Wyniki wyszukiwania - Biblioteka Nauki

1

Mathematical Fuzzy Logic and Natural Numbers

100%

Hájek P.

Fundamenta Informaticae

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2007

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tom Vol. 81, nr 1-3

155-163

EN

A weak variant of Robinson's arithmetic Q where the binary operations of addition and multiplication are replaced by ternary relations (not necessarily defining total crisp functions) is formulated and investigated over the mathematical fuzzy logic BL". Essential undecidability of this fuzzy arithmetic is proved by a careful analysis of the classical proof of essential undecidability of arithmetic.

2

From computing with numbers to computing with words - From manipulation of measurements to manipulation of perceptions

100%

Zadeh L. A.

International Journal of Applied Mathematics and Computer Science

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2002

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tom 12

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nr 3

307-324

EN

Computing, in its usual sense, is centered on manipulation of numbers and symbols. In contrast, computing with words, or CW for short, is a methodology in which the objects of computation are words and propositions drawn from a natural language, e.g., small, large, far, heavy, not very likely, the price of gas is low and declining, Berkeley is near San Francisco, it is very unlikely that there will be a significant increase in the price of oil in the near future, etc. Computing with words is inspired by the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Familiar examples of such tasks are parking a car, driving in heavy traffic, playing golf, riding a bicycle, understanding speech and summarizing a story. Underlying this remarkable capability is the brain’s crucial ability to manipulate perceptions – perceptions of distance, size, weight, color, speed, time, direction, force, number, truth, likelihood and other characteristics of physical and mental objects. Manipulation of perceptions plays a key role in human recognition, decision and execution processes. As a methodology, computing with words provides a foundation for a computational theory of perceptions – a theory which may have an important bearing on how humans make – and machines might make – perception-based rational decisions in an environment of imprecision, uncertainty and partial truth. A basic difference between perceptions and measurements is that, in general, measurements are crisp whereas perceptions are fuzzy. One of the fundamental aims of science has been and continues to be that of progressing from perceptions to measurements. Pursuit of this aim has led to brilliant successes. We have sent men to the moon; we can build computers that are capable of performing billions of computations per second; we have constructed telescopes that can explore the far reaches of the universe; and we can date the age of rocks that are millions of years old. But alongside the brilliant successes stand conspicuous underachievements and outright failures. We cannot build robots which can move with the agility of animals or humans; we cannot automate driving in heavy traffic; we cannot translate from one language to another at the level of a human interpreter; we cannot create programs which can summarize non-trivial stories; our ability to model the behavior of economic systems leaves much to be desired; and we cannot build machines that can compete with children in the performance of a wide variety of physical and cognitive tasks. It may be argued that underlying the underachievements and failures is the unavailability of a methodology for reasoning and computing with perceptions rather than measurements. An outline of such a methodology – referred to as a computational theory of perceptions – is presented in this paper. The computational theory of perceptions, or CTP for short, is based on the methodology of computing with words (CW). In CTP, words play the role of labels of perceptions and, more generally, perceptions are expressed as propositions in a natural language. CW-based techniques are employed to translate propositions expressed in a natural language into what is called the Generalized Constraint Language (GCL). In this language, the meaning of a proposition is expressed as a generalized constraint, X isr R, where X is the constrained variable, R is the constraining relation and isr is a variable copula in which r is a variable whose value defines the way in which R constrains X. Among the basic types of constraints are: possibilistic, veristic, probabilistic, random set, Pawlak set, fuzzy graph and usuality. The wide variety of constraints in GCL makes GCL a much more expressive language than the language of predicate logic. In CW, the initial and terminal data sets, IDS and TDS, are assumed to consist of propositions expressed in a natural language. These propositions are translated, respectively, into antecedent and consequent constraints. Consequent constraints are derived from antecedent constraints through the use of rules of constraint propagation. The principal constraint propagation rule is the generalized extension principle. The derived constraints are retranslated into a natural language, yielding the terminal data set (TDS). The rules of constraint propagation in CW coincide with the rules of inference in fuzzy logic. A basic problem in CW is that of explicitation of X, R and r in a generalized constraint, X isr R, which represents the meaning of a proposition, p, in a natural language. There are two major imperatives for computing with words. First, computing with words is a necessity when the available information is too imprecise to justify the use of numbers; and second, when there is a tolerance for imprecision which can be exploited to achieve tractability, robustness, low solution cost and better rapport with reality. Exploitation of the tolerance for imprecision is an issue of central importance in CW and CTP. At this juncture, the computational theory of perceptions – which is based on CW – is in its initial stages of development. In time, it may come to play an important role in the conception, design and utilization of information/intelligent systems. The role model for CW and CTP is the human mind.

