the control augmentation of aircraft.
Rule based fuzzy logic approach for classification of fibromyalgia syndrome.
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
Modelling Of Anticipated Damage Ratio On Breakwaters Using Fuzzy Logic
Mercan, D. E.; Yagci, O.; Kabdasli, S.
2003-04-01
In breakwater design the determination of armour unit weight is especially important in terms of the structure's life. In a typical experimental breakwater stability study, different wave series composed of different wave heights; wave period and wave steepness characteristics are applied in order to investigate performance the structure. Using a classical approach, a regression equation is generated for damage ratio as a function of characteristic wave height. The parameters wave period and wave steepness are not considered. In this study, differing from the classical approach using a fuzzy logic, a relationship between damage ratio as a function of mean wave period (T_m), wave steepness (H_s/L_m) and significant wave height (H_s) was further generated. The system's inputs were mean wave period (T_m), wave steepness (H_s/L_m) and significant wave height (H_s). For fuzzification all input variables were divided into three fuzzy subsets, their membership functions were defined using method developed by Mandani (Mandani, 1974) and the rules were written. While for defuzzification the centroid method was used. In order to calibrate and test the generated models an experimental study was conducted. The experiments were performed in a wave flume (24 m long, 1.0 m wide and 1.0 m high) using 20 different irregular wave series (P-M spectrum). Throughout the study, the water depth was 0.6 m and the breakwater cross-sectional slope was 1V/2H. In the armour layer, a type of artificial armour unit known as antifer cubes were used. The results of the established fuzzy logic model and regression equation model was compared with experimental data and it was determined that the established fuzzy logic model gave a more accurate prediction of the damage ratio on this type of breakwater. References Mandani, E.H., "Application of Fuzzy Algorithms for Control of Simple Dynamic Plant", Proc. IEE, vol. 121, no. 12, December 1974.
On the Difference between Traditional and Deductive Fuzzy Logic
Czech Academy of Sciences Publication Activity Database
Běhounek, Libor
2008-01-01
Roč. 159, č. 10 (2008), s. 1153-1164 ISSN 0165-0114 R&D Projects: GA AV ČR KJB100300502 Institutional research plan: CEZ:AV0Z10300504 Keywords : deductive fuzzy logic * fuzzy elements * gradual sets * entropy of fuzzy sets * aggregation * membership degrees * methodology of fuzzy mathematics Subject RIV: BA - General Mathematics Impact factor: 1.833, year: 2008
High-efficiency induction motor drives using type-2 fuzzy logic
Khemis, A.; Benlaloui, I.; Drid, S.; Chrifi-Alaoui, L.; Khamari, D.; Menacer, A.
2018-03-01
In this work we propose to develop an algorithm for improving the efficiency of an induction motor using type-2 fuzzy logic. Vector control is used to control this motor due to the high performances of this strategy. The type-2 fuzzy logic regulators are developed to obtain the optimal rotor flux for each torque load by minimizing the copper losses. We have compared the performances of our fuzzy type-2 algorithm with the type-1 fuzzy one proposed in the literature. The proposed algorithm is tested with success on the dSPACE DS1104 system even if there is parameters variance.
Fuzzy logic controllers and chaotic natural convection loops
International Nuclear Information System (INIS)
Theler, German
2007-01-01
The study of natural circulation loops is a subject of special concern for the engineering design of advanced nuclear reactors, as natural convection provides an efficient and completely passive heat removal system. However, under certain circumstances thermal-fluid-dynamical instabilities may appear, threatening the reactor safety as a whole.On the other hand, fuzzy logic controllers provide an ideal framework to approach highly non-linear control problems. In the present work, we develop a software-based fuzzy logic controller and study its application to chaotic natural convection loops.We numerically analyse the linguistic control of the loop known as the Welander problem in such conditions that, if the controller were not present, the circulation flow would be non-periodic unstable.We also design a Taka gi-Sugeno fuzzy controller based on a fuzzy model of a natural convection loop with a toroidal geometry, in order to stabilize a Lorenz-chaotic behaviour.Finally, we show experimental results obtained in a rectangular natural circulation loop [es
ANFIS optimized semi-active fuzzy logic controller for magnetorheological dampers
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.
Analysis of Learning Development With Sugeno Fuzzy Logic And Clustering
Directory of Open Access Journals (Sweden)
Maulana Erwin Saputra
2017-06-01
Full Text Available In the first journal, I made this attempt to analyze things that affect the achievement of students in each school of course vary. Because students are one of the goals of achieving the goals of successful educational organizations. The mental influence of students’ emotions and behaviors themselves in relation to learning performance. Fuzzy logic can be used in various fields as well as Clustering for grouping, as in Learning Development analyzes. The process will be performed on students based on the symptoms that exist. In this research will use fuzzy logic and clustering. Fuzzy is an uncertain logic but its excess is capable in the process of language reasoning so that in its design is not required complicated mathematical equations. However Clustering method is K-Means method is method where data analysis is broken down by group k (k = 1,2,3, .. k. To know the optimal number of Performance group. The results of the research is with a questionnaire entered into matlab will produce a value that means in generating the graph. And simplify the school in seeing Student performance in the learning process by using certain criteria. So from the system that obtained the results for a decision-making required by the school.
Efficiency of particle swarm optimization applied on fuzzy logic DC motor speed control
Directory of Open Access Journals (Sweden)
Allaoua Boumediene
2008-01-01
Full Text Available This paper presents the application of Fuzzy Logic for DC motor speed control using Particle Swarm Optimization (PSO. Firstly, the controller designed according to Fuzzy Logic rules is such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC the designed FLC-PSO speed controller obtains better dynamic behavior and superior performance of the DC motor, as well as perfect speed tracking with no overshoot.
Virtual reality simulation of fuzzy-logic control during underwater dynamic positioning
Thekkedan, Midhin Das; Chin, Cheng Siong; Woo, Wai Lok
2015-03-01
In this paper, graphical-user-interface (GUI) software for simulation and fuzzy-logic control of a remotely operated vehicle (ROV) using MATLAB™ GUI Designing Environment is proposed. The proposed ROV's GUI platform allows the controller such as fuzzy-logic control systems design to be compared with other controllers such as proportional-integral-derivative (PID) and sliding-mode controller (SMC) systematically and interactively. External disturbance such as sea current can be added to improve the modelling in actual underwater environment. The simulated results showed the position responses of the fuzzy-logic control exhibit reasonable performance under the sea current disturbance.
Fuzzy logic feedback control for fed-batch enzymatic hydrolysis of lignocellulosic biomass.
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.
Energy Technology Data Exchange (ETDEWEB)
Lagunas, J.; Caratozzolo, P.; Ortega, C.; Gonzalez, R.
2004-07-01
This paper presents and validates the use of a fuzzy-logic-based supervisory controller for the management of energy in a hybrid system. The general configuration of the hybrid system is presented as well as the operational objectives of the supervisory controller. The inputs and outputs of the controller are also presented along with the hierarchical structure employed in order to reduce the number of rules in the knowledge base. The results obtained are compared against those of a conventional controller. Simulations were carried out using Matlab. (Author)
Design of interpretable fuzzy systems
Cpałka, Krzysztof
2017-01-01
This book shows that the term “interpretability” goes far beyond the concept of readability of a fuzzy set and fuzzy rules. It focuses on novel and precise operators of aggregation, inference, and defuzzification leading to flexible Mamdani-type and logical-type systems that can achieve the required accuracy using a less complex rule base. The individual chapters describe various aspects of interpretability, including appropriate selection of the structure of a fuzzy system, focusing on improving the interpretability of fuzzy systems designed using both gradient-learning and evolutionary algorithms. It also demonstrates how to eliminate various system components, such as inputs, rules and fuzzy sets, whose reduction does not adversely affect system accuracy. It illustrates the performance of the developed algorithms and methods with commonly used benchmarks. The book provides valuable tools for possible applications in many fields including expert systems, automatic control and robotics.
On logical, algebraic, and probabilistic aspects of fuzzy set theory
Mesiar, Radko
2016-01-01
The book is a collection of contributions by leading experts, developed around traditional themes discussed at the annual Linz Seminars on Fuzzy Set Theory. The different chapters have been written by former PhD students, colleagues, co-authors and friends of Peter Klement, a leading researcher and the organizer of the Linz Seminars on Fuzzy Set Theory. The book also includes advanced findings on topics inspired by Klement’s research activities, concerning copulas, measures and integrals, as well as aggregation problems. Some of the chapters reflect personal views and controversial aspects of traditional topics, while others deal with deep mathematical theories, such as the algebraic and logical foundations of fuzzy set theory and fuzzy logic. Originally thought as an homage to Peter Klement, the book also represents an advanced reference guide to the mathematical theories related to fuzzy logic and fuzzy set theory with the potential to stimulate important discussions on new research directions in the fiel...
Control of beam halo-chaos using fuzzy logic controller
International Nuclear Information System (INIS)
Gao Yuan; Yuan Haiying; Tan Guangxing; Luo Wenguang
2012-01-01
Considering the ion beam with initial K-V distribution in the periodic focusing magnetic filed channels (PFCs) as a typical sample, a fuzzy control method for control- ling beam halo-chaos was studied. A fuzzy proportional controller, using output of fuzzy inference as a control factor, was presented for adjusting exterior focusing magnetic field. The stability of controlled system was proved by fuzzy phase plane analysis. The simulation results demonstrate that the chaotic radius of envelope can be controlled to the matched radius via controlling magnetic field. This method was also applied to the multi-particle model. Under the control condition, the beam halos and its regeneration can be eliminated effectively, and that both the compactness and the uniformity of ion beam are improved evidently. Since the exterior magnetic field can be rather easily adjusted by proportional control and the fuzzy logic controller is independent to the mathematical model, this method has adaptive ability and is easily realized in experiment. The research offers a valuable reference for the design of the PFCs in the high- current linear ion accelerators. (authors)
Improved Step Response of Power System Stabilizer Using Fuzzy Logic Controller
Kiran, Nagulapati; Kumar, M. Sudheer; Raju, M. Naga
2014-01-01
As every power system is constantly being subjected to disturbances, we should see that these disturbances do not make the system unstable. Therefor additional signals derived from speed deviation, excitation deviation and accelerating power are injected into voltage regulators. The device to provide these signals is referred as power system stabilizer. The use of power system stabilizers has become very common in operation of large electric power systems. The conventional PSS which uses lead...
Zohuri, Bahman
2017-01-01
This book provides a technical approach to a Business Resilience System with its Risk Atom and Processing Data Point based on fuzzy logic and cloud computation in real time. Its purpose and objectives define a clear set of expectations for Organizations and Enterprises so their network system and supply chain are totally resilient and protected against cyber-attacks, manmade threats, and natural disasters. These enterprises include financial, organizational, homeland security, and supply chain operations with multi-point manufacturing across the world. Market shares and marketing advantages are expected to result from the implementation of the system. The collected information and defined objectives form the basis to monitor and analyze the data through cloud computation, and will guarantee the success of their survivability's against any unexpected threats. This book will be useful for advanced undergraduate and graduate students in the field of computer engineering, engineers that work for manufacturing com...
International Nuclear Information System (INIS)
Guth, M.A.S.
