| 1. |
- Erman, Maria, et al.
(författare)
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Fuzzy Logic Applications in Wireless Communications
- 2009
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Konferensbidrag (refereegranskat)abstract
- A survey of fuzzy logic applications and principles in wireless communications is presented, with the aim of highlighting successful usage of fuzzy logic techniques in applied telecommunications and signal processing. To the best of our knowledge, this is the first such study of its kind. This paper will focus firstly on discerning prevalent fuzzy logic or fuzzy-hybrid approaches in the areas of channel estimation, channel equalization and decoding, and secondly outlining what conditions and situations for which fuzzy logic techniques are most suited for these approaches. Furthermore, after insights gained from isolating fuzzy logic techniques applied to real problems, this paper proposes areas for further research targeted to practice-oriented researchers.
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| 2. |
- Isaksson, Lennart, et al.
(författare)
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A Fuzzy Set Theory Based Method to Discover Transmissions in Wireless Personal Area Networks
- 2006
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Konferensbidrag (refereegranskat)abstract
- The complexity within a Wireless Personal Area Network (WPAN) increases. Several technologies have to share the same radio spectrum. In this paper we take a look at the 2.4 GHz Industrial Scientific and Medical (ISM)-band. This paper discusses a method of selecting the best wireless channel within Wireless Local Area Network (WLAN) when several technologies could be used in the same WPAN range of the needed access point. The issue is to keep away from already occupied channels. The method is divided into four steps: the passive probing of the power level detecting IEEE 802.15.4 (ZigBee) channels using a new and affordable hardware, the transformation of the probed data to a linguistic level using Fuzzy Set Theory (FST), the classification of the data, and finally the sorting and selection of channels based on whose power levels.
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| 3. |
- Kurach, Damian, et al.
(författare)
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Face Classification Based on Linguistic Description of Facial Features
- 2014
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Konferensbidrag (refereegranskat)abstract
- This paper presents an Artificial Intelligence approach towards classification of persons based on a verbal description of their facial features. Face features are extracted by use of an existing detection techniques, such as measurements of horizontal and vertical size of the facial elements like nose, eyes, etc. This approach allows to create fuzzy sets representing selected facial features. What is important, linguistic variables corresponding to the fuzzy sets conform the terminology applied by law enforcement to create an eyewitness verbal description. Then, fuzzy IF-THEN rules are employed for classification both the facial composites (sketches) and usual images from face databases. With regard to this concept, satisfactory results have been obtained and presented.
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| 4. |
- Liu, Yang, et al.
(författare)
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A Fuzzy-Rough Sets Based Compact Rule Induction Method for Classifying Hybrid Data
- 2012
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Konferensbidrag (refereegranskat)abstract
- Rule induction plays an important role in knowledge discovery process. Rough set based rule induction algorithms are characterized by excellent accuracy, but they lack the abilities to deal with hybrid attributes such as numeric or fuzzy attributes. In real-world applications, data usually exists with hybrid formats, and thus a unified rule induction algorithm for hybrid data learning is desirable. We firstly model different types of attributes in equivalence relationship, and define the key concepts of block, minimal complex and local covering based on fuzzy rough sets model, then propose a rule induction algorithm for hybrid data learning. Furthermore, in order to estimate performance of the proposed method, we compare it with state-of-the-art methods for hybrid data learning. Comparative studies indicate that rule sets extracted by this method can not only achieve comparable accuracy, but also get more compact rule sets. It is therefore concluded that the proposed method is effective for hybrid data learning.
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| 5. |
- Liu, Yang, et al.
(författare)
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Rough Sets Based Inequality Rule Learner for Knowledge Discovery
- 2012
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Konferensbidrag (refereegranskat)abstract
- Traditional rule learners employ equality relations between attributes and values to express decision rules. However, inequality relationships, as supplementary relations to equation, can make up a new function for complex knowledge acquisition. We firstly discuss an extended compensatory model of decision table, and examine how it can simultaneously express both equality and inequality relationships of attributes and values. In order to cope with large-scale compensatory decision table, we propose a scalable inequality rule leaner, which initially compresses the input spaces of attribute value pairs. Example and experimental results show that the proposed learner can generate compact rule sets that maintain higher classification accuracies than equality rule learners.
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| 6. |
- Mustafa, Wail, et al.