3

Mathematical Fuzzy Logic: An Invitation to Interesting Research Areas

63%

Gottwald S.

Fundamenta Informaticae

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2007

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tom Vol. 81, nr 1-3

123-137

EN

The paper discusses some core topics which present open problems in the field of mathematical fuzzy logic and in the foundations of fuzzy set theory. It may reasonably be assumed that these problems shall have great influence for the future development of fuzzy logic within the next decade.

4

Fuzzy logic classifier for radio signals recognition

63%

Łopatka J. , Pędzisz M.

Journal of Telecommunications and Information Technology

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2001

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tom nr 2

31-34

EN

This paper presents a new digital modulation recognition algorithm for classifying baseband signals in the presence of additive white Gaussian noise. Elaborated classification technique uses various statistical moments of the signal amplitude, phase and frequency applied to the fuzzy classifier. Classification results are given and it is found that the technique performs well at low SNR. The benefits of this technique are that it is simple to implement, has generalization property and requires no apriori knowledge of the SNR, carrier phase or baud rate of the signal for classification.

5

Fuzzy diagnostic reasoning that takes into account the uncertainty of the relation between faults and symptoms

51%

Kościelny J. M. , Syfert M.

International Journal of Applied Mathematics and Computer Science

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2006

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tom 16

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nr 1

27-35

EN

Knowledge about the relation between faults and the observed symptoms is necessary for fault isolation. Such a relation can be expressed in various forms, including binary diagnostic matrices or information systems. The paper presents the use of fuzzy logic for diagnostic reasoning. This method enables us to take into account various kinds of uncertainties connected with diagnostic reasoning, including the uncertainty of the faults-symptoms relation. The presented methods allow us to determine the fault certainty factor as well as certainty factors of the normal and unknown process states. The unknown process state factor groups all the states with unknown and multiple faults with the states with improper residual values, while the normal state factor indicates similarity between the observed state and the pattern fault-free state.

6

Semi-quantitative Modelling of Gene Regulatory Processes with Unknown Parameter Values Using Fuzzy Logic and Petri Nets

51%

Bordon J. , Moškon M. , Zimic N. , Mraz M.

Fundamenta Informaticae

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2018

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tom Vol. 160, nr 1/2

81--100

EN

Petri nets are a well-established modelling framework in life sciences and have been widely applied to systems and synthetic biology in recent years. With the various extensions they serve as graphical and simulation interface for both qualitative and quantitative modelling approaches. In terms of quantitative approaches, Stochastic and Continuous Petri nets are extensively used for modelling biological system’s dynamics if underlying kinetic data are known. However, these are often only vaguely defined or even missing. In this paper we present a fuzzy approach, which can be used to model biological processes with unknown kinetic data in order to still obtain quantitatively relevant simulation results. We define fuzzy firing rate functions, which can be used in Continuous Petri nets and are able to describe different processes that govern the dynamics of gene expression networks. They can be used in combination with the conventional firing rate functions and applied only in the parts of the system for which the kinetic data are missing. The case study of the proposed approach is performed on models of a hypothetical repressilator and Neurospora circadian rhythm.