1987-01-01
This paper applies the method of assigning probability in Dempster-Shafer Theory (DST) to the components of rule-based expert systems used in the control of nuclear reactors. Probabilities are assigned to premises, consequences, and rules themselves. This paper considers how uncertainty can propagate through a system of Boolean equations, such as fault trees or expert systems. The probability masses assigned to primary initiating events in the expert system can be derived from observing a nuclear reactor in operation or based on engineering knowledge of the reactor parts. Use of DST mass assignments offers greater flexibility to the construction of expert systems
Czech Academy of Sciences Publication Activity Database
Hájek, Petr; Harmancová, Dagmar
2000-01-01
Roč. 8, č. 4 (2000), s. 495-498 ISSN 0218-4885 Grant - others:COST(XE) Action 15 Institutional research plan: AV0Z1030915 Keywords : fuzzy logic * Gödel logic * intuitionistic logic * hedges Subject RIV: BA - General Mathematics Impact factor: 0.145, year: 2000
Interdisciplinarity, logic of uncertainty and fuzzy logic in primary school
Directory of Open Access Journals (Sweden)
Luciana Delli Rocili
2015-12-01
Full Text Available On the occasion of the 120th anniversary of Mathesis, this work wants to be a memory, a tribute to two great presidents of Mathesis: Bruno de Finetti and Angelo Fadini. Both have pursued the idea of interdisciplinary teaching and research. Bruno de Finetti, with his books on The invention of truth, (1934, and on Logic and Intuitive Mathematics, (1959, and his very famous "Theory of probability", (1970, shows a rejection of formal education, comfortable, monodisciplinary, made of certainties, and chooses the impervious way of addressing the problems that are to the base of science. Angelo Fadini, with his papers and books on Theory of Fuzzy Sets, shows first in Italy several logical questions which puts as the basis for practical applications in Architecture. This paper is an attempt to experiment, in an interdisciplinary framework, the basic ideas of Bruno de Finetti and Angelo Fadini in primary school, in the belief that in the Primary School are formed ideas and intuitions, while in the secondary school the attention is focused mainly on specific issues of Mathematics. We shows some results of a still ongoing experimentation. Interdisciplinarietà, logica dell'incerto e logica sfumata nella scuola primaria In occasione dei 120 anni della Mathesis, questo lavoro vuole essere un ricordo, un omaggio a due grandi Presidenti della Mathesis: Bruno de Finetti e Angelo Fadini. Entrambi hanno portato avanti l’idea della interdisciplinarietà nell’insegnamento e nella ricerca. Bruno de Finetti, con la sua “Matematica Logico Intuitiva” del 1959, e la sua “Teoria delle probabilità”, del 1970, e ancora prima, con “L’invenzione della verità”, del 1934, mostra un rifiuto dell’insegnamento formale, comodo, monodisciplinare, fatto di certezze, e sceglie la strada impervia dell’affrontare i problemi che sono alla base della scienza. Angelo Fadini, con la sua Teoria degli Insiemi Sfocati, mostra per primo in Italia varie questioni
International Nuclear Information System (INIS)
Porto Junior, Almir Carlos Soares
2014-01-01
The question of components reliability, specifically process control valves, has become an important issue to be investigated in nuclear power plants and other areas such as refinery or offshore oil rig, considering the safety and life extension of the plant. The development of non intrusive monitoring and diagnostic method allows the identification of defects in components of the plant during normal operation. The objective of this dissertation is to present an analysis and diagnosis of control valves of a steam plant part that simulates the secondary circuit of a pressurized water reactor. This installation is part of propulsion equipment testing laboratory of the Brazilian Navy, at Ipero-SP. The methodology for design is based on graphical analysis of two parameters, the valve air pressure actuator and the displacement of the valve plug. These data are extracted by a smart positioner, part of Delta V™ Automation System. An analysis is implemented in detecting anomalies by an approach using Expert Systems by the technique of fuzzy logic. Once the basic measures of control valves are taken, it is possible to detect symptoms of failure, leakage, friction, damage, etc. The monitoring and diagnostic system has been designed in MATLAB® version 2009 th by the complement 'Fuzzy Logic Toolbox'. It is a noninvasive technique. Thus, it is possible to know what is happening with the chosen components, just analyzing the parameters of the valve. The software called ValveLink® (developed by Emerson) receives signals from hardware component (intelligent positioner) installed next to the control valve. These signals (electrical current) are transformed into information which are used input parameters: air pressure valve actuator and valve plug displacement. With the use of fuzzy logic, these parameters are interpreted. They suffer inferences by rules written by experts in valves. After these inferences, the information is processed and sent as output signals
Petr Hájek on mathematical fuzzy logic
Montagna, Franco
2014-01-01
This volume celebrates the work of Petr Hájek on mathematical fuzzy logic and presents how his efforts have influenced prominent logicians who are continuing his work. The book opens with a discussion on Hájek's contribution to mathematical fuzzy logic and with a scientific biography of him, progresses to include two articles with a foundation flavour, that demonstrate some important aspects of Hájek's production, namely, a paper on the development of fuzzy sets and another paper on some fuzzy versions of set theory and arithmetic. Articles in the volume also focus on the treatment of vague
International Nuclear Information System (INIS)
Kang, Ki Sig
1995-02-01
The on - line Operator Aid SYStem (OASYS) has been developed to support operator's decision making process and to ensure the safety of nuclear power plants (NPPs) by timely providing operators with proper guidelines according to the plant operation mode. The OASYS consists of four systems such as the signal validation and management system (SVMS), the plant monitoring system (PMS), the alarm filtering and diagnostic system (AFDS), and the dynamic emergency procedure tracking system (DEPTS). The SVMS and the PMS help operators to maintain a plant as a normal operation condition. The AFDS covers the abnormal events until they result in exceeding the limit range of reactor trip signals, while after a reactor trip, the DEPTS aids operators with proper guidelines so as to shutdown safely. The OASYS uses a rule based expert system and a fuzzy logic. The rule based expert system is used to classify the pre-defined events and track the emergency operating procedures (EOPs) through data processing. The fuzzy logic is used to generate the conceptual high level alarms for the prognostic diagnosis and to evaluate the qualitative fuzzy criteria used in EOPs. Performance assessment of the OASYS demonstrates that it is capable of diagnosing plant abnormal conditions and providing operators appropriate guidelines with fast response time and consistency. The developed technology for OASYS will be used to design the Integrated Advanced Control Room in which a plant can be operated by one operator during normal operation. The advanced EOP for emergency operation has been developed by focusing attention on the importance of the operators' role in emergency conditions. To overcome the complexity of current EOPs and maintain the consistency of operators' action according to plant emergency conditions, operator's tasks were allocated according to their duties in the advanced EOP and the computerized operator aid system (COAS) has been developed as an alternative to reduce operator
Closed Loop Fuzzy Logic Controlled PV Based Cascaded Boost Five-Level Inverter System
Revana, Guruswamy; Kota, Venkata Reddy
2018-04-01
Recent developments in intelligent control methods and power electronics have produced PV based DC to AC converters related to AC drives. Cascaded boost converter and inverter find their way in interconnecting PV and Induction Motor. This paper deals with digital simulation and implementation of closed loop controlled five-level inverter based Photo-Voltaic (PV) system. The objective of this work is to reduce the harmonics using Multi Level Inverter based system. The DC output from the PV panel is boosted using cascaded-boost-converters. The DC output of these cascaded boost converters is applied to the bridges of the cascaded inverter. The AC output voltage is obtained by the series cascading of the output voltage of the two inverters. The investigations are done with Induction motor load. Cascaded boost-converter is proposed in the present work to produce the required DC Voltage at the input of the bridge inverter. A simple FLC is applied to CBFLIIM system. The FLC is proposed to reduce the steady state error. The simulation results are compared with the hardware results. The results of the comparison are made to show the improvement in dynamic response in terms of settling time and steady state error. Design procedure and control strategy are presented in detail.
Closed Loop Fuzzy Logic Controlled PV Based Cascaded Boost Five-Level Inverter System
Revana, Guruswamy; Kota, Venkata Reddy
2017-12-01
Recent developments in intelligent control methods and power electronics have produced PV based DC to AC converters related to AC drives. Cascaded boost converter and inverter find their way in interconnecting PV and Induction Motor. This paper deals with digital simulation and implementation of closed loop controlled five-level inverter based Photo-Voltaic (PV) system. The objective of this work is to reduce the harmonics using Multi Level Inverter based system. The DC output from the PV panel is boosted using cascaded-boost-converters. The DC output of these cascaded boost converters is applied to the bridges of the cascaded inverter. The AC output voltage is obtained by the series cascading of the output voltage of the two inverters. The investigations are done with Induction motor load. Cascaded boost-converter is proposed in the present work to produce the required DC Voltage at the input of the bridge inverter. A simple FLC is applied to CBFLIIM system. The FLC is proposed to reduce the steady state error. The simulation results are compared with the hardware results. The results of the comparison are made to show the improvement in dynamic response in terms of settling time and steady state error. Design procedure and control strategy are presented in detail.
Use of UPFC device controlled by fuzzy logic controllers for decoupled power flow control
Directory of Open Access Journals (Sweden)
Ivković Sanja
2014-01-01
Full Text Available This paper investigates the possibility of decoupled active and reactive power flow control in a power system using a UPFC device controlled by fuzzy logic controllers. A Brief theoretical review of the operation principles and applications of UPFC devices and design principles of the fuzzy logic controller used are given. A Matlab/Simulink model of the system with UPFC, the fuzzy controller setup, and graphs of the results are presented. Conclusions are drawn regarding the possibility of using this system for decoupled control of the power flow in power systems based on analysis of these graphs.
A reinforcement learning-based architecture for fuzzy logic control
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.
Fuzzy logic controller for crude oil levels at Escravos Tank Farm ...
African Journals Online (AJOL)
Fuzzy logic controller (FLC) for crude oil flow rates and tank levels was designed for monitoring flow and tank level management at Escravos Tank Farm in Nigeria. The fuzzy control system incorporated essence of expert knowledge required to handle the tasks. Proportional Integral Derivative (PID) control of crude flow ...
Structural modeling and fuzzy-logic based diagnosis of a ship propulsion benchmark
DEFF Research Database (Denmark)
Izadi-Zamanabadi, Roozbeh; Blanke, M.; Katebi, S.D.
2000-01-01
An analysis of structural model of a ship propulsion benchmark leads to identifying the subsystems with inherent redundant information. For a nonlinear part of the system, a Fuzzy logic based FD algorithm with adaptive threshold is employed. The results illustrate the applicability of structural...... analysis as well as fuzzy observer....
Structural modeling and fuzzy-logic based diagnosis of a ship propulsion benchmark
DEFF Research Database (Denmark)
Izadi-Zamanabadi, Roozbeh; Blanke, M.; Katebi, S.D.
2000-01-01
An analysis of structural model of a ship propulsion benchmark leads to identifying the subsystems with inherent redundant information. For a nonlinear part of the system, a Fuzzy logic based FD algorithm with adaptive threshold is employed. The results illustrate the applicability of structural...... analysis as well as fuzzy observer...
A Fuzzy-Logic Subsumption Controller for Home Energy Management Systems
Energy Technology Data Exchange (ETDEWEB)
Ainstworth, Nathan; Johnson, Brian; Lundstrom, Blake
2015-10-05
Presentation for NAPS 2015 associated with conference publication CP-64392. Home Energy Management Systems (HEMS) are controllers that manage and coordinate the generation, storage, and loads in a home. These controllers are increasingly necessary to ensure that increasing penetrations of distributed energy resources are used effectively and do not disrupt the operation of the grid. In this paper, we propose a novel approach to HEMS design based on behavioral control methods, which do not require accurate models or predictions and are very responsive to changing conditions.
Directory of Open Access Journals (Sweden)
Karem R. Domínguez Hernández
2013-01-01
Full Text Available Cervical cancer is the second largest cause of death among women worldwide. Nowadays, this disease is preventable and curable at low cost and low risk when an accurate diagnosis is done in due time, since it is the neoplasm with the highest prevention potential. This work describes the development of an expert system able to provide a diagnosis to cervical neoplasia (CN precursor injuries through the integration of fuzzy logics and image interpretation techniques. The key contribution of this research focuses on atypical cases, specifically on atypical glandular cells (AGC. The expert system consists of 3 phases: (1 risk diagnosis which consists of the interpretation of a patient’s clinical background and the risks for contracting CN according to specialists; (2 cytology images detection which consists of image interpretation (IM and the Bethesda system for cytology interpretation, and (3 determination of cancer precursor injuries which consists of in retrieving the information from the prior phases and integrating the expert system by means of a fuzzy logics (FL model. During the validation stage of the system, 21 already diagnosed cases were tested with a positive correlation in which 100% effectiveness was obtained. The main contribution of this work relies on the reduction of false positives and false negatives by providing a more accurate diagnosis for CN.
Fuzzy expert systems using CLIPS
Le, Thach C.
1994-01-01
This paper describes a CLIPS-based fuzzy expert system development environment called FCLIPS and illustrates its application to the simulated cart-pole balancing problem. FCLIPS is a straightforward extension of CLIPS without any alteration to the CLIPS internal structures. It makes use of the object-oriented and module features in CLIPS version 6.0 for the implementation of fuzzy logic concepts. Systems of varying degrees of mixed Boolean and fuzzy rules can be implemented in CLIPS. Design and implementation issues of FCLIPS will also be discussed.
Twenty-Five Years of the Fuzzy Factor: Fuzzy Logic, the Courts, and Student Press Law.
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…
Fuzzy Logic Based The Application of Multi-Microcontroller in Mobile Robot Model
Directory of Open Access Journals (Sweden)
Nuryono Satya Widodo
2009-12-01
Full Text Available This paper proposed a fuzzy logic based mobile robot as implemented in a multimicrocontroller system. Fuzzy logic controller was developed based on a behavior based approach. The Controller inputs were obtained from seven sonar sensor and three tactile switches. Behavior based approach was implemented in different level priority of behaviors. The behaviors were: obstacle avoidance, wall following and escaping as the emergency behavior. The results show that robot was able to navigate autonomously and avoid the entire obstacle.
Directory of Open Access Journals (Sweden)
Y. N. Petrenko
2011-01-01
Full Text Available The purpose of a crane control system is to provide load transfer with minimum swinging. The paper presents a developed three-dimensional simulation model of a bridge crane with fuzzy logic controller designed with application of genetic algorithms. Comparative indices of oscillation while load transferring are given in the paper. The indices have been obtained at various parameters of the fuzzy logic controller.
Enric Trillas a passion for fuzzy sets : a collection of recent works on fuzzy logic
Verdegay, Jose; Esteva, Francesc
2015-01-01
This book presents a comprehensive collection of the latest and most significant research advances and applications in the field of fuzzy logic. It covers fuzzy structures, rules, operations and mathematical formalisms, as well as important applications of fuzzy logic in a number of fields, like decision-making, environmental prediction and prevention, communication, controls and many others. Dedicated to Enric Trillas in recognition of his pioneering research in the field, the book also includes a foreword by Lotfi A. Zadeh and an outlook on the future of fuzzy logic.
A fuzzy classifier system for process control
Karr, C. L.; Phillips, J. C.
1994-01-01
A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.
A new approach of active compliance control via fuzzy logic control for multifingered robot hand
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.
Advances In Infection Surveillance and Clinical Decision Support With Fuzzy Sets and Fuzzy Logic.
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.
