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## Synthesis and Analysis of Fuzzy Diagnostic Systems for Railway Bridges

### Citations

5323 |
Artificial Intelligence: A Modern Approach.
- Russell, Norvig
- 1995
(Show Context)
Citation Context ... ability of effective processing of not very precise information. This capability, together with the other qualities of human reasoning, becomes the interest centre of an artificial intelligence (AI) =-=[34]-=-. The AI methods seem to be very promising for the description and control of complicated systems. The most important of them are the possibility of processing non-numerical, linguistic information. A... |

1258 |
Rough Sets. Theoretical Aspects of Reasoning about Data.
- Pawlak
- 1991
(Show Context)
Citation Context ...e the number and shapes of input and output membership functions by means of genetic algorithms [22,28], and also to optimise the number of rules in FIS by means of the so-called theory of rough sets =-=[2,10,11,26]-=-. It is possible to use artificial neural networks [40], fuzzy cognitive maps [41], and neuro-fuzzy model. Acknowledgement The work was supported by the Czech Science Foundation - project GAČR 103/08/... |

852 |
Outline of a new approach to the analysis of complex systems and decision processes.
- ZADEH
- 1973
(Show Context)
Citation Context ...nd output variables, and the fuzzy rule base) was used because an exact general method for definition of their number does not exist [16,31]. A definition of the number of fuzzy rules is described in =-=[15,16,31,43,44,45,46]-=-, or the method in [13,22,42] can be used. The number of fuzzy rules can also be optimized by genetic algorithms and evolution strategies [1,25]. The FIS is (Fig.1) represented by a block with inputs ... |

719 |
Fuzzy Logic in Control Systems: Fuzzy Logic Controller
- Lee
- 1990
(Show Context)
Citation Context ...self-learning, robustness and easy implementation are supported at the expense of preciseness, and they are ranged into the framework of the so called “soft computing“ or “computational intelligence” =-=[16,23,27,28,30]-=-. The goal of this paper is a verification of the use of FL for the evaluation of the technical conditions of existing bridge objects, thereby also their reliability and service life, on the bases of ... |

480 |
Fuzzy sets as a basis for a theory of possibility
- Zadeh
- 1978
(Show Context)
Citation Context ...nd output variables, and the fuzzy rule base) was used because an exact general method for definition of their number does not exist [16,31]. A definition of the number of fuzzy rules is described in =-=[15,16,31,43,44,45,46]-=-, or the method in [13,22,42] can be used. The number of fuzzy rules can also be optimized by genetic algorithms and evolution strategies [1,25]. The FIS is (Fig.1) represented by a block with inputs ... |

441 |
Generating fuzzy rules by learning from examples
- Wang, Mendel
(Show Context)
Citation Context ...ase) was used because an exact general method for definition of their number does not exist [16,31]. A definition of the number of fuzzy rules is described in [15,16,31,43,44,45,46], or the method in =-=[13,22,42]-=- can be used. The number of fuzzy rules can also be optimized by genetic algorithms and evolution strategies [1,25]. The FIS is (Fig.1) represented by a block with inputs inn and output out and can be... |

74 |
Fuzzy Classifier Design
- Kuncheva
- 2000
(Show Context)
Citation Context ...ation. This model is possible perceived classification problem. Classification deals with knowledge and data characterized by uncertainty. This was realized by means of a fuzzy inference system (FIS) =-=[15,30]-=-. The heuristic approach for the creation of FIS (it means the shape and number of membership function (MF) for input and output variables, and the fuzzy rule base) was used because an exact general m... |

70 |
Hierarchical fuzzy control
- Raju, Zhou, et al.
- 1991
(Show Context)
Citation Context ...nd evolution strategies [1,25]. The FIS is (Fig.1) represented by a block with inputs inn and output out and can be defined as MISO (Multiple Inputs and Single Output) system. It is more described in =-=[16,29,23,24]-=-. in1 in2 inn . FIS fis1 out Fig.1 MISO fuzzy inference system The general structure of FIS is presented in Fig.2 [13,23]. It contains processes of fuzzification, inference and defuzzification. Inputs... |

34 |
Fuzzy Logic with Engineering Applications, 2 nd Ed
- Ross
- 2004
(Show Context)
Citation Context ...self-learning, robustness and easy implementation are supported at the expense of preciseness, and they are ranged into the framework of the so called “soft computing“ or “computational intelligence” =-=[16,23,27,28,30]-=-. The goal of this paper is a verification of the use of FL for the evaluation of the technical conditions of existing bridge objects, thereby also their reliability and service life, on the bases of ... |