7

Takagi Sugeno fuzzy expert model based soft fault diagnosis for two tank interacting system

51%

Manikandan P. , Geetha M.

Archives of Control Sciences

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2014

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tom Vol. 24, no. 3

271--287

EN

The inherent characteristics of fuzzy logic theory make it suitable for fault detection and diagnosis (FDI). Fault detection can benefit from nonlinear fuzzy modeling and fault diagnosis can profit from a transparent reasoning system, which can embed operator experience, but also learn from experimental and/or simulation data. Thus, fuzzy logic-based diagnostic is advantageous since it allows the incorporation of a-priori knowledge and lets the user understand the inference of the system. In this paper, the successful use of a fuzzy FDI based system, based on dynamic fuzzy models for fault detection and diagnosis of an industrial two tank system is presented. The plant data is used for the design and validation of the fuzzy FDI system. The validation results show the effectiveness of this approach.

8

Dynamic fuzzy model for detecting verbal violence in real time

51%

Campos O. , Pancardo P. , Hernandez-Nolasco J. A.

Computer Science

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2022

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tom T. 23 (4)

467--493

EN

The crime rates in Mexico have been increasing in recent years; every day, there are reports on social media and in the news where assaults and verbal aggression by criminals can be seen. Public transportation units suffer from violence that authorities have not been able to reduce despite their efforts. This is why we have developed a fuzzy logic model that can adapt to almost any scenario thanks to the dynamism that we have implemented in each of its stages. We have obtained promising results that we believe will be of great help to the authorities for detecting the exact moment in which verbal aggression that is typical of a violent assault is happening in real time. This is a tool to help the authorities, not a substitution; we are simply making use of the latest technologies that are available to us. The goal of this paper is to provide a novel method for Mexican authorities in Mexico City in order to help the actual surveillance systems make faster decisions about whether violent assaults are happening or not.

9

Auto-tuning of PID controller based on fuzzy logic

51%

Niewiara Ł. , Zawirski K.

Computer Applications in Electrical Engineering

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2013

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tom Vol. 11

230--240

EN

Issues related to the automatic selection of the PID controller settings have been known for several years. This article describes the concept of self-tuning using fuzzy logic block (FLB). The FLB represents expert knowledge and in the process serves as the master. It describes the advantages of the designed structure of fuzzy logic simulation system application. The paper contains block diagram descriptions of designed controller and control loop. The designed tuning algorithm bases on quality indicators and rule bases which was described as membership functions. The readjustment of the controller settings takes place in subsequent iterations of the tuning process. Simulation tests result of the proposed tuning system topology were presented. As the simulation environment was used Matlab Simulink.

10

Differential protection with better stabilization for external faults

51%

Solak K. , Rebizant W.

Present Problems of Power System Control

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2011

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tom Nr 1

39-48

EN

This paper presents a new differential protection scheme for transmission lines with application of fuzzy signal processing and support of phase comparison criterion. Traditional differential relays may have problems with proper classification of external faults with CT saturation. Better protection stabilization for such cases is obtained with support of fuzzy signal processing. In proposed solution the input signals as well as the standard percentage characteristic are fuzzified. The performance of presented fuzzy protection scheme has been tested with the signals generated with use of EMTP-ATP program and compared to the traditional solution.

PL

W artykule zaprezentowano nowe zabezpieczenie różnicowe, w którym zastosowano rozmyte przetwarzanie sygnałów oraz dodatkowe kryterium porównawczo-fazowe. Proponowane zabezpieczenie jest bardziej odporne na zwarcia zewnętrzne z nasyceniem przekładników prądowych, co potwierdziły przeprowadzone testy. Proponowany algorytm testowany był na sygnałach pochodzących z symulacji w EMTP-ATP, a wyniki porównano ze standardowym przekaźnikiem różnicowym.