Systematic methods for the design of a class of fuzzy logic controllers
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
Fifty years of fuzzy logic and its applications
Rishe, Naphtali; Kandel, Abraham
2015-01-01
This book presents a comprehensive report on the evolution of Fuzzy Logic since its formulation in Lotfi Zadeh’s seminal paper on “fuzzy sets,” published in 1965. In addition, it features a stimulating sampling from the broad field of research and development inspired by Zadeh’s paper. The chapters, written by pioneers and prominent scholars in the field, show how fuzzy sets have been successfully applied to artificial intelligence, control theory, inference, and reasoning. The book also reports on theoretical issues; features recent applications of Fuzzy Logic in the fields of neural networks, clustering, data mining, and software testing; and highlights an important paradigm shift caused by Fuzzy Logic in the area of uncertainty management. Conceived by the editors as an academic celebration of the fifty years’ anniversary of the 1965 paper, this work is a must-have for students and researchers willing to get an inspiring picture of the potentialities, limitations, achievements and accomplishments...
INDONESIA PUBLIC BANKS PERFORMANCE EVALUATION USING FUZZY LOGIC
Directory of Open Access Journals (Sweden)
Sugiarto Sugiarto
2016-10-01
Full Text Available Return on Asset (ROA is a variable that has the greatest ability in predicting public banks stock prices in Indonesia. The coefficient of determination of ROA on public banks stock prices in Indonesia reached 54.8%. ROA has a significant positive influence on public bank stock prices in Indonesia. Fuzzy logic process on the performance of the 15 public banks in Indonesia have been carried out using the data of ROA for the period 2010 up to 2013. Bank reference performance according to ROA is based on Bank Indonesia Letter No. 6 / 23DPNP / 2011. The performance of each bank was analyzed by conventional methods and as a comparison used fuzzy logic. The evaluation with fuzzy logic method able to provide added value to the currently enforced performance evaluation method. There is significant difference in conclusion between the determination of fuzzy logic models and conventional method
Fuzzy logic color detection: Blue areas in melanoma dermoscopy images.
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. Copyright © 2014 Elsevier Ltd. All rights reserved.
Development of Fuzzy Logic Controller for Quanser Bench-Top Helicopter
Jafri, M. H.; Mansor, H.; Gunawan, T. S.
2017-11-01
Bench-top helicopter is a laboratory scale helicopter that usually used as a testing bench of the real helicopter behavior. This helicopter is a 3 Degree of Freedom (DOF) helicopter which works by three different axes wshich are elevation, pitch and travel. Thus, fuzzy logic controller has been proposed to be implemented into Quanser bench-top helicopter because of its ability to work with non-linear system. The objective for this project is to design and apply fuzzy logic controller for Quanser bench-top helicopter. Other than that, fuzzy logic controller performance system has been simulated to analyze and verify its behavior over existing PID controller by using Matlab & Simulink software. In this research, fuzzy logic controller has been designed to control the elevation angle. After simulation has been performed, it can be seen that simulation result shows that fuzzy logic elevation control is working for 4°, 5° and 6°. These three angles produce zero steady state error and has a fast response. Other than that, performance comparisons have been performed between fuzzy logic controller and PID controller. Fuzzy logic elevation control has a better performance compared to PID controller where lower percentage overshoot and faster settling time have been achieved in 4°, 5° and 6° step response test. Both controller are have zero steady state error but fuzzy logic controller is managed to produce a better performance in term of settling time and percentage overshoot which make the proposed controller is reliable compared to the existing PID controller.
Directory of Open Access Journals (Sweden)
Mohamad Agung Prawira Negara
2017-08-01
Full Text Available Telecommunications and robotics technology is being developed to assist and facilitate the work of a human. In the field of telecommunications particularly smartphone has reached the planting of operating systems like android until planting sensors such as an accelerometer, gyro, proximity, etc. We would like to take advantage of the accelerometer sensor on a smartphone as robot control. We will compare the use of Sugeno Fuzzy Logic and Mamdani Fuzzy Logic to determine the best control method. The basic components of the robot are the Bluetooth module HC-05 as a medium of communication with the android, arduino as the control system and actuators such as DC motors drive the rear wheels to adjust the speed of the robot, and servo motor drives the front wheels to adjust the degree of turn robot. In robot’s movement test, 4 of 8 trials or approximately 50% stated better Sugeno Fuzzy Logic than Mamdani Fuzzy Logic in terms of linearity. In robot's controller response test, for Sugeno Fuzzy Logic method the average delay is 0.41 seconds, and for Mamdani Fuzzy Logic method the average delay is 10.80 seconds.
Fuzzy Logic and Its Application in Football Team Ranking
Directory of Open Access Journals (Sweden)
Wenyi Zeng
2014-01-01
some certain rules, we propose four parameters to calculate fuzzy similar matrix, obtain fuzzy equivalence matrix and the ranking result for our numerical example, T7, T3, T1, T9, T10, T8, T11, T12, T2, T6, T5, T4, and investigate four parameters sensitivity analysis. The study shows that our fuzzy logic method is reliable and stable when the parameters change in certain range.
Fuzzy logic controller for weaning neonates from mechanical ventilation.
Hatzakis, G. E.; Davis, G. M.
2002-01-01
Weaning from mechanical ventilation is the gradual detachment from any ventilatory support till normal spontaneous breathing can be fully resumed. To date, we have developed a fuzzy logic controller for weaning COPD adults using pressure support ventilation (PS). However, adults and newborns differ in the pathophysiology of lung disease. We therefore used our fuzzy logic-based weaning platform to develop modularized components for weaning newborns with lung disease. Our controller uses the he...
Fuzzy logic based ELF magnetic field estimation in substations
International Nuclear Information System (INIS)
Kosalay, I.
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. (authors)
Fuzzy Logic and Intelligent Technologies in Nuclear Science
International Nuclear Information System (INIS)
Da Ruan
1998-01-01
FLINS is the acronym for Fuzzy Logic and Intelligent Technologies in Nuclear Science. The main task for FLINS is to solve intricate problems pertaining to the nuclear environment by using modern technologies as additional tools and to bridge the gap between novel technologies and the industrial nuclear world. In 1997, major efforts went to the specific prototyping of Fuzzy Logic Control of SCK-CEN's BR1 research Reactor. Progress and achievements are reported
Fuzzy logic based ELF magnetic field estimation in substations.
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.
Risk analysis with a fuzzy-logic approach of a complex installation
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.
Implementation of Fuzzy Logic Based Temperature-Controlled Heat ...
African Journals Online (AJOL)
This research then compares the control performance of PID (Proportional Integral and Derivative) and Fuzzy logic controllers. Conclusions are made based on these control performances. The results show that the control performance for a Fuzzy controller is quite similar to PID controller but comparatively gives a better ...
USE OF FUZZY LOGIC TO INVESTIGATE WEATHER PARAMETER ...
African Journals Online (AJOL)
Load forecasting guides the power company to make some decisions on generation, transmission and distribution of electrical power. This work presents a solution methodology, using fuzzy logic approach for short term load forecasting (STLF) for Adamawa State University, Mubi. The proposed methodology utilized fuzzy ...
PENGGUNAAN FUZZY LOGIC UNTUK KONTROL PARALLEL CONVERTER DC-DC
Directory of Open Access Journals (Sweden)
Bambang Prio Hartono
2012-09-01
Full Text Available Abstract: Using system fuzzy logic as control technology have been used on low load dc-dc converter with combined parallel compiled dc-dc converter can obtain big load. With existence of differrence of component parameter and each parallel compiled converter can obtained different current and voltage output. Function of controller for to do adjustment, so that current which is applied to load by each converter can be obtained difference error as small as possible or same. The object of research is developing design of large signal dc-dc converter which is combined with using FLC so that obtain better performance. To get better performance have been made plant model and simulation with CDE method. The more systematic system and design is needed to overcome bigger load on dc-dc converter, so that parallel compiled current master slave control system on dc-dc converter with using fuzzy logic controller is used. Result of research showed that error or difference of current which is applied to load can handled by fuzzy logic controller. Technic of current and voltage controller co to do adjustment current and voltage distribution equally to load. Distribution of iL1,iL2 and output voltage Vo on dc-dc converter with load 2,25 until 7,875 and voltage 100 until 120 volt, load current beetwen 12 until 48, % relatif error Vo 0,4% until 0,9%.
Development of erosion risk map using fuzzy logic approach
Directory of Open Access Journals (Sweden)
Fauzi Manyuk
2017-01-01
Full Text Available Erosion-hazard assessment is an important aspect in the management of a river basin such as Siak River Basin, Riau Province, Indonesia. This study presents an application of fuzzy logic approach to develop erosion risk map based on geographic information system. Fuzzy logic is a computing approach based on “degrees of truth” rather than the usual “true or false” (1 or 0 Boolean logic on which the modern computer is based. The results of the erosion risk map were verified by using field measurements. The verification result shows that the parameter of soil-erodibility (K indicates a good agreement with field measurement data. The classification of soil-erodibility (K as the result of validation were: very low (0.0–0.1, medium (0.21-0.32, high (0.44-0.55 and very high (0.56-0.64. The results obtained from this study show that the erosion risk map of Siak River Basin were dominantly classified as medium level which cover about 68.54%. The other classifications were high and very low erosion level which cover about 28.84% and 2.61% respectively.
Qualitative assessment of environmental impacts through fuzzy logic
International Nuclear Information System (INIS)
Peche G, Roberto
2008-01-01
The vagueness of many concepts usually utilized in environmental impact studies, along with frequent lack of quantitative information, suggests that fuzzy logic can be applied to carry out qualitative assessment of such impacts. This paper proposes a method for valuing environmental impacts caused by projects, based on fuzzy sets theory and methods of approximate reasoning. First, impacts must be described by a set of features. A linguistic variable is assigned to each feature, whose values are fuzzy sets. A fuzzy evaluation of environmental impacts is achieved using rule based fuzzy inference and the estimated fuzzy value of each feature. Generalized modus ponens has been the inference method. Finally, a crisp value of impact is attained by aggregation and defuzzification of all fuzzy results
Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 1
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.
Proceedings of the Third International Workshop on Neural Networks and Fuzzy Logic, volume 2
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.
Astronomical pipeline processing using fuzzy logic
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.
Evolving fuzzy rules in a learning classifier system
Valenzuela-Rendon, Manuel
1993-01-01
The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.
Fuzzy logic anti-skid control for commercial trucks
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.
Expanding Basic Fuzzy Logic with Truth Constants for Component Delimiters
Czech Academy of Sciences Publication Activity Database
Haniková, Zuzana
2012-01-01
Roč. 197, 16 June (2012), s. 95-107 ISSN 0165-0114 R&D Projects: GA ČR GEICC/08/E018 Institutional research plan: CEZ:AV0Z10300504 Keywords : mathematics * non-classical logics * algebra * basic fuzzy logic BL * propositional constants Subject RIV: BA - General Mathematics Impact factor: 1.749, year: 2012
A Note on the Notion of Truth in Fuzzy Logic
Czech Academy of Sciences Publication Activity Database
Hájek, Petr; Shepherdson, J.
2001-01-01
Roč. 109, 1-2 (2001), s. 65-69 ISSN 0168-0072 Institutional research plan: AV0Z1030915 Keywords : many-valued logic * fuzzy logic Subject RIV: BA - General Mathematics Impact factor: 0.519, year: 2001
Fuzzy logic an introductory course for engineering students
Trillas, Enric
2015-01-01
This book introduces readers to fundamental concepts in fuzzy logic. It describes the necessary theoretical background and a number of basic mathematical models. Moreover, it makes them familiar with fuzzy control, an important topic in the engineering field. The book offers an unconventional introductory textbook on fuzzy logic, presenting theory together with examples and not always following the typical mathematical style of theorem-corollaries. Primarily intended to support engineers during their university studies, and to spark their curiosity about fuzzy logic and its applications, the book is also suitable for self-study, providing a valuable resource for engineers and professionals who deal with imprecision and non-random uncertainty in real-world applications.
Gomaa Haroun, A H; Li, Yin-Ya
2017-11-01
In the fast developing world nowadays, load frequency control (LFC) is considered to be a most significant role for providing the power supply with good quality in the power system. To deliver a reliable power, LFC system requires highly competent and intelligent control technique. Hence, in this article, a novel hybrid fuzzy logic intelligent proportional-integral-derivative (FLiPID) controller has been proposed for LFC of interconnected multi-area power systems. A four-area interconnected thermal power system incorporated with physical constraints and boiler dynamics is considered and the adjustable parameters of the FLiPID controller are optimized using particle swarm optimization (PSO) scheme employing an integral square error (ISE) criterion. The proposed method has been established to enhance the power system performances as well as to reduce the oscillations of uncertainties due to variations in the system parameters and load perturbations. The supremacy of the suggested method is demonstrated by comparing the simulation results with some recently reported heuristic methods such as fuzzy logic proportional-integral (FLPI) and intelligent proportional-integral-derivative (PID) controllers for the same electrical power system. the investigations showed that the FLiPID controller provides a better dynamic performance and outperform compared to the other approaches in terms of the settling time, and minimum undershoots of the frequency as well as tie-line power flow deviations following a perturbation, in addition to perform appropriate settlement of integral absolute error (IAE). Finally, the sensitivity analysis of the plant is inspected by varying the system parameters and operating load conditions from their nominal values. It is observed that the suggested controller based optimization algorithm is robust and perform satisfactorily with the variations in operating load condition, system parameters and load pattern. Copyright © 2017 ISA. Published by
Fuzzy-logic-based active vibration control of beams using piezoelectric patches
Sharma, Manu; Singh, S. P.; Sachdeva, B. L.