(författare)
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Fuzzy-based Opportunistic Power Control Strategy in Cognitive Radio Networks
- 2010
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Konferensbidrag (refereegranskat)abstract
- This paper considers the spectrum sharing network consisting of a pair of primary users (PUs) and a pair of cognitive users (CRs) in a fading channel. The pair of PUs establishes a wireless link as the PU link. The pair of CRs establishes a wireless link as the CR link. The PU link and CR link utilize spectrum simultaneously with different priorities. The PU link has a higher priority to utilize spectrum with respect to the CR link. When the PU link utilizes spectrum, a desired quality of service (QoS) is given to be assured and the CR utilizes spectrum with an opportunistic power scale under this constraint, assuring the desired QoS on the PU link. To compute an optimal opportunistic power scale for the CR link, a fuzzy-based opportunistic power control strategy is proposed based on the Mamdani fuzzy control model using two input variables: the PU’s SNR and PU’s interference channel gain. By the proposed fuzzy-based power control strategy, the desired QoS could be assured on the PU link and the bit error rate (BER) is also reduced compared with the spectrum sharing network without power control strategy.
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| 7. |
- Rakus-Andersson, Elisabeth
(författare)
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A Choice of Optimal Medicines by Fuzzy Decision Making Including Unequal Objectives
- 2005
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Ingår i: Issues in the Representation and Processing of Uncertain and Imprecise Information. - Warsaw : "EXIT" - The Publishing House of Polish Academy of Sciences. - 8360434018 ; , s. 307-321
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Some researches have already developed fuzzy decision making theoretical algorithms, but there are only a few medical fields of their applications. We thus make a trial of adopting the Yager decision making algorithm to extract the optimal medicine from a collection of drugs that can be given to a patient to cure him of an illness. The choice of the most efficacious remedy is based on clinical symptoms typical of the considered morbid unit.
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| 8. |
- Rakus-Andersson, Elisabeth
(författare)
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A Comparison of Fuzzy Decision Making Models Supporting the Optimal Therapy
- 2000
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Ingår i: Fuzzy Systems in Medicine. - Heidelberg-New York : Physica-verlag – A Springer-verlag Company. - 3790812633 ; , s. 561-572
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- Fuzzy set theory offers numerous methods that prove helpful in solving medical problems. They have already been successfully used for instance to fix the optimal level of drug action in patients revealing no clinical symptoms after treatment. In many morbid processes, however, although indices of measurable symptoms improve after the course of medication, the symptoms themselves do not retreat entirely. The authors have already proposed different fuzzy techniques involved in the solution of the problem described above. This time the suggestion of comparing three fuzzy decision making models aim at facilitation of the optimal drug choice in the case of symptoms that prevail after treatment.
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| 9. |
- Rakus-Andersson, Elisabeth
(författare)
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A Diagnostic Process Extended in Time as a Fuzzy Model
- 1999
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Ingår i: Computing Anticipatory Systems. - New York : American Institute of Physics, Woodbury, New York. - 1563968630 ; , s. 283-288
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The paper refers to earlier results obtained by the authors and constitutes their essential complement and extension by introducing to a diagnostic model the assumption that the decision concerning the diagnosis is based on observations of symptoms carried out repeatedly, by stages, which may have effect in a change of these symptoms in increasing time. The model concerns the observations of symptoms at an individual patient at a time interval. The changes of the symptoms give some additional information, sometimes very important in the diagnostic process when the clinical picture of a patient in a certain interval of time differs from that one which has been received from the beginning of the disease. It may occur that the change in the intensity of a symptom decides an acceptance of another diagnosis after some time when the patient does not feel better. The aim is to fix an optimal diagnosis on the basis of clinical symptoms typical of several morbid units with respect to the changes of these symptoms in time. In order to solve such a posed problem the authors apply the method of fuzzy relation equations, which are modelled by means of logical laws and the rules of inference. Moreover, in the final decision concerning the choice of a proper diagnosis, a normalized Euclidean distance is introduced as a measure between a real decision and an ”ideal” decision. A simple example presents the practical action of the method to show its relevance to a possible user.
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| 10. |
- Rakus-Andersson, Elisabeth
(författare)
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A Fuzzy Decision Making Model Applied to the Choice of the Therapy in the Case of Symptoms not Disappearing after the Treatment
- 1999
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Konferensbidrag (refereegranskat)abstract
- Fuzzy set theory has used many auxiliary methods into the trials of solutions of some medical problems. One of the attempts was the evaluation of the optimal level of the drug action in the case when the clinical symptoms disappeared completely after the treatment. However, there can occur such a morbid process in which the symptoms prevail after the treatment. The medication improves too high or too low level of the quantitative symptom but it still indicates the presence of the illness. It is not so easy to choose the medicine, which acts best because it can happen that most of them influence the same symptoms, while they do not improve the others. A fuzzy decision making model tries to make easier to find such a drug which affects most of the symptoms in the highest degree. In the next attempt of solving the problem we propose the using of discrete membership functions in the model instead of the continuous ones. It should improve the thoroughness of the method and heighten the reliability of the accepted decision.
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