11

Signature recognition with a hybrid approach combining modular neural networks and fuzzy logic for response integration

51%

Beltrán M. , Melin P. , Trujillo L. , Lopez M.

Journal of Automation Mobile Robotics and Intelligent Systems

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2010

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tom Vol. 4, No. 1

20-27

EN

This paper describes a modular neural network (MNN) with fuzzy integration for the problem of signature recognition. Currently, biometric identification has gained a great deal of research interest within the pattern recognition community. For instance, many attempts have been made in order to automate the process of identifying a person’s handwritten signature; however this problem has proven to be a very difficult task. In this work, we propose a MNN that has three separate modules, each using different image features as input, these are: edges, wavelet coefficients, and the Hough transform matrix. Then, the outputs from each of these modules are combined using a Sugeno fuzzy integral and a fuzzy inference system. The experimental results obtained using a database of 30 individual’s shows that the modular architecture can achieve a very high 99.33% recognition accuracy with a test set of 150 images. Therefore, we conclude that the proposed architecture provides a suitable platform to build a signature recognition system. Furthermore we consider the verification of signatures as false acceptance, false rejection and error recognition of the MNN.

12

Response Integration in Ensemble Neural Networks using The Sugeno Integral and Fuzzy Inference System for Pattern Recognition

51%

Lopez M. , Melin P.

Journal of Automation Mobile Robotics and Intelligent Systems

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2008

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tom Vol. 2, No. 1

52-58

EN

Combining the outputs of multiple neural networks has been used in Ensemble architectures to improve the decision accuracy in many applications fields, including pattern recognition, in particular for the case of fingerprints. In this paper, we describe a set of experiments performed in order to find the optimal individual networks in terms of the architecture and training rule. In the second step, we used the fuzzy Sugeno Integral to integrate results of the ensemble neural networks. This method combines objective evidence in the form of the network's outputs, with subjective measures of their performance. In the third step, we used a Fuzzy Inference System for the decision process of finding the output of the ensemble neural networks, and finally a comparison of experimental results between Fuzzy Sugeno Integral and the Fuzzy Inference System are presented.

13

Fuzzy reactive control of wheeled mobile robot

51%

Hendzel Z.

Journal of Theoretical and Applied Mechanics

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2004

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tom Vol. 42 nr 3

503-517

EN

This paper proposed a sensor based navigation method with a fuzzy combiner for navigation of a mobile robot in uncertain environments. We discuss a fuzzy approach to path design and control of simple individual behaviours of a wheeled mobile robot in an unknown 2D environment with static obstacles. A strategy of reactive navigation is developed including three main behaviours: reaching the middle of a collision-free space behaviour, goal-seeking behaviour and wall-following behaviour. These simple individual behaviours are achieved by means of fuzzy inference systems. It is assumed that each low-level behaviour has been well developed at the design stage and then fused by the fuzzy combiner of behaviours to determine proper actions acting on the environment at the running stage. The fuzzy combiner can fuse low-level behaviours so that the mobile robot can go for the goal position without colliding with obstacles. The fuzzy combiner is a soft switch that chooses more than one low-level action to be active with different degrees through fuzzy combination at each time step. The output of the navigation level is fed into a fuzzy tracking controller that takes into account the dynamics of the mobile robot. A computer simulation have been conducted to illustrate the performance of these proposed fuzzy combiner of behaviours by a series of experiments on an emulator of the wheeled mobile robot Pioneer-2DX.