2003-10-01
The present work presents a fuzzy logic based controller with a compact rule base, for active vibration control of beams. The controller was implemented experimentally on a test beam and the results were found satisfactory. The test system consists of a cantilevered beam with two piezoelectric patches mounted near its root in collocated fashion. This piezo-beam system was modelled using Finite Element Method. To derive the equations of motion, Hamilton's principle was used. Electro-mechanical interaction of the piezoelectric patch with the beam was modelled using linear constitutive equations for piezoceramics, which relate strain and electric displacement to stress and electric field. The fuzzy logic controller is based on modal velocity of the beam. The basis for generating the fuzzy logic rule base of this controller is obtained from negative velocity feedback control. Modal velocity of the beam acts as an input to the fuzzy controller and actuation force is the output from the inference engine. Linear decay of vibratory amplitude is observed in case of fuzzy logic controller as opposed to logarithmic decay in case of negative velocity feedback control Present controller has just three rules. This is an important achievement because bulky fuzzy logic controllers for active vibration control require fast processors for real time implementation (Kwak and Sciulli and Mayhan and Washington).
A fuzzy logic controller for feedwater regulation in pressurized water reactors
International Nuclear Information System (INIS)
Eryuerek, E.E.; Upadhyaya, B.R.; Alguindigue, I.E.
1994-01-01
Fuzzy control refers to the application of fuzzy logic theory to control systems. In this paper fuzzy controllers for steam generator water level control and pump speed control are presented, and their performance in the presence of perturbations is discussed. In order to test the robustness of the controllers, their performance is compared with the performance of model based adaptive controllers and traditional PID controllers. The control actions calculated by the fuzzy controllers is have the characteristic of quick and smooth control compared to the others
Muniraj, Murali; Arulmozhiyal, Ramaswamy
2015-01-01
A control actuation system has been used extensively in automotive, aerospace, and defense applications. The major challenges in modeling control actuation system are rise time, maximum peak to peak overshoot, and response to nonlinear system with percentage error. This paper addresses the challenges in modeling and real time implementation of control actuation system for missiles glider applications. As an alternative fuzzy-PID controller is proposed in BLDC motor drive followed by linkage mechanism to actuate fins in missiles and gliders. The proposed system will realize better rise time and less overshoot while operating in extreme nonlinear dynamic system conditions. A mathematical model of BLDC motor is derived in state space form. The complete control actuation system is modeled in MATLAB/Simulink environment and verified by performing simulation studies. A real time prototype of the control actuation is developed with dSPACE-1104 hardware controller and a detailed analysis is carried out to confirm the viability of the proposed system.
Controlling Smart Green House Using Fuzzy Logic Method
Directory of Open Access Journals (Sweden)
Rafiuddin Syam
2015-10-01
Full Text Available To increase agricultural output it is needed a system that can help the environmental conditions for optimum plant growth. Smart greenhouse allows for plants to grow optimally, because the temperature and humidity can be controlled so that no drastic changes. It is necessary for optimal smart greenhouse needed a system to manipulate the environment in accordance with the needs of the plant. In this case the setting temperature and humidity in the greenhouse according to the needs of the plant. So using an automated system for keeping such environmental condition is important. In this study, the authors use fuzzy logic to make the duration of watering the plants more dynamic in accordance with the input temperature and humidity so that the temperature and humidity in the green house plants maintained in accordance to the reference condition. Based on the experimental results using fuzzy logic method is effective to control the duration of watering and to maintain the optimum temperature and humidity inside the greenhouse
Fuzzy control system for a mobile robot
International Nuclear Information System (INIS)
Hai Quan Dai; Dalton, G.R.; Tulenko, J.
1992-01-01
Since the first fuzzy logic control system was proposed by Mamdani, many studies have been carried out on industrial process and real-time controls. The key problem for the application of fuzzy logic control is to find a suitable set of fuzzy control rules. Three common modes of deriving fuzzy control rules are often distinguished and mentioned: (1) expert experience and knowledge; (2) modeling operator control actions; and (3) modeling a process. In cases where an operator's skill is important, it is very useful to derive fuzzy control rules by modeling an operator's control actions. It is possible to model an operator's control behaviors in terms of fuzzy implications using the input-output data concerned with his/her control actions. The authors use the model obtained in this way as the basis for a fuzzy controller. The authors use a finite number of fuzzy or approximate control rules. To control a robot in a cluttered reactor environment, it is desirable to combine all the methods. In this paper, the authors describe a general algorithm for a mobile robot control system with fuzzy logic reasoning. They discuss the way that knowledge of fuzziness will be represented in this control system. They also describe a simulation program interface to the K2A Cybermation mobile robot to be used to demonstrate the control system
Improvements to Earthquake Location with a Fuzzy Logic Approach
Gökalp, Hüseyin
2018-01-01
In this study, improvements to the earthquake location method were investigated using a fuzzy logic approach proposed by Lin and Sanford (Bull Seismol Soc Am 91:82-93, 2001). The method has certain advantages compared to the inverse methods in terms of eliminating the uncertainties of arrival times and reading errors. In this study, adopting this approach, epicentral locations were determined based on the results of a fuzzy logic space concerning the uncertainties in the velocity models. To map the uncertainties in arrival times into the fuzzy logic space, a trapezoidal membership function was constructed by directly using the travel time difference between the two stations for the P- and S-arrival times instead of the P- and S-wave models to eliminate the need for obtaining information concerning the velocity structure of the study area. The results showed that this method worked most effectively when earthquakes occurred away from a network or when the arrival time data contained phase reading errors. In this study, to resolve the problems related to determining the epicentral locations of the events, a forward modeling method like the grid search technique was used by applying different logical operations (i.e., intersection, union, and their combination) with a fuzzy logic approach. The locations of the events were depended on results of fuzzy logic outputs in fuzzy logic space by searching in a gridded region. The process of location determination with the defuzzification of only the grid points with the membership value of 1 obtained by normalizing all the maximum fuzzy output values of the highest values resulted in more reliable epicentral locations for the earthquakes than the other approaches. In addition, throughout the process, the center-of-gravity method was used as a defuzzification operation.
Fuzzy-logic-assisted surgical planning in adolescent idiopathic scoliosis.
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.
Directory of Open Access Journals (Sweden)
S.Z.Mohammad Noor
2016-06-01
Full Text Available This paper presents an implementation of Single-phase Single stage String inverter for Grid connected Photovoltaic (PV system. The proposed system uses Modified Perturb and Observe (P&O algorithm implemented using Fuzzy Logic Control (FLC as Maximum Power Point Tracking (MPPT. The inverter is designed for 340W system using two series of STP170s24/Ac PV modules. The MPPT unit keeps tracking the maximum power from the PV array by changing the modulation index and the phase angle of inverter’s output voltage. The simulation model is developed using Matlab/Simulink to evaluate the performance of the converter. Selected experimental results are also presented in this paper.
DEFF Research Database (Denmark)
Boiocchi, Riccardo; Gernaey, Krist; Sin, Gürkan
2016-01-01
A methodology is developed to systematically design the membership functions of fuzzy-logic controllers for multivariable systems. The methodology consists of a systematic derivation of the critical points of the membership functions as a function of predefined control objectives. Several...... constrained optimization problems corresponding to different qualitative operation states of the system are defined and solved to identify, in a consistent manner, the critical points of the membership functions for the input variables. The consistently identified critical points, together with the linguistic...... knowledge performed worse. This proved the importance of using a systematic methodology for the derivation of the membership functions for multivariable systems. These results are promising for future applications of the controller in real full-scale plants. Furthermore, the methodology can be used...
Fuzzy logic type 1 and type 2 based on LabVIEW FPGA
Ponce-Cruz, Pedro; MacCleery, Brian
2016-01-01
This book is a comprehensive introduction to LabVIEW FPGA™, a package allowing the programming of intelligent digital controllers in field programmable gate arrays (FPGAs) using graphical code. It shows how both potential difficulties with understanding and programming in VHDL and the consequent difficulty and slowness of implementation can be sidestepped. The text includes a clear theoretical explanation of fuzzy logic (type 1 and type 2) with case studies that implement the theory and systematically demonstrate the implementation process. It goes on to describe basic and advanced levels of programming LabVIEW FPGA and show how implementation of fuzzy-logic control in FPGAs improves system responses. A complete toolkit for implementing fuzzy controllers in LabVIEW FPGA has been developed with the book so that readers can generate new fuzzy controllers and deploy them immediately. Problems and their solutions allow readers to practice the techniques and to absorb the theoretical ideas as they arise. Fuzzy L...
Directory of Open Access Journals (Sweden)
Abbas Ali Zamani
2012-07-01
Full Text Available Physical systems always include constraints and limits. Usually, the limits and constraints, in the control systems, are appeared as temperature and pressure limits or pumps capacity. One of the existing limits in the systems with PID controller is associated with the actuator’s saturation limits. With the saturating of the actuator, the controller’s output and plant’s input will be different and the output signal of controller do not lead the system and their states could not update correctly where this issue makes the system response undesirable. In this paper, by adding a fuzzy compensator that it’s parameters are tuned using imperialist competitive algorithm, the actuator saturation is prevented and the important parameters of the system response, such as setting time and overshoot, are improved.
Simulating Shopper Behavior using Fuzzy Logic in Shopping Center Simulation
Directory of Open Access Journals (Sweden)
Jason Christian
2016-12-01
Full Text Available To simulate real-world phenomena, a computer tool can be used to run a simulation and provide a detailed report. By using a computer-aided simulation tool, we can retrieve information relevant to the simulated subject in a relatively short time. This study is an extended and complete version of an initial research done by Christian and Hansun and presents a prototype of a multi-agent shopping center simulation tool along with a fuzzy logic algorithm implemented in the system. Shopping centers and all their components are represented in a simulated 3D environment. The simulation tool was created using the Unity3D engine to build the 3D environment and to run the simulation. To model and simulate the behavior of agents inside the simulation, a fuzzy logic algorithm that uses the agents’ basic knowledge as input was built to determine the agents’ behavior inside the system and to simulate human behaviors as realistically as possible.
Single board system for fuzzy inference
Symon, James R.; Watanabe, Hiroyuki
1991-01-01
The very large scale integration (VLSI) implementation of a fuzzy logic inference mechanism allows the use of rule-based control and decision making in demanding real-time applications. Researchers designed a full custom VLSI inference engine. The chip was fabricated using CMOS technology. The chip consists of 688,000 transistors of which 476,000 are used for RAM memory. The fuzzy logic inference engine board system incorporates the custom designed integrated circuit into a standard VMEbus environment. The Fuzzy Logic system uses Transistor-Transistor Logic (TTL) parts to provide the interface between the Fuzzy chip and a standard, double height VMEbus backplane, allowing the chip to perform application process control through the VMEbus host. High level C language functions hide details of the hardware system interface from the applications level programmer. The first version of the board was installed on a robot at Oak Ridge National Laboratory in January of 1990.
Fuzzy logic based classification and assessment of pathological voice signals.
Aghazadeh, Babak Seyed; Heris, Hossein Khadivi
2009-01-01
In this paper an efficient fuzzy wavelet packet (WP) based feature extraction method and fuzzy logic based disorder assessment technique were used to investigate voice signals of patients suffering from unilateral vocal fold paralysis (UVFP). Mother wavelet function of tenth order Daubechies (d10) was employed to decompose signals in 5 levels. Next, WP coefficients were used to measure energy and Shannon entropy features at different spectral sub-bands. Consequently, using fuzzy c-means method, signals were clustered into 2 classes. The amount of fuzzy membership of pathological and normal signals in their corresponding clusters was considered as a measure to quantify the discrimination ability of features. A classification accuracy of 100 percent was achieved using an artificial neural network classifier. Finally, fuzzy c-means clustering method was used as a way of voice pathology assessment. Accordingly, fuzzy membership function based health index is proposed.
Adaptive Process Control with Fuzzy Logic and Genetic Algorithms
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.
Adaptive process control using fuzzy logic and genetic algorithms
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.
Toward a fuzzy logic control of the infant incubator.
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.
Bouharati, S.; Benmahammed, K.; Harzallah, D.; El-Assaf, Y. M.
The classical methods for detecting the micro biological pollution in water are based on the detection of the coliform bacteria which indicators of contamination. But to check each water supply for these contaminants would be a time-consuming job and a qualify operators. In this study, we propose a novel intelligent system which provides a detection of microbiological pollution in fresh water. The proposed system is a hierarchical integration of an Artificial Neuro-Fuzzy Inference System (ANFIS). This method is based on the variations of the physical and chemical parameters occurred during bacteria growth. The instantaneous result obtained by the measurements of the variations of the physical and chemical parameters occurred during bacteria growth-temperature, pH, electrical potential and electrical conductivity of many varieties of water (surface water, well water, drinking water and used water) on the number Escherichia coli in water. The instantaneous result obtained by measurements of the inputs parameters of water from sensors.
Fuzzy logic-based prognostic score for outcome prediction in esophageal cancer.
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.
Distributed traffic signal control using fuzzy logic
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.
Novin, Vahid; Givehchi, Saeed; Hoveidi, Hassan
2016-09-01
Reliable methods are crucial to cope with uncertainties in the risk analysis process. The aim of this study is to develop an integrated approach to assessing risks of benzene in the petrochemical plant that produces benzene. We offer an integrated system to contribute imprecise variables into the health risk calculation. The project was conducted in Asaluyeh, southern Iran during the years from 2013 to 2014. Integrated method includes fuzzy logic and artificial neural networks. Each technique had specific computational properties. Fuzzy logic was used for estimation of absorption rate. Artificial neural networks can decrease the noise of the data so applied for prediction of benzene concentration. First, the actual exposure was calculated then it combined with Integrated Risk Information System (IRIS) toxicity factors to assess real health risks. High correlation between the measured and predicted benzene concentration was achieved (R 2 = 0.941). As for variable distribution, the best estimation of risk in a population implied 33% of workers exposed less than 1×10 -5 and 67% inserted between 1.0×10 -5 to 9.8×10 -5 risk levels. The average estimated risk of exposure to benzene for entire work zones is equal to 2.4×10 -5 , ranging from 1.5×10 -6 to 6.9×10 -5 . The integrated model is highly flexible as well as the rules possibly will be changed according to the necessities of the user in a different circumstance. The measured exposures can be duplicated well through proposed model and realistic risk assessment data will be produced.