27 |
Genetic Algorithms for Pattern Recognition
- Pal, Wang, et al.
- 1996
(Show Context)
Citation Context ...e number of fuzzy rules is described in [15,16,31,43,44,45,46], or the method in [13,22,42] can be used. The number of fuzzy rules can also be optimized by genetic algorithms and evolution strategies =-=[1,25]-=-. The FIS is (Fig.1) represented by a block with inputs inn and output out and can be defined as MISO (Multiple Inputs and Single Output) system. It is more described in [16,29,23,24]. in1 in2 inn . F... |

12 |
Hierarchical fuzzy control of multivariable systems
- Gegov, Frank
- 1995
(Show Context)
Citation Context ...umber of fuzzy rules in the fuzzy rule base, and the FIS can be realized ineffectively and an explanation cannot be perspicuous. This problem can be removed by a hierarchical structure (Fig.3) of FIS =-=[7,14,27,29]-=-. In the hierarchical structure of FIS it is necessary to determine the number of fuzzy rules for the first and other levels, see more in [29]. in1 in2 in3 inn FIS . fis1 in1 in2 . ink inn FIS fis1 FI... |

10 |
Fuzzy genetic algorithm and applications
- Buckley, Hayashi
- 1994
(Show Context)
Citation Context ...e number of fuzzy rules is described in [15,16,31,43,44,45,46], or the method in [13,22,42] can be used. The number of fuzzy rules can also be optimized by genetic algorithms and evolution strategies =-=[1,25]-=-. The FIS is (Fig.1) represented by a block with inputs inn and output out and can be defined as MISO (Multiple Inputs and Single Output) system. It is more described in [16,29,23,24]. in1 in2 inn . F... |

9 |
Modeling dynamical systems via the Takagi-Sugeno fuzzy model
- Mastorakis
(Show Context)
Citation Context ... of MFs of fuzzy sets) is realised during the fuzzification process. The inference mechanism is based on the operations of FL (min and max) and implication within fuzzy rules from the fuzzy rule base =-=[18,19,24,30]-=-. Transformation of the outputs of ISSN: 1790-0832 383 Issue 3, Volume 7, March 2010WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS Jiri Krupka, Petr Rudolf, Jaroslav Mencik individual rul... |

5 | General Fuzzy Systems as Extensions of the Takagi-Sugeno Methodology
- Mastorakis
- 2004
(Show Context)
Citation Context .... Nowadays, these methods are mostly based on the Monte Carlo simulation technique [17]. The experience is reasonably good, including the fatigue-life prediction for steel components or constructions =-=[19]-=-. However, the situation with large, complex and long-life structures, such as bridges, is more complicated. In addition to fatigue, there are several other causes of properties degradation, for examp... |

5 |
Using Fuzzy Sets in Operational Research
- Zimmerman
- 1983
(Show Context)
Citation Context ...nd output variables, and the fuzzy rule base) was used because an exact general method for definition of their number does not exist [16,31]. A definition of the number of fuzzy rules is described in =-=[15,16,31,43,44,45,46]-=-, or the method in [13,22,42] can be used. The number of fuzzy rules can also be optimized by genetic algorithms and evolution strategies [1,25]. The FIS is (Fig.1) represented by a block with inputs ... |

4 |
13822 Bases for design of structures - Assessment of existing structures
- ISO
- 2005
(Show Context)
Citation Context ...objects and their assessment using FL tools. General principles on reliability for various structures are presented in [8]. Bases for design of structures and assessment of existing structures are in =-=[3,9]-=-. From the point of design of new structures and the assessment of existing ones, we are interested in quantification of their reliability level. According to the current level of knowledge and degree... |

3 |
Information System Analysis Based on Rough Sets, Theses of the Dissertation
- Jirava
- 2007
(Show Context)
Citation Context ...e the number and shapes of input and output membership functions by means of genetic algorithms [22,28], and also to optimise the number of rules in FIS by means of the so-called theory of rough sets =-=[2,10,11,26]-=-. It is possible to use artificial neural networks [40], fuzzy cognitive maps [41], and neuro-fuzzy model. Acknowledgement The work was supported by the Czech Science Foundation - project GAČR 103/08/... |