PL

W pracy analizuje się zagadnienie nawigacji odruchowej mobilnego robota kołowego w nieznanym otoczeniu. Przedstawiono rozmyte podejście do projektowania trajektorii ruchu i sterowania prostych indywidualnych zachowań mobilnego robota kołowego w nieznanym środowisku dwuwymiarowym ze statycznymi przeszkodami. Stategię odruchowej nawigacji zastosowano do realizacji trzech elementarnych zachowań: osiągnij środek wolnej przestrzeni, idź do celu, podążaj przy ścianie. Powyższe elementarne zachowania zrealizowano stosując układy z logiką rozmytą. Zakłada się że każde z elementarnych zachowań realizowane przez niższy poziom sterowania jest poprawnie zaprojektowane, a ich koordynacja odbywa się na wyższym poziomie hierarchii sterowania przez rozmyty układ koordynacyjny, którego zadaniem jest wygenerowanie właściwych sterowań umożliwiających realizację zadania nawigacji. Rozmyty układ koordynacji elementarnych zachowań łączy te zachowania generując zadaną trajektorię ruchu wybranego punktu mobilnego robota kołowego. Trajektoria ta jest realizowana przez układ niskiego poziomu sterowania ruchem nadążnym mobilnego robota kołowego uwzględniającym dynamikę obiektu sterowania. W celu weryfikacji zaproponowanego rozwiązania przeprowadzono symulacje komputerowe na emulatorze mobilnego robota Pionner-2DX.

14

Fuzzy based trust management system for cloud environment

51%

Kumar S. , Mittal S. , Singh M.

Advances in Science and Technology. Research Journal

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2016

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tom Vol. 10, nr 30

32--37

EN

Cloud computing is a business model with high degree of flexibility, scalability in providing infrastructure, platform and software as a service over the internet. Cloud promises for easiness and reduced expense to service providers and consumers. However, a lack of trust between these two stakeholders has hindered the universal accep¬tance of cloud for outsourced services. In this paper, a fuzzy based trust management system is proposed to facilitate cloud consumers in identifying trustworthy providers. The performance of the proposed system is validated through a simulation using CloudAnalyst and Simulink.

15

High molecular mix surface pressure control system

51%

Butchvarov D. , Geortchev V. , Boneva A. , Krusteva R.

Prace Instytutu Podstaw Informatyki Polskiej Akademii Nauk

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2001

|

tom Nr 928

3-6

EN

The paper presents modular system for data logging, using the fuzzy sets theory. The microprocessor system configuration includes master module, slave modules and has ability for integration of orthogonal buses to the standard system bus of XT/AT IBM PC. The architecture of the master and slave modules and the communication between them are explained in details. The software is based on fuzzy theory, using triangle member ship functions and standard gravity method.

16

Fuzzy approach to complex system analysis

51%

Mazurkiewicz J. , Nowak K. , Walkowiak T.

Journal of Polish Safety and Reliability Association

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2012

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tom Vol. 3, No. 2

169--178

EN

The paper presents a new approach to reliability and functional analysis of sophisticated complex systems using fuzzy logic. We propose to use such methodology since dependability parameters of the system are mostly approximated by experts instead of classical sources of data. Presented approach show different idea – modeling system using the unified structure – in functional sense. We assess the reliability of the system by accumulated down time. Fuzzy logic based reliability analysis, as well as Computer Information System and Discrete Transport System modeling and simulation are presented. Moreover, results of numerical experiment performed on a test case scenario related to the economic and functional aspects using proposed methodology are given. Fuzzy approach allows reducing the problem of assumptions of reliability distributions and – this way – seems to be very interesting for real systems management and tuning.

17

Saccades detection in optokinetic nystagmus - a fuzzy approach

51%

Pander T. , Czabański R. , Przybyła T. , Pojda-Wilczek D.

Journal of Medical Informatics & Technologies

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2012

|

tom Vol. 19

33--39

EN

The analysis of eye movements is valuable in both clinical work and research. One of the characteristic type of eye movements is saccade. The accurate detection of saccadic eye movements is the base for further processing of saccade parameters such velocity, amplitude and duration. This paper presents an accurate saccade detection method which is supported by the fuzzy clustering. The proposed detection function is computationally efficient and precisely determines the time position of the saccadic eye movement event. The described method is characterized by low sensitivity for any kind of noise and can be applied in the analysis of the congenital nystagmus.