Bassaneze, Vinicius; Sacramento, Chester Bittencourt; Freire, Rodolfo; Alencar, Patrícia Fernandes De; Ortega, Neli Regina Siqueira; Krieger, Jose Eduardo
2013-01-01
The a priori identification of induced pluripotent stem cells remains a challenge. Being able to quickly identify the most embryonic stem cell-similar induced pluripotent stem cells when validating results could help to reduce costs and save time. In this context, tools based on non-classic logic can be useful in creating aid-systems based on visual criteria. True colonies when viewed at 100x magnification have been found to have the following 3 characteristics: a high degree of border delineation, a more uniform texture, and the absence of a cracked texture. These visual criteria were used for fuzzy logic modeling. We investigated the possibility of predicting the presence of alkaline phosphatase activity, typical of true induced pluripotent stem cell colonies, after 25 individuals, with varying degrees of experience in working with murine iPS cells, categorized the images of 136 colonies based on visual criteria. Intriguingly, the performance evaluation by area under the ROC curve (16 individuals with satisfactory performance), Spearman correlation (all statistically significant), and Cohen's Kappa agreement analysis (all statistically significant) demonstrates that the discriminatory capacity of different evaluators are similar, even those who have never cultivated cells. Thus, we report on a new system to facilitate visual identification of murine- induced pluripotent stem cell colonies that can be useful for staff training and opens the possibility of exploring visual characteristics of induced pluripotent stem cell colonies with their functional peculiarities. The fuzzy model has been integrated as a web-based tool named “2see-iPS” which is freely accessed at http://genetica.incor.usp.br/2seeips/. PMID:23950970
DEFF Research Database (Denmark)
Aldana, Nelson Leonardo Diaz; Dragicevic, Tomislav; Vasquez, Juan Carlos
2014-01-01
-charge or deep-discharge in one of the energy storage units. Primary control in a microgrid is responsible for power sharing among units; and droop control is typically used in this stage. This paper proposes a modular and decentralized gain-scheduling control strategy based on fuzzy logic that ensures balanced...
IMPLEMENTATION OF FUZZY LOGIC BASED TEMPERATURE ...
African Journals Online (AJOL)
The “center of gravity” or the “centroid” method of defuzzification was chosen, since it weighs the effect of each input variable towards the calculation of the output [5]. Input fuzzy sets and rules are converted into an output fuzzy set, and then into a crisp output for controlling the steam control valve. All the rules that have any ...
2000-02-01
A Fuzzy Logic Ramp Metering Algorithm was implemented on 126 ramps in the greater Seattle area. This report documents the implementation of the Fuzzy Logic Ramp Metering Algorithm at the Northwest District of the Washington State Department of Transp...
On Witnessed Models in Fuzzy Logic III - Witnessed Gödel Logics
Czech Academy of Sciences Publication Activity Database
Hájek, Petr
2010-01-01
Roč. 56, č. 2 (2010), s. 171-174 ISSN 0942-5616 R&D Projects: GA MŠk(CZ) 1M0545 Institutional research plan: CEZ:AV0Z10300504 Keywords : mathematical fuzzy logic * Gödel logic * witnessed models * arithmetical complexity Subject RIV: BA - General Mathematics Impact factor: 0.361, year: 2010
Towards rational closure for fuzzy logic: The case of propositional Godel logic
CSIR Research Space (South Africa)
Casini, G
2013-12-01
Full Text Available In the field of non-monotonic logics, the notion of rational closure is acknowledged as a landmark and we are going to see whether such a construction can be adopted in the context of mathematical fuzzy logic, a so far (apparently) unexplored...
Application of fuzzy logic controller to load-follow operations in pressurized water reactors
International Nuclear Information System (INIS)
Lin, Chaung; Lin, Hua-Wei
1994-01-01
The fuzzy logic controller was developed to control load-follow operations in pressurized water reactors. The reactor core characteristics change according to different fuel cycles or core exposures, thus making a nonlinear time-varying control problem. This proposed method, however, does not require a mathematical model to design the controller, and so avoids redesigning or tuning controller gain for various cores. Clearly, this method is very suitable for reactor load-following operation control. The control system has two subsystems: one is to track the desired power, and the other is to keep axial offset close to the target value. Both controllers use fuzzy logic: one is the conventional type, and the other uses fuzzy logic to tune the parameters of the controller so the controller can correspond to various core characteristics. Simulation results show that the control system performs well for different cores, and so this system is useful for load-follow operation. (author)
Directory of Open Access Journals (Sweden)
Murali Muniraj
2015-01-01
Full Text Available A control actuation system has been used extensively in automotive, aerospace, and defense applications. The major challenges in modeling control actuation system are rise time, maximum peak to peak overshoot, and response to nonlinear system with percentage error. This paper addresses the challenges in modeling and real time implementation of control actuation system for missiles glider applications. As an alternative fuzzy-PID controller is proposed in BLDC motor drive followed by linkage mechanism to actuate fins in missiles and gliders. The proposed system will realize better rise time and less overshoot while operating in extreme nonlinear dynamic system conditions. A mathematical model of BLDC motor is derived in state space form. The complete control actuation system is modeled in MATLAB/Simulink environment and verified by performing simulation studies. A real time prototype of the control actuation is developed with dSPACE-1104 hardware controller and a detailed analysis is carried out to confirm the viability of the proposed system.
Muniraj, Murali; Arulmozhiyal, Ramaswamy
2015-01-01
A control actuation system has been used extensively in automotive, aerospace, and defense applications. The major challenges in modeling control actuation system are rise time, maximum peak to peak overshoot, and response to nonlinear system with percentage error. This paper addresses the challenges in modeling and real time implementation of control actuation system for missiles glider applications. As an alternative fuzzy-PID controller is proposed in BLDC motor drive followed by linkage mechanism to actuate fins in missiles and gliders. The proposed system will realize better rise time and less overshoot while operating in extreme nonlinear dynamic system conditions. A mathematical model of BLDC motor is derived in state space form. The complete control actuation system is modeled in MATLAB/Simulink environment and verified by performing simulation studies. A real time prototype of the control actuation is developed with dSPACE-1104 hardware controller and a detailed analysis is carried out to confirm the viability of the proposed system. PMID:26613102
A Genetic Algorithm and Fuzzy Logic Approach for Video Shot Boundary Detection.
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.
Fuzzy-logic-based resource allocation for isolated and multiple platforms
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.
Directory of Open Access Journals (Sweden)
A. Stanley Raj
2015-01-01
Full Text Available Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzy C-means clustering, fuzzy K-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS training on synthetic data. Here in this approach, graphical user interface (GUI was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity, while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth. A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.
Coordinated signal control for arterial intersections using fuzzy logic
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.
Fuzzy logic control of vehicle suspensions with dry friction nonlinearity
Indian Academy of Sciences (India)
of methods could be used to perform defuzzification, two of the most common of which are: i) The Mamdani method that returns the centroid of the output fuzzy region as the crisp output of the fuzzy interface system. ii) The TVFI (truth value flow inference) method that returns a weighted average as the crisp output of the fuzzy ...
Directory of Open Access Journals (Sweden)
Dimas Firmanda Al Riza
2015-02-01
settling time selama 1 jam 20 menit dan rata-rata error sebesar -0,36 oC. Proses fermentasi selama 16 jam menggunakan fermentor dengan kontroler fuzzy menghasilkan yogurt dengan pH sebesar 3,66, jumlah mikroba Lactobacillus sp. sebanyak 4,85 x 108cfu/mL, dan Streptococcus sp. sebanyak 1,34 x 10 6 cfu/mL. Kata kunci: Fermentasi, yogurt, susu sapi, fuzzy, kontrol suhu
Fuzzy logic: A "simple" solution for complexities in neurosciences?
Godil, Saniya Siraj; Shamim, Muhammad Shahzad; Enam, Syed Ather; Qidwai, Uvais
2011-02-26
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. This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology. 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. In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.
evaluation of a multi-variable self-learning fuzzy logic controller
African Journals Online (AJOL)
Dr Obe
2003-03-01
Mar 1, 2003 ... the merger of fuzzy logic and other forms of soft computing (principally Neural. Networks and Genetic ... merger of soft computing technologies, but instead is based on a purely fuzzy logic platform, was .... A scheme capable of automatic elicitation of suitable rules for a multivariable fuzzy logic controller has ...
Fuzzy Logic as a Tool for Assessing Students' Knowledge and Skills
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…
Membership function modification of fuzzy logic controllers with histogram equalization.
Zhuang, H; Wu, X
2001-01-01
In most fuzzy logic controllers (FLCs), initial membership functions (MFs) are normally laid evenly all across the universes of discourse (UD) that represent fuzzy control inputs. However, for evenly distributed MFs, there exists a potential problem that may adversely affect the control performance; that is, if the actual inputs are not equally distributed, but instead concentrate within a certain interval that is only part of the entire input area, this will result in two negative effects. On one hand, the MFs staying in the dense-input area will not be sufficient to react precisely to the inputs, because these inputs are too close to each other compared to the MFs in this area. The same fuzzy control output could be triggered for several different inputs. On the other hand, some of the MFs assigned for the sparse-input area are "wasted". In this paper we argue that, if we arrange the placement of these MFs according to a statistical study of feedback errors in a closed-loop system, we can expect a better control performance. To this end, we introduce a new mechanism to modify the evenly distributed MFs with the help of a technique termed histogram equalization. The histogram of the errors is actually the spatial distribution of real-time errors of the control system. To illustrate the proposed MF modification approach, a computer simulation of a simple system that has a known mathematical model is first analyzed, leading to our understanding of how this histogram-based modification mechanism functions. We then apply this method to an experimental laser tracking system to demonstrate that in real-world applications, a better control performance can he obtained by using this proposed technique.
Complexity of Some Language Fragments of Fuzzy Logics
Czech Academy of Sciences Publication Activity Database
Haniková, Zuzana
2017-01-01
Roč. 21, č. 1 (2017), s. 69-77 ISSN 1432-7643 R&D Projects: GA ČR GAP202/11/1632 Institutional support: RVO:67985807 Keywords : fuzzy logic * propositional logic * language fragment * implicational fragment * commutative semigroup * equational theory * computational complexity Subject RIV: BA - General Mathematics OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 2.472, year: 2016
Assessment of Seismic Damage on The Exist Buildings Using Fuzzy Logic
Pınar, USTA; Nihat, MOROVA; EVCİ, Ahmet; ERGÜN, Serap
2018-01-01
Earthquake as a natural disaster could damage the lives of many people and buildings all over the world. These is micvulnerability of the buildings needs to be evaluated. Accurate evaluation of damage sustained by buildings during natural disaster events is critical to determine the buildings safety and their suitability for future occupancy. The earthquake is one of the disasters that structures face the most. There fore, there is a need to evaluate seismic damage and vulnerability of the buildings to protect them. These days fuzzy systems have been widely used in different fields of science because of its simpli city and efficiency. Fuzzy logic provides a suitable framework for reasoning, deduction, and decision making in fuzzy conditions. In this paper, studies on earthquake hazard evaluation of buildings by fuzzy logic modeling concepts in the literature have been investigated and evaluated, as a whole.
Abouchabana, Nabil; Haddadi, Mourad; Rabhi, Abdelhamid; El Hajjaji, Ahmed
2017-11-01
Photovoltaic generators (PVG) produce a variable power according to the solar radiation (G) and temperature (T). This variation affects the sizing of the components of DC / DC converters, powered by such PVG, and make it difficult. The effects may differ from one component to another. The main and critical one is presented by the inductor, the element that stores the energy during sampled periods. We propose in this work an auto-adaptation of these inductor values to maintain optimal performance of the power yield of these converters. Our idea is to replace the inductor by a coupled inductor where this adjustment is made by the addition of an adjustable electric field in the magnetic core. Low current intensities come from the PVG supply the second inductor of the coupled inductor through a circuit controlled by a fuzzy controller (FC). The whole system is modeled and simulated under MATLAB/SIMULINK for the control part of the system and under PSPICE for the power part of the system. The obtained results show good performances of the proposed converter over the standard one.
Rollover prevention for sport utility vehicle using fuzzy logic controller
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.
Construction of a fuzzy and all Boolean logic gates based on DNA
DEFF Research Database (Denmark)
M. Zadegan, Reza; Jepsen, Mette D E; Hildebrandt, Lasse
2015-01-01
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....
Design of a fuzzy logic based controller for neutron power regulation
International Nuclear Information System (INIS)
Velez D, D.