3 |
Modelling of Rough-Fuzzy Classifier
- JIRAVA, KŘUPKA
- 2008
(Show Context)
Citation Context ...e the number and shapes of input and output membership functions by means of genetic algorithms [22,28], and also to optimise the number of rules in FIS by means of the so-called theory of rough sets =-=[2,10,11,26]-=-. It is possible to use artificial neural networks [40], fuzzy cognitive maps [41], and neuro-fuzzy model. Acknowledgement The work was supported by the Czech Science Foundation - project GAČR 103/08/... |

3 |
Modelling of Internal Human Population Migration Classifiers by Fuzzy Inference
- Křupka, Kašparová
(Show Context)
Citation Context ...umber of fuzzy rules in the fuzzy rule base, and the FIS can be realized ineffectively and an explanation cannot be perspicuous. This problem can be removed by a hierarchical structure (Fig.3) of FIS =-=[7,14,27,29]-=-. In the hierarchical structure of FIS it is necessary to determine the number of fuzzy rules for the first and other levels, see more in [29]. in1 in2 in3 inn FIS . fis1 in1 in2 . ink inn FIS fis1 FI... |

3 |
Analysis of Decision Processes of Automation Control System with
- Olej, Křupka
- 1996
(Show Context)
Citation Context ...self-learning, robustness and easy implementation are supported at the expense of preciseness, and they are ranged into the framework of the so called “soft computing“ or “computational intelligence” =-=[16,23,27,28,30]-=-. The goal of this paper is a verification of the use of FL for the evaluation of the technical conditions of existing bridge objects, thereby also their reliability and service life, on the bases of ... |

3 |
Prediction of gross domestic product development by Takagi-Sugeno fuzzy inference systems
- Olej, Krupka
(Show Context)
Citation Context ...nd evolution strategies [1,25]. The FIS is (Fig.1) represented by a block with inputs inn and output out and can be defined as MISO (Multiple Inputs and Single Output) system. It is more described in =-=[16,29,23,24]-=-. in1 in2 inn . FIS fis1 out Fig.1 MISO fuzzy inference system The general structure of FIS is presented in Fig.2 [13,23]. It contains processes of fuzzification, inference and defuzzification. Inputs... |

3 | Path Planning in Dynamic Environment Using Fuzzy Cognitive Maps - Vaščák, Rutrich - 2008 |

2 | Fuzzy If-Then Rules Extraction for Medical Diagnosis Using Genetic Algorithm
- Rotshtein, Posner, et al.
(Show Context)
Citation Context ... means the shape and number of membership function (MF) for input and output variables, and the fuzzy rule base) was used because an exact general method for definition of their number does not exist =-=[16,31]-=-. A definition of the number of fuzzy rules is described in [15,16,31,43,44,45,46], or the method in [13,22,42] can be used. The number of fuzzy rules can also be optimized by genetic algorithms and e... |

2 |
Reliability and the Service Life of Existing Bridges and Their Assessment Using Modern Computational Tools. A doctoral dissertation (in print
- RUDOLF
(Show Context)
Citation Context ...ct kinds, e.g. 6.1 for “loss of concrete” and 6.2 “loss of steel” for “loss of material”. In the other of the remaining levels, the defect kinds have categories and these then can have defect classes =-=[20,32,33,37]-=-. Furthermore, the bridge defects di are described as a triple by their parameters: defect extent ei, defect intensity ii and defect location by the following way: di = { defect location, ei, ii }, (1... |

2 |
Determination of Reliability of Existing Railway Bridges (in Czech). In. Vědeckotechnický sborník Českých drah
- ŠERTLER
- 1999
(Show Context)
Citation Context ...lity level. According to the current level of knowledge and degree of processing of parameters entering the process of structure evaluation, its reliability is quantified using reliability conditions =-=[37]-=-. These conditions are defined in relation to the applied method of reliability theory. According to the way of expressing the random character of reliability parameters, deterministic, semiprobabilis... |

1 |
Indiscernibility relation for continuous attributes: Application in image recognition
- Cyran, Stanczyk
- 2007
(Show Context)
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1 | Analysing of Artificial Intelligence Methods - Kašparová, Křupka - 2007 |