18

Combining fuzzy and cellular learning automata methods for clustering wireless sensor network to increase life of the network

51%

Aramideh J. , Jelodar H.

Advances in Science and Technology. Research Journal

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2014

|

tom Vol. 8, nr 24

1--8

EN

Wireless sensor networks have attracted attention of researchers considering their abundant applications. One of the important issues in this network is limitation of energy consumption which is directly related to life of the network. One of the main works which have been done recently to confront with this problem is clustering. In this paper, an attempt has been made to present clustering method which performs clustering in two stages. In the first stage, it specifies candidate nodes for being head cluster with fuzzy method and in the next stage, the node of the head cluster is determined among the candidate nodes with cellular learning automata. Advantage of the clustering method is that clustering has been done based on three main parameters of the number of neighbors, energy level of nodes and distance between each node and sink node which results in selection of the best nodes as a candidate head of cluster nodes. Connectivity of network is also evaluated in the second part of head cluster determination. Therefore, more energy will be stored by determining suitable head clusters and creating balanced clusters in the network and consequently, life of the network increases.

19

Aircraft docking guidance system to the gate using fuzzy logic

51%

Żurawski P. , Grzesik N. , Kuźma K.

Journal of KONES

|

2018

|

tom Vol. 25, No. 3

549--556

EN

The article presents the concept of automated final process of aircraft taxiing to the gate at the terminal. On the basis of an analysis of the possibilities of aircraft taxiing in civil airports, the authors attempted at optimizing this process. The main objective of the project is to reduce the taxiing time, consequently reducing fuel consumption as well as the rotation time. As a result of the work, the authors designed a controller based on fuzzy logic, which, depending on the initial parameters, calculates the set values for the execution system of aircraft control in the horizontal plane and for the taxi speed. The controller receives two input signals, which determine two output signals. The designed controller allows comprehensive and fully automated aircraft steering. The project relies on data with regard to the apron class D, suited to handle aircraft with a wingspan of up to 52 m and the characteristics of a Boeing 767-200 in speed taxiing and the maximum turn of the nose gear. The measurements of the apron have been adopted in accordance with international regulations in the ICAO DOC 9157 “Aerodrome Design Manual”. The maximum deviation of the nose gear from the centre line was assumed 2.5 m in each direction and a safe distance behind the immobile aircraft equal to 25 m. The length of the Boeing aircraft 767-200 is below 48 m, therefore the input boundary parameters are equal to +/- 2.5 m from the centre line and 80 m from the designated aircraft stand (nose gear). The article presents the project of the controller and its optimization. The authors simulated the controller operation in the package MATLAB “Simulink”. The article ends with data analysis and final conclusions.

20

Zastosowanie logiki rozmytej w budowie modeli geologicznych

51%

Darłak B. , Kowalska-Włodarczyk M.

Nafta-Gaz

|

2009

|

tom R. 65, nr 6

454--461

PL

Przedstawiona praca jest kontynuacją próby wprowadzenia metody logiki rozmytej do rutynowych modelowań geologicznych. Wykorzystując dane laboratoryjne i otworowe uzupełniano z jej pomocą brakujące fragmenty profilowań petrofizycznych i geofizycznych. Metoda ta pozwoliła także prognozować całe brakujące profile na podstawie zestawu innych danych, ponieważ nie wymaga ona nauczyciela, czyli zbioru uczącego, opiera się natomiast na poprawnie sformułowanych regułach wnioskowania, co w modelowaniach geologicznych daje jej przewagę nad sieciami neuronowymi.

EN

This paper presents a possibilities of fuzzy logic application in geological model evaluation. Two potential fields of activity was showed. The first is to complete petrophysical and well log database. The second, more ambitious is estimation of petrophysical parameters in no data wells. It is possible because of depending of fuzzy logic on inference rules not on teaching files. It is the main advantage of fuzzy logic over ANN extrapolation method.