2000-01-01
This work presents a fuzzy logic controller design for neutron power control, from its source to its full power level, applied to a nuclear reactor model. First, we present the basic definitions on fuzzy sets as generalized definitions of the crisp (non fuzzy) set theory. Likewise, we define the basic operations on fuzzy sets (complement, union, and intersection), and the operations on fuzzy relations such as projection and cylindrical extension operations. Furthermore, some concepts of the fuzzy control theory, such as the main modules of the typical fuzzy controller structure and its internal variables, are defined. After the knowledge base is obtained by simulation of the reactor behavior, where the controlled system is modeled by a simple nonlinear reactor model, this model is used to infer a set of fuzzy rules for the reactor response to different insertions of reactivity. The reduction of the response time, using fuzzy rule based controllers on this reactor, is possible by adjusting the output membership functions, by selecting fuzzy rule sets, or by increasing the number of crisp inputs to the fuzzy controller. System characteristics, such as number of rules, response times, and safety parameter values, were considered in the evaluation of each controller merits. Different fuzzy controllers are designed to attain the desired power level, to maintain a constant level for long periods of time, and to keep the reactor away from a shutdown condition. The basic differences among the controllers are the number of crisp inputs and the novel implementation of a crisp power level-based selection of different sets of output membership functions. Simulation results highlight, mainly: (1) A decrease of the response variations at low power level, and (2) a decrease in the time required to attain the desired neutron power. Finally, we present a comparative study of different fuzzy control algorithms applied to a nuclear model. (Author)
A real time fuzzy logic power management strategy for a fuel cell vehicle
International Nuclear Information System (INIS)
Hemi, Hanane; Ghouili, Jamel; Cheriti, Ahmed
2014-01-01
Highlights: • We present a real time fuzzy logic power management strategy. • This strategy is applied to hybrid electric vehicle dynamic model. • Three configurations evaluated during a drive cycle. • The hydrogen consumption is analysed for the three configurations. - Abstract: This paper presents real time fuzzy logic controller (FLC) approach used to design a power management strategy for a hybrid electric vehicle and to protect the battery from overcharging during the repetitive braking energy accumulation. The fuel cell (FC) and battery (B)/supercapacitor (SC) are the primary and secondary power sources, respectively. This paper analyzes and evaluates the performance of the three configurations, FC/B, FC/SC and FC/B/SC during real time driving conditions and unknown driving cycle. The MATLAB/Simulink and SimPowerSystems software packages are used to model the electrical and mechanical elements of hybrid vehicles and implement a fuzzy logic strategy
A practical introduction to fuzzy logic using LISP
Argüelles Mendez, Luis
2016-01-01
This book makes use of the LISP programming language to provide readers with the necessary background to understand and use fuzzy logic to solve simple to medium-complexity real-world problems. It introduces the basics of LISP required to use a Fuzzy LISP programming toolbox, which was specifically implemented by the author to “teach” the theory behind fuzzy logic and at the same time equip readers to use their newly-acquired knowledge to build fuzzy models of increasing complexity. The book fills an important gap in the literature, providing readers with a practice-oriented reference guide to fuzzy logic that offers more complexity than popular books yet is more accessible than other mathematical treatises on the topic. As such, students in first-year university courses with a basic tertiary mathematical background and no previous experience with programming should be able to easily follow the content. The book is intended for students and professionals in the fields of computer science and engineering, ...
Implementation of a Fuzzy Logic Speed Controller for a Permanent ...
African Journals Online (AJOL)
The purpose is to achieve accurate trajectory control of the speed of permanent magnet brushless DC Motor, especially when the motor and load parameters are unknown. Based on the mathematic model of BLDCM, a fuzzy logic controller is designed, and the membership function is composed by Gauss function.
Fuzzy logic control of the building structure with CLEMR dampers
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.
Pneumatic motor speed control by trajectory tracking fuzzy logic ...
Indian Academy of Sciences (India)
In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is deﬁned to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to ﬁnd the TTFLC boundary values of membership functions ...
On Theories and Models in Fuzzy Predicate Logics
Czech Academy of Sciences Publication Activity Database
Hájek, Petr; Cintula, Petr
2006-01-01
Roč. 71, č. 3 (2006), s. 863-880 ISSN 0022-4812 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy logic * model theory * witnessed models * conservative extension * completeness theorem Subject RIV: BA - General Mathematics Impact factor: 0.664, year: 2006
Different control applications on a vehicle using fuzzy logic control
Indian Academy of Sciences (India)
Abstract. In this paper, the active suspension control of a vehicle model that has five degrees of freedom with a passenger seat using a fuzzy logic controller is studied. Three cases are taken into account as different control applications. In the first case, the vehicle model having passive suspensions with an active passenger.
A fuzzy logic based clustering strategy for improving vehicular ad ...
Indian Academy of Sciences (India)
ITS proposes to manage vehicle traffic, support drivers with safety .... the same time. The vehicle that sends firstly a message for inviting the vehicles to join and has more cluster members will be elected as a cluster head. There are ... In this study, an alternative approach using fuzzy logic under dynamic network conditions.
A Self-Organising Fuzzy Logic Controller | Ekemezie | Nigerian ...
African Journals Online (AJOL)
One major drawback of fuzzy logic controllers is the difficulty encountered in the construction of a rule- base that is suitable for the controlled process. In this paper we tackle this problem by proposing an algorithm that allows a designer to initially specify a possibly inaccurate rule-base, which is then made more and more ...
Pneumatic motor speed control by trajectory tracking fuzzy logic
Indian Academy of Sciences (India)
In this study, trajectory tracking fuzzy logic controller (TTFLC) is proposed for the speed control of a pneumatic motor (PM). A third order trajectory is deﬁned to determine the trajectory function that has to be tracked by the PM speed. Genetic algorithm (GA) is used to ﬁnd the TTFLC boundary values of membership functions ...
Towards Metamathematics of Weak Arithmetics over Fuzzy Logic
Czech Academy of Sciences Publication Activity Database
Hájek, Petr
2011-01-01
Roč. 19, č. 3 (2011), s. 467-475 ISSN 1367-0751 R&D Projects: GA AV ČR IAA100300503 Institutional research plan: CEZ:AV0Z10300504 Keywords : weak arithmetics * mathematical fuzzy logic * Gödel’s theorem * essential undecidability Subject RIV: BA - General Mathematics Impact factor: 0.913, year: 2011
Fuzzy logic control of vehicle suspensions with dry friction nonlinearity
Indian Academy of Sciences (India)
We design and investigate the performance of fuzzy logic-controlled (FLC) active suspensions on a nonlinear vehicle model with four degrees of freedom, without causing any degeneration in suspension working limits. Force actuators were mounted parallel to the suspensions. In this new approach, linear combinations of ...
Normal Forms for Fuzzy Logics: A Proof-Theoretic Approach
Czech Academy of Sciences Publication Activity Database
Cintula, Petr; Metcalfe, G.
2007-01-01
Roč. 46, č. 5-6 (2007), s. 347-363 ISSN 1432-0665 R&D Projects: GA MŠk(CZ) 1M0545 Institutional research plan: CEZ:AV0Z10300504 Keywords : fuzzy logic * normal form * proof theory * hypersequents Subject RIV: BA - General Mathematics Impact factor: 0.620, year: 2007
modelling room cooling capacity with fuzzy logic procedure
African Journals Online (AJOL)
The primary aim of this study is to develop a model for estimation of the cooling requirement of residential rooms. Fuzzy logic was employed to model four input variables (window area (m2), roof area (m2), external wall area (m2) and internal load (Watt). The algorithm of the inference engine applied sets of 81 linguistic ...
Comments on Interpretability and Decidability in Fuzzy Logic
Czech Academy of Sciences Publication Activity Database
Hájek, Petr
2011-01-01
Roč. 21, č. 5 (2011), s. 823-828 ISSN 0955-792X R&D Projects: GA ČR GEICC/08/E018 Institutional research plan: CEZ:AV0Z10300504 Keywords : mathematical fuzzy logic * interpretability * decidability Subject RIV: BA - General Mathematics Impact factor: 0.611, year: 2011
Nonlinear Aerodynamic Modeling From Flight Data Using Advanced Piloted Maneuvers and Fuzzy Logic
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.
Fuzzy-logic-based safety verification framework for nuclear power plants.
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.
Fuzzy logic techniques for blotch feature evaluation in dermoscopy images.
Khan, Azmath; Gupta, Kapil; Stanley, R J; Stoecker, William V; Moss, Randy H; Argenziano, Giuseppe; Soyer, H Peter; Rabinovitz, Harold S; Cognetta, Armand B
2009-01-01
Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative color and blotch size yield the highest diagnostic accuracy of 81.2%.
An Innovative Fuzzy-Logic-Based Methodology for Trend Identification
International Nuclear Information System (INIS)
Wang Xin; Tsoukalas, Lefteri H.; Wei, Thomas Y.C.; Reifman, Jaques
2001-01-01
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
FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS
Directory of Open Access Journals (Sweden)
Zhan Wei Siew
2012-12-01
Full Text Available Wireless sensor networks (WSNs have been vastly developed due to the advances in microelectromechanical systems (MEMS using WSN to study and monitor the environments towards climates changes. In environmental monitoring, sensors are randomly deployed over the interest area to periodically sense the physical environments for a few months or even a year. Therefore, to prolong the network lifetime with limited battery capacity becomes a challenging issue. Low energy adaptive cluster hierarchical (LEACH is the common clustering protocol that aim to reduce the energy consumption by rotating the heavy workload cluster heads (CHs. The CHs election in LEACH is based on probability model which will lead to inefficient in energy consumption due to least desired CHs location in the network. In WSNs, the CHs location can directly influence the network energy consumption and further affect the network lifetime. In this paper, factors which will affect the network lifetime will be presented and the demonstration of fuzzy logic based CH selection conducted in base station (BS will also be carried out. To select suitable CHs that will prolong the network first node dies (FND round and consistent throughput to the BS, energy level and distance to the BS are selected as fuzzy inputs.
Using Fuzzy Logic to Enhance Stereo Matching in Multiresolution Images
Directory of Open Access Journals (Sweden)
Marcos D. Medeiros
2010-01-01
Full Text Available 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.
Fuzzy logic and information fusion to commemorate the 70th birthday of Professor Gaspar Mayor
Sastre, Joan
2016-01-01
This book offers a timely report on key theories and applications of soft-computing. Written in honour of Professor Gaspar Mayor on his 70th birthday, it primarily focuses on areas related to his research, including fuzzy binary operators, aggregation functions, multi-distances, and fuzzy consensus/decision models. It also discusses a number of interesting applications such as the implementation of fuzzy mathematical morphology based on Mayor-Torrens t-norms. Importantly, the different chapters, authored by leading experts, present novel results and offer new perspectives on different aspects of Mayor’s research. The book also includes an overview of evolutionary fuzzy systems, a topic that is not one of Mayor’s main areas of interest, and a final chapter written by the Spanish pioneer in fuzzy logic, Professor E. Trillas. Computer and decision scientists, knowledge engineers and mathematicians alike will find here an authoritative overview of key soft-computing concepts and techniques.
Detection of Stator Winding Fault in Induction Motor Using Fuzzy Logic with Optimal Rules
Directory of Open Access Journals (Sweden)
Hamid Fekri Azgomi
2013-04-01
Full Text Available Induction motors are critical components in many industrial processes. Therefore, swift, precise and reliable monitoring and fault detection systems are required to prevent any further damages. The online monitoring of induction motors has been becoming increasingly important. The main difficulty in this task is the lack of an accurate analytical model to describe a faulty motor. A fuzzy logic approach may help to diagnose traction motor faults. This paper presents a simple method for the detection of stator winding faults (which make up 38% of induction motor failures based on monitoring the line/terminal current amplitudes. In this method, fuzzy logic is used to make decisions about the stator motor condition. In fact, fuzzy logic is reminiscent of human thinking processes and natural language enabling decisions to be made based on vague information. The motor condition is described using linguistic variables. Fuzzy subsets and the corresponding membership functions describe stator current amplitudes. A knowledge base, comprising rule and data bases, is built to support the fuzzy inference. Simulation results are presented to verify the accuracy of motor’s fault detection and knowledge extraction feasibility. The preliminary results show that the proposed fuzzy approach can be used for accurate stator fault diagnosis.
Control of motion stability of the line tracer robot using fuzzy logic and kalman filter
Novelan, M. S.; Tulus; Zamzami, E. M.
2018-03-01
Setting of motion and balance line tracer robot two wheels is actually a combination of a two-wheeled robot balance concept and the concept of line follower robot. The main objective of this research is to maintain the robot in an upright and can move to follow the line of the Wizard while maintaining balance. In this study the motion balance system on line tracer robot by considering the presence of a noise, so that it takes the estimator is used to mengestimasi the line tracer robot motion. The estimation is done by the method of Kalman Filter and the combination of Fuzzy logic-Fuzzy Kalman Filter called Kalman Filter, as well as optimal smooting. Based on the results of the study, the value of the output of the fuzzy results obtained from the sensor input value has been filtered before entering the calculation of the fuzzy. The results of the output of the fuzzy logic hasn’t been able to control dc motors are well balanced at the moment to be able to run. The results of the fuzzy logic by using membership function of triangular membership function or yet can control with good dc motor movement in order to be balanced
Postoperative vomiting in pediatric oncologic patients: prediction by a fuzzy logic model.
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.
Directory of Open Access Journals (Sweden)
Era Purwanto
2010-10-01
Full Text Available In response to concerns about energy cost, energy dependence, and environmental damage, a rekindling of interest in electric vehicles (EV’s has been obvious. Thus, the development of power electronics technology for EV’s will take an accelerated pace to fulfill the market needs, regarding with the problem in this paper is presented development of fuzzy logic inverter in induction motor control for electric vehicle propulsion. The Fuzzy logic inverter is developed in this system to directed toward developing an improved propulsion system for electric vehicles applications, the fuzzy logic controller is used for switching process. This paper is describes the design concepts, configuration, controller for inverter fuzzy logic and drive system is developed for this high-performance electric vehicle.