1 |
Olej V., Determination of Numbers of Inference Rules of Special Automation Control System
- Křupka
- 1995
(Show Context)
Citation Context ...ase) was used because an exact general method for definition of their number does not exist [16,31]. A definition of the number of fuzzy rules is described in [15,16,31,43,44,45,46], or the method in =-=[13,22,42]-=- can be used. The number of fuzzy rules can also be optimized by genetic algorithms and evolution strategies [1,25]. The FIS is (Fig.1) represented by a block with inputs inn and output out and can be... |

1 |
Guštar M., Anagnos T., Simulationbased reliability assessment for structural engineers. Boca Raton
- Marek
- 1996
(Show Context)
Citation Context ...number of measurements. For this reason, probabilistic methods for safety and lifetime predictions are sometimes used. Nowadays, these methods are mostly based on the Monte Carlo simulation technique =-=[17]-=-. The experience is reasonably good, including the fatigue-life prediction for steel components or constructions [19]. However, the situation with large, complex and long-life structures, such as brid... |

1 |
Optimisation of Maintenance Strategy in a Bridge Network
- Menčík, Rudolf
- 2005
(Show Context)
Citation Context ...ct kinds, e.g. 6.1 for “loss of concrete” and 6.2 “loss of steel” for “loss of material”. In the other of the remaining levels, the defect kinds have categories and these then can have defect classes =-=[20,32,33,37]-=-. Furthermore, the bridge defects di are described as a triple by their parameters: defect extent ei, defect intensity ii and defect location by the following way: di = { defect location, ei, ii }, (1... |

1 | Improved safe-life prediction of existing bridge structures - Menčík, Beran, et al. - 2007 |

1 |
Adaptive Fuzzy Rules Based Classification Systems
- Nozaki, Ishibuchi, et al.
- 1996
(Show Context)
Citation Context ...ase) was used because an exact general method for definition of their number does not exist [16,31]. A definition of the number of fuzzy rules is described in [15,16,31,43,44,45,46], or the method in =-=[13,22,42]-=- can be used. The number of fuzzy rules can also be optimized by genetic algorithms and evolution strategies [1,25]. The FIS is (Fig.1) represented by a block with inputs inn and output out and can be... |

1 |
Fuzzy Control and Fuzzy Systems. 2nd edn
- Pedrycz
- 1993
(Show Context)
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1 |
Artificial inteligence in modelling and managamenent
- Pokorný
- 1996
(Show Context)
Citation Context ...the law of empirical probability, using the knowledge of distribution of probability of random events, the methods for work with uncertainty come from the so-called law of distribution of possibility =-=[28]-=-. The quality of human judgement is characterized by the ability of effective processing of not very precise information. This capability, together with the other qualities of human reasoning, becomes... |

1 | Bridge Condition Evaluation Using Fuzzy Logic
- Rudolf
(Show Context)
Citation Context ...ct kinds, e.g. 6.1 for “loss of concrete” and 6.2 “loss of steel” for “loss of material”. In the other of the remaining levels, the defect kinds have categories and these then can have defect classes =-=[20,32,33,37]-=-. Furthermore, the bridge defects di are described as a triple by their parameters: defect extent ei, defect intensity ii and defect location by the following way: di = { defect location, ei, ii }, (1... |

1 |
S 5 Administration of bridge objects. Service instructions (republicated). Praha: Ministerstvo dopravy ČR
- SŽDC
- 1995
(Show Context)
Citation Context ...bility assessment of the bridge is the evaluation of its condition, which in practical judging data is, however, often incomplete, numerically imprecise and also linguistic. A supervising activity in =-=[39]-=- consists of general (annual) and detailed (three yearly) inspections namely. The protocol about a detailed bridge inspection quotes the found faults and the proposal of total condition classification... |

1 |
The application of neural networks for detection and identification of fault conditions
- Tkáč, Chovanec
(Show Context)
Citation Context ...s by means of genetic algorithms [22,28], and also to optimise the number of rules in FIS by means of the so-called theory of rough sets [2,10,11,26]. It is possible to use artificial neural networks =-=[40]-=-, fuzzy cognitive maps [41], and neuro-fuzzy model. Acknowledgement The work was supported by the Czech Science Foundation - project GAČR 103/08/1340 ‘Fatigue endurance of steel orthotropic bridge dec... |

1 |
Design Fuzzy Controllers for Complex Systems
- Zhang, Ma, et al.
- 1993
(Show Context)
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