A fuzzy logic controlled superconducting magnetic energy storage, SMES frequency stabilizer
Energy Technology Data Exchange (ETDEWEB)
Hemeida, Ashraf Mohamed [E.E. Dept, Higher Institute of Energy, South Valley University, Aswan (Egypt)
2010-06-15
This paper presents application of fuzzy logic controlled superconducting magnetic energy storage device, SMES to damp the frequency oscillations of interconnected two-area power systems due to load excursions. The system frequency oscillations appear due to load disturbance. To stabilize the system frequency oscillations, the active power can be controlled via superconducting magnetic energy storage device, SMES. The error in the area control and its rate of change is used as controller input signals to the proposed fuzzy logic controller. In order to judge the effect of the proposed fuzzy logic controlled SMES, a comparative study is made between its effect and the effect of the conventional proportional plus integral (PI) controlled SMES. The studied system consists of two-area (thermal-thermal) power system each one equipped with SMES unit. The time simulation results indicate the superiority of the proposed fuzzy logic controlled SMES over the conventional PI SMES in damping the system oscillations and reach quickly to zero frequency deviation. The system is modeled and solved by using MATLAB software. (author)
A SELF-ORGANISING FUZZY LOGIC CONTROLLER
African Journals Online (AJOL)
ES Obe
centroid method will be employed. Since the ultimate result of the fuzzy reasoning process is the defuzzified output, it is necessary first of all to choose a defuzzification method that is suitable to the proposed strategy. The weighted averaging method of defuzzification described in [5], which is similar to the centroid method, is.
Autonomous Control of a Quadrotor UAV Using Fuzzy Logic
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
Intelligent control a hybrid approach based on fuzzy logic, neural networks and genetic algorithms
Siddique, Nazmul
2014-01-01
Intelligent Control considers non-traditional modelling and control approaches to nonlinear systems. Fuzzy logic, neural networks and evolutionary computing techniques are the main tools used. The book presents a modular switching fuzzy logic controller where a PD-type fuzzy controller is executed first followed by a PI-type fuzzy controller thus improving the performance of the controller compared with a PID-type fuzzy controller. The advantage of the switching-type fuzzy controller is that it uses one rule-base thus minimises the rule-base during execution. A single rule-base is developed by merging the membership functions for change of error of the PD-type controller and sum of error of the PI-type controller. Membership functions are then optimized using evolutionary algorithms. Since the two fuzzy controllers were executed in series, necessary further tuning of the differential and integral scaling factors of the controller is then performed. Neural-network-based tuning for the scaling parameters of t...
System control fuzzy neural sewage pumping stations using genetic algorithms
Directory of Open Access Journals (Sweden)
Владлен Николаевич Кузнецов
2015-06-01
Full Text Available It is considered the system of management of sewage pumping station with regulators based on a neuron network with fuzzy logic. Linguistic rules for the controller based on fuzzy logic, maintaining the level of effluent in the receiving tank within the prescribed limits are developed. The use of genetic algorithms for neuron network training is shown.
Self-tuning fuzzy logic nuclear reactor controller
International Nuclear Information System (INIS)
Sharif Heger, A.; Alang-Rashid, N.K.
1996-01-01
We present a method for self-tuning of fuzzy logic controllers based on the estimation of the optimum value of the centroids of its output fuzzy set. The method can be implemented on-line and does not require modification of membership functions and control rules. The main features of this method are: the rules are left intact to retain the operator's expertise in the FLC rule base, and the parameters that require any adjustment are identifiable in advance and their number is kept at a minimum. Therefore, the use of this method preserves the control statements in the original form. Results of simulation and actual tests show that this tuning method improves the performance of fuzzy logic controllers in following the desired reactor power level trajectories. In addition, this method demonstrates a similar improvement for power up and power down experiments, based on both simulation and actual case studies. For these experiments, the control rules for the fuzzy logic controller were derived from control statements that expressed the relationships between error, rate of error change, and duration of direction of control rod movements
Fuzzy logic-based tumor-marker profiles improved sensitivity in the diagnosis of lung cancer.
Schneider, Joachim; Bitterlich, Norman; Velcovsky, Hans-Georg; Morr, Harald; Katz, Norbert; Eigenbrodt, Erich
2002-06-01
The aim of this study was to improve the diagnostic efficiency of tumor markers in the diagnosis of lung cancer, by the mathematical evaluation of a tumor marker profile employing fuzzy logic modelling. A panel of four tumor markers, i.e., carcinoembryonic antigen (CEA), cytokeratin 19 antibody (CYFRA 21-1), neuron-specific enolase (NSE), squamous cell carcinoma-related antigen (SCC) and, additionally, C-reactive protein (CRP), was measured in 175 newly diagnosed lung cancer patients with different histological types and stages. Results were compared with those in 120 control subjects, including 27 with chronic obstructive pulmonary diseases (COPD), 65 with pneumoconiosis, and 11 persons with acute inflammatory lung diseases. A classificator was developed using a fuzzy-logic rule-based system. Application of the fuzzy-logic rule-based system to the tumor marker values of CYFRA 21-1, NSE, and CRP yielded an increase in sensitivity of approximately 20%, i.e., 92%, compared with that of the best single marker, CYFRA 21-1(sensitivity, 72%). The corresponding specificity was 95%. The fuzzy classificator significantly improved the sensitivity of the tumor marker panel in stages I and IIIa for non-small-cell lung cancer, as well as in "limited disease" status for small-cell lung cancer. Also, the diagnosis of other stages of lung cancer was enhanced. Fuzzy-logic analysis was proven to be more powerful than the measurement of single markers alone or combinations using multiple logistic regression analysis of all markers. Therefore, fuzzy logic offers a promising diagnostic tool to improve tumor marker efficiency.
Fuzzy-logic based learning style prediction in e-learning using web ...
Indian Academy of Sciences (India)
humanoid robot. IJCSSE 26(3). Triantafillou E, Pomportsis A and Georgiadou E 2002 AES-CS: Adaptive educational system base on cognitive styles. In: Proceedings AH2002 Workshop, 10–20. Wilges B, Mateus G P, Nassar S M and Bastos R C 2012 Integration of BDI agent with fuzzy logic in a virtual learning environment.
Q-V droop control using fuzzy logic and reciprocal characteristic
DEFF Research Database (Denmark)
Wanga, Lu; Hu, Yanting; Chen, Zhe
2014-01-01
electric power at distributed voltage level, which not only is an autonomous system, but also can be connected to the main grid. To improve the stability and controllability of the power grid, this paper presents an improved Q-V droop control strategy using fuzzy logic controller and reciprocal...
Fuzzy, crisp, and human logic in e-commerce marketing data mining
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.
Fuzzy logic estimator of rotor time constant in induction motors
Energy Technology Data Exchange (ETDEWEB)
Alminoja, J. [Tampere University of Technology (Finland). Control Engineering Laboratory; Koivo, H. [Helsinki University of Technology, Otaniemi (Finland). Control Engineering Laboratory
1997-12-31
Vector control of AC machines is a well-known and widely used technique in induction machine control. It offers an exact method for speed control of induction motors, but it is also sensitive to the changes in machine parameters. E.g. rotor time constant has a strong dependence on temperature. In this paper a fuzzy logic estimator is developed, with which the rotor time constant can be estimated when the machine has a load. It is more simple than the estimators proposed in the literature. The fuzzy estimator is tested by simulation when step-wise abrupt changes and slow drifting occurs. (orig.) 7 refs.
LA LÓGICA DIFUSA COMPENSATORIA / THE COMPENSATORY FUZZY LOGIC
Directory of Open Access Journals (Sweden)
Jesús Cejas-Montero
2011-06-01
Full Text Available
La Lógica Difusa Compensatoria es un modelo lógico que permite la modelación simultánea de los procesos deductivos y de toma de decisiones. Sus características más importantes son: la flexibilidad, la tolerancia con la imprecisión, la capacidad para moldear problemas no-lineales y su fundamento en el lenguaje de sentido común. El artículo pretende llevar a la comunidad académico-empresarial las ideas fundamentales de la Lógica Difusa Compensatoria, ilustrándola en sus posibles campos de aplicación para lograr la competitividad de una organización.
Abstract
The Compensatory Fuzzy Logic is a logical model that allows the simultaneous modeling of the deductive and decision-making processes. The most important characteristics of Compensatory Fuzzy Logic are: the flexibility, the tolerance with the inaccuracy, the capacity to model no-lineal problems and its foundation in the language of common sense. The article seeks to bring the basic ideas of the Compensatory Fuzzy Logic to the academic–managerial community, illustrating it in its possible fields of application, in order to achieve the competitiveness of an organization.
A Study on the Fuzzy-Logic-Based Solar Power MPPT Algorithms Using Different Fuzzy Input Variables
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Jaw-Kuen Shiau
2015-04-01
Full Text Available Maximum power point tracking (MPPT is one of the key functions of the solar power management system in solar energy deployment. This paper investigates the design of fuzzy-logic-based solar power MPPT algorithms using different fuzzy input variables. Six fuzzy MPPT algorithms, based on different input variables, were considered in this study, namely (i slope (of solar power-versus-solar voltage and changes of the slope; (ii slope and variation of the power; (iii variation of power and variation of voltage; (iv variation of power and variation of current; (v sum of conductance and increment of the conductance; and (vi sum of angles of arctangent of the conductance and arctangent of increment of the conductance. Algorithms (i–(iv have two input variables each while algorithms (v and (vi use a single input variable. The fuzzy logic MPPT function is deployed using a buck-boost power converter. This paper presents the details of the determinations, considerations of the fuzzy rules, as well as advantages and disadvantages of each MPPT algorithm based upon photovoltaic (PV cell properties. The range of the input variable of Algorithm (vi is finite and the maximum power point condition is well defined in steady condition and, therefore, it can be used for multipurpose controller design. Computer simulations are conducted to verify the design.
Directory of Open Access Journals (Sweden)
Ying-Chih Lai
2016-05-01
Full Text Available The demand for pedestrian navigation has increased along with the rapid progress in mobile and wearable devices. This study develops an accurate and usable Step Length Estimation (SLE method for a Pedestrian Dead Reckoning (PDR system with features including a wide range of step lengths, a self-contained system, and real-time computing, based on the multi-sensor fusion and Fuzzy Logic (FL algorithms. The wide-range SLE developed in this study was achieved by using a knowledge-based method to model the walking patterns of the user. The input variables of the FL are step strength and frequency, and the output is the estimated step length. Moreover, a waist-mounted sensor module has been developed using low-cost inertial sensors. Since low-cost sensors suffer from various errors, a calibration procedure has been utilized to improve accuracy. The proposed PDR scheme in this study demonstrates its ability to be implemented on waist-mounted devices in real time and is suitable for the indoor and outdoor environments considered in this study without the need for map information or any pre-installed infrastructure. The experiment results show that the maximum distance error was within 1.2% of 116.51 m in an indoor environment and was 1.78% of 385.2 m in an outdoor environment.
Fuzzy logic techniques for rendezvous and docking of two geostationary satellites
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.
On enhancing on-line collaboration using fuzzy logic modeling
Directory of Open Access Journals (Sweden)
Leontios J. Hadjileontiadis
2004-04-01
Full Text Available Web-based collaboration calls for professional skills and competences to the benefit of the quality of the collaboration and its output. Within this framework, educational virtual environments may provide a means for training upon these skills and in particular the collaborative ones. On the basis of the existing technological means such training may be enhanced even more. Designing considerations towards this direction include the close follow-up of the collaborative activity and provision of support grounded upon a pedagogical background. To this vein, a fuzzy logic-based expert system, namely Collaboration/Reflection-Fuzzy Inference System (C/R-FIS, is presented in this paper. By means of interconnected FISs, the C/R-FIS expert system automatically evaluates the collaborative activity of two peers, during their asynchronous, written, web-based collaboration. This information is used for the provision of adaptive support to peers during their collaboration, towards equilibrium of their collaborative activity. In particular, this enhanced formative feedback aims at diminishing the possible dissonance between the individual collaborative skills by challenging self-adjustment procedures. The proposed model extents the evaluation system of a web-based collaborative tool namely Lin2k, which has served as a test-bed for the C/R-FIS experimental use. Results from its experimental use have proved the potentiality of the proposed model to significantly contribute to the enhancement of the collaborative activity and its transferability to other collaborative learning contexts, such as medicine, environmental engineering, law, and music education.
Hybrid neural network and fuzzy logic approaches for rendezvous and capture in space
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.
Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor
International Nuclear Information System (INIS)
Zainal, Nurul Afiqah; Tat, Chan Sooi; Ajisman
2016-01-01
Renewable energy produced by solar module gives advantages for generated three- phase induction motor in remote area. But, solar module's output 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. (paper)
Fuzzy logic in automatic control devices; La logique floue dans les automatismes du SIG
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Belorgey, J. [CEA Saclay, 91 - Gif-sur-Yvette (France). Dept. d' Astrophysique, de la Physique des Particules, de la Physique Nucleaire et de l' Instrumentation Associee
1998-03-01
Fuzzy logic is a theory that, applied to an automatic control device, allows to perform a regulation as efficiently as an operating expert could have done manually. The description of the behaviour of a regulation system implies the use of laws such as 'if...then', these laws link input variables that are 'conditions' to output variables that are 'conclusions'. In DAPNIA facilities fuzzy logic has been used to improve the performances of 3 control systems: -the regulation of the helium cycle compressor of a condenser, this regulation has required 21 laws, 4 conditions and 3 conclusions, -the regulation of the temperature of the LHC testing station at STCM, and -the regulation of the temperature of hydrogen target for the CLAS experiment, by means of fuzzy logic temperature stability has been driven from {+-}150 mK to {+-}20 mK, this regulation is based on 9 laws, 2 conditions and 2 conclusions. The application of fuzzy logic to regulation is presented on a simple example. (A.C.)
Fuzzy Logic Controlled Solar Module for Driving Three- Phase Induction Motor
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.
A "fuzzy"-logic language for encoding multiple physical traits in biomolecules.
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.
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.
Amezquita-Sanchez, Juan P.; Valtierra-Rodriguez, Martin; Perez-Ramirez, Carlos A.; Camarena-Martinez, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J.
2017-07-01
Squirrel-cage induction motors (SCIMs) are key machines in many industrial applications. In this regard, the monitoring of their operating condition aiming at avoiding damage and reducing economical losses has become a demanding task for industry. In the literature, several techniques and methodologies to detect faults that affect the integrity and performance of SCIMs have been proposed. However, they have only been focused on analyzing either the start-up transient or the steady-state operation regimes, two common operating scenarios in real practice. In this work, a novel methodology for broken rotor bar (BRB) detection in SCIMs during both start-up and steady-state operation regimes is proposed. It consists of two main steps. In the first one, the analysis of three-axis vibration signals using fractal dimension (FD) theory is carried out. Since different FD-based algorithms can give different results, three algorithms named Katz’ FD, Higuchi’s FD, and box dimension, are tested. In the second step, a fuzzy logic system for each regime is presented for automatic diagnosis. To validate the proposal, a motor with different damage levels has been tested: one with a partially BRB, a second with one fully BRB, and the third with two BRBs. The obtained results demonstrate the proposed effectiveness.
International Nuclear Information System (INIS)
Amezquita-Sanchez, Juan P; Valtierra-Rodriguez, Martin; Perez-Ramirez, Carlos A; Camarena-Martinez, David; Garcia-Perez, Arturo; Romero-Troncoso, Rene J
2017-01-01
Squirrel-cage induction motors (SCIMs) are key machines in many industrial applications. In this regard, the monitoring of their operating condition aiming at avoiding damage and reducing economical losses has become a demanding task for industry. In the literature, several techniques and methodologies to detect faults that affect the integrity and performance of SCIMs have been proposed. However, they have only been focused on analyzing either the start-up transient or the steady-state operation regimes, two common operating scenarios in real practice. In this work, a novel methodology for broken rotor bar (BRB) detection in SCIMs during both start-up and steady-state operation regimes is proposed. It consists of two main steps. In the first one, the analysis of three-axis vibration signals using fractal dimension (FD) theory is carried out. Since different FD-based algorithms can give different results, three algorithms named Katz’ FD, Higuchi’s FD, and box dimension, are tested. In the second step, a fuzzy logic system for each regime is presented for automatic diagnosis. To validate the proposal, a motor with different damage levels has been tested: one with a partially BRB, a second with one fully BRB, and the third with two BRBs. The obtained results demonstrate the proposed effectiveness. (paper)
Fuzzy logic utilization for the diagnosis of metallic loose part impact in nuclear power plant
International Nuclear Information System (INIS)
Oh, Y.-G.; Hong, H.-P.; Han, S.-J.; Chun, C.S.; Kim, B.-K.
1996-01-01
In consideration of the fuzzy nature of impact signals detected from the complex mechanical structures in a nuclear power plant under operation. Loose Part Monitoring System with a signal processing technique utilizing fuzzy logic is proposed. In the proposed Fuzzy Loose Part Monitoring System design, comprehensive relations among the impact signal features are taken into account in the fuzzy rule bases for the alarm discrimination and impact event diagnosis. Through the performance test with a mock-up facility, the proposed approach for the loose parts monitoring and diagnosis has been revealed to be effective not only in suppressing the false alarm generation but also in characterizing the metallic loose-part impact event, from the points of Possible Impacted-Area and Degree of Impact Magnitude
Postmodern Fuzzy System Theory: A Deconstruction Approach Based on Kabbalah
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Gabriel Burstein
2014-11-01
Full Text Available Modern general system theory proposed a holistic integrative approach based on input-state-output dynamics as opposed to the traditional reductionist detail based approach. Information complexity and uncertainty required a fuzzy system theory, based on fuzzy sets and fuzzy logic. While successful in dealing with analysis, synthesis and control of technical engineering systems, general system theory and fuzzy system theory could not fully deal with humanistic and human-like intelligent systems which combine technical engineering components with human or human-like components characterized by their cognitive, emotional/motivational and behavioral/action levels of operation. Such humanistic systems are essential in artificial intelligence, cognitive and behavioral science applications, organization management and social systems, man-machine systems or human factor systems, behavioral knowledge based economics and finance applications. We are introducing here a “postmodern fuzzy system theory” for controlled state dynamics and output fuzzy systems and fuzzy rule based systems using our earlier postmodern fuzzy set theory and a Kabbalah possible worlds model of modal logic and semantics type. In order to create a postmodern fuzzy system theory, we “deconstruct” a fuzzy system in order to incorporate in it the cognitive, emotional and behavioral actions and expressions levels characteristic for humanistic systems. Kabbalah offers a structural, fractal and hierarchic model for integrating cognition, emotions and behavior. We obtain a canonic deconstruction for a fuzzy system into its cognitive, emotional and behavioral fuzzy subsystems.
Fuzzy logic augmentation of nature-inspired optimization metaheuristics theory and applications
Melin, Patricia
2015-01-01
This book describes recent advances on fuzzy logic augmentation of nature-inspired optimization metaheuristics and their application in areas such as intelligent control and robotics, pattern recognition, time series prediction and optimization of complex problems. The book is organized in two main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of theoretical aspects of fuzzy logic augmentation of nature-inspired optimization metaheuristics, which basically consists of papers that propose new optimization algorithms enhanced using fuzzy systems. The second part contains papers with the main theme of application of optimization algorithms, which are basically papers using nature-inspired techniques to achieve optimization of complex optimization problems in diverse areas of application.
Directory of Open Access Journals (Sweden)
Maria Valeria Piras
2015-01-01
Full Text Available Fuzzy logic applied to the visual inspection of existing buildings has been proposed in relation to simple structures. Isostatic structures are characterized by a unique and known collapse mechanism, which does not vary with geometry or load change. In this paper we apply fuzzy logic to visual inspection for complex structures such as hyperstatic ones in which the collapse mechanism depends not only on the geometry but also on the size and disposition of loads. The goal of this paper is to give relevant weight, in the fuzzy analysis, not only to the single expression of degradation, due to its localization within the element, but also to the structural element itself by assigning a different resistance to the various elements. The underlying aim of the proposed method is to manage, evaluate, and process all the information coming from visual inspections in order to realize a management information system for the evaluation of the safety level of even complex structures.
Bioimpedance-based identification of malnutrition using fuzzy logic
International Nuclear Information System (INIS)
Wieskotten, S; Isermann, R; Heinke, S; Wabel, P; Moissl, U; Becker, J; Pirlich, M; Keymling, M
2008-01-01
Protein-energy malnutrition reduces the quality of life, lengthens the time in hospital and dramatically increases mortality. Currently there is no simple and objective method available for assessing nutritional status and identifying malnutrition. The aim of this work is to develop a novel assistance system that supports the physician in the assessment of the nutritional status. Therefore, three subject groups were investigated: the first group consisted of 688 healthy subjects. Two additional groups consisted of 707 patients: 94 patients with primary diseases that are known to cause malnutrition, and 613 patients from a hospital admission screening. In all subjects bioimpedance spectroscopy measurements were performed, and the body composition was calculated. Additionally, in all patients the nutritional status was assessed by the subjective global assessment score. These data are used for the development and validation of the assistance system. The basic idea of the system is that nutritional status is reflected by body composition. Hence, features of the nutritional status, based on the body composition, are determined and compared with reference ranges, derived from healthy subjects' data. The differences are evaluated by a fuzzy logic system or a decision tree in order to identify malnourished patients. The novel assistance system allows the identification of malnourished patients, and it can be applied for screening and monitoring of the nutritional status of hospital patients
Fault level prediction for distribution network using fuzzy logic identifier
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Shi Fang
2016-01-01
Full Text Available With the increasing penetration of the renewable power energy sources, the potential fault current of the distribution power systems changes more frequently as the connection structure of the distribution power system varies from time to time. Traditionally, the fault level can be estimated through short circuit analysis which is time consuming and sometimes difficult as it needs to know the parameters of the transmission line and transformers as well as the structure of the power system. In this paper, an online-used fault level prediction method is proposed via monitoring the phasor value of the local positive-sequence voltage and current. The ratio of the voltage change and current change are used to distinguish the natural deviation of the load from the switching operations or disturbances on the grid side. Several continuous changes of the voltage and current caused by load fluctuations are recorded and used to parameterize the equivalent circuit of the power system and to estimate the fault current level. A fuzzy logic identifier is used for adaptively selecting and recording the satisfactory changes by defining an index of confidence level. The implementation of the proposed scheme is demonstrated in a relay after introducing a low-voltage blocking function. A typical distribution power system with renewable generators is established in PSCAD/EMTDC and is used to verify the effectiveness and accuracy of the proposed method under various load changing conditions.
Mapping Shape Geometry And Emotions Using Fuzzy Logic
DEFF Research Database (Denmark)
Achiche, Sofiane; Ahmed, Saeema
2008-01-01
An important aspect of artifact/product design is defining the aesthetic and emotional value. The success of a product is not only dependent on its functionality but also on the emotional value that it creates to its user. However, if several designers are faced with a task to create an object...... that would evoke a certain emotion (aggressive, soft, heavy, friendly, etc.), each would most likely interpret the emotion with a different set of geometric features and shapes. In this paper the authors propose an approach to formalize the relationship between geometric information of a 3D object...... and the intended emotion using fuzzy logic. To achieve this; 3D objects (shapes) created by design engineering students to match a set of words/emotions were analyzed. The authors identified geometric information as inputs of the fuzzy model and developed a set of fuzzy if/then rules to map the relationships...
Fuzzy Temporal Logic Based Railway Passenger Flow Forecast Model
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
Simulation of New Simple Fuzzy Logic Maximum Power Point ...
African Journals Online (AJOL)
Journal of Fundamental and Applied Sciences ... The input parameters and duty cycle D are used to generate the optimal MPPT under different operating conditions, The photovoltaic system simulated and constructed by photovoltaic arrays, a DC/DC boost converter, a fuzzy MPPT control and a resistive load, The Fuzzy ...
Optimization of Neuro-Fuzzy System
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M. Sarosa
2007-05-01
Full Text Available Neuro-fuzzy system has been shown to provide a good performance on chromosome classification but does not offer a simple method to obtain the accurate parameter values required to yield the best recognition rate. This paper presents a neuro-fuzzy system where its parameters can be automatically adjusted using genetic algorithms. The approach combines the advantages of fuzzy logic theory, neural networks, and genetic algorithms. The structure consists of a four layer feed-forward neural network that uses a GBell membership function as the output function. The proposed methodology has been applied and tested on banded chromosome classification from the Copenhagen Chromosome Database. Simulation result showed that the proposed neuro-fuzzy system optimized by genetic algorithms offers advantages in setting the parameter values, improves the recognition rate significantly and decreases the training/testing time which makes genetic neuro-fuzzy system suitable for chromosome classification.
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Turek, M. [Institute of Nano- and Biotechnologies (INB), Aachen University of Applied Sciences, Campus Juelich, Juelich (Germany); Institute of Bio- and Nanosystems (IBN), Research Centre Juelich GmbH, Juelich (Germany); Heiden, W.; Riesen, A. [Bonn-Rhein-Sieg University of Applied Sciences, Sankt Augustin (Germany); Chhabda, T.A. [Institute of Nano- and Biotechnologies (INB), Aachen University of Applied Sciences, Campus Juelich, Juelich (Germany); Schubert, J.; Zander, W. [Institute of Bio- and Nanosystems (IBN), Research Centre Juelich GmbH, Juelich (Germany); Krueger, P. [Institute of Biochemistry and Molecular Biology, RWTH Aachen, Aachen (Germany); Keusgen, M. [Institute for Pharmaceutical Chemistry, Philipps-University Marburg, Marburg (Germany); Schoening, M.J. [Institute of Nano- and Biotechnologies (INB), Aachen University of Applied Sciences, Campus Juelich, Juelich (Germany); Institute of Bio- and Nanosystems (IBN), Research Centre Juelich GmbH, Juelich (Germany)], E-mail: m.j.schoening@fz-juelich.de
2009-10-30
The cross-sensitivity of chemical sensors for several metal ions resembles in a way the overlapping sensitivity of some biological sensors, like the optical colour receptors of human retinal cone cells. While it is difficult to assign crisp classification values to measurands based on complex overlapping sensory signals, fuzzy logic offers a possibility to mathematically model such systems. Current work goes into the direction of mixed heavy metal solutions and the combination of fuzzy logic with heavy metal-sensitive, silicon-based chemical sensors for training scenarios of arbitrary sensor/probe combinations in terms of an electronic tongue. Heavy metals play an important role in environmental analysis. As trace elements as well as water impurities released from industrial processes they occur in the environment. In this work, the development of a new fuzzy logic method based on potentiometric measurements performed with three different miniaturised chalcogenide glass sensors in different heavy metal solutions will be presented. The critical validation of the developed fuzzy logic program will be demonstrated by means of measurements in unknown single- and multi-component heavy metal solutions. Limitations of this program and a comparison between calculated and expected values in terms of analyte composition and heavy metal ion concentration will be shown and discussed.