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| 2. |
- Cordon, O, et al.
(författare)
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Ten years of genetic fuzzy systems : current framework and new trends
- 2004
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Ingår i: Fuzzy sets and systems (Print). - 0165-0114 .- 1872-6801. ; 141:1, s. 5-31
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Forskningsöversikt (refereegranskat)abstract
- Fuzzy systems have demonstrated their ability to solve different kinds of problems in various application domains. Currently, there is an increasing interest to augment fuzzy systems with learning and adaptation capabilities. Two of the most successful approaches to hybridise fuzzy systems with learning and adaptation methods have been made in the realm of soft computing. Neural fuzzy systems and genetic fuzzy systems hybridise the approximate reasoning method of fuzzy systems with the learning capabilities of neural networks and evolutionary algorithms. The objective of this paper is to provide an account of genetic fuzzy systems, with special attention to genetic fuzzy rule-based systems. After a brief introduction to models and applications of genetic fuzzy systems, the field is overviewed, new trends are identified, a critical evaluation of genetic fuzzy systems for fuzzy knowledge extraction is elaborated, and open questions that remain to be addressed in the future are raised. The paper also includes some of the key references required to quickly access implementation details of genetic fuzzy systems.
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| 3. |
- Driankov, Dimiter, 1952-
(författare)
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An outline of a fuzzy sets approach to decision making with interdependent goals
- 1987
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Ingår i: Fuzzy sets and systems (Print). - : Elsevier. - 0165-0114 .- 1872-6801. ; 21:3, s. 275-288
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Tidskriftsartikel (refereegranskat)abstract
- The aim of the present paper is to outline a formal framework for dealing with the problem of decision making with multiple interdependent goals. The approach uses the idea of aspiration levels in order to bridge the gap between the prescriptive and the descriptive approaches thus allowing the problems of evaluation, choice and generation of alternatives to be treated in a coherent formal framework. On the other hand the approach recognizes the existance of interdependent and very often conflicting goals and suggests that in the case when the relationships between the goals can not be modelled by means of mathematical equations one should employ some techniques for knowledge representation based on fuzzy production rules and basic concepts from the theory of approximate reasoning.
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| 4. |
- Eklund, Patrik, 1958-, et al.
(författare)
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Basic notions for fuzzy topology, I
- 1988
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Ingår i: Fuzzy sets and systems (Print). - : Elsevier BV. - 0165-0114 .- 1872-6801. ; 26:3, s. 333-356
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Tidskriftsartikel (refereegranskat)
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| 5. |
- Eklund, Patrik, 1958-, et al.
(författare)
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Basic notions for fuzzy topology, II
- 1988
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Ingår i: Fuzzy sets and systems (Print). - : Elsevier BV. - 0165-0114 .- 1872-6801. ; 27:2, s. 171-195
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Tidskriftsartikel (refereegranskat)
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| 6. |
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| 7. |
- Eklund, Patrik, 1958-, et al.
(författare)
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Comparison of learning strategies for adaptation of fuzzy controller parameters
- 1999
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Ingår i: Fuzzy sets and systems (Print). - : Elsevier. - 0165-0114 .- 1872-6801. ; 106:3, s. 321-333
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Tidskriftsartikel (refereegranskat)abstract
- For tuning fuzzy controllers, several parameter identification techniques are available, ranging from more robust descent methods to sophisticated optimisation. However, from an application point of view, it is not always clear that numerical sophistication wins over more pragmatic approaches to tuning. Obviously, the data sets play crucial roles in efforts to reach successful tuning. Especially data sets generated from real processes often contain not only noisy data and conflicting subsets, but also the connected problem of non-covering input spaces. In this paper we will compare several parameter identification techniques w.r.t. different data sets. We focus on selections of learning rates and on defining training sequences related to subclasses of parameters.
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| 8. |
- Eklund, Patrik, 1958-, et al.
(författare)
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Rule generation as an alternative to knowledge acquisition : a systems architecture for medical informatics
- 1994
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Ingår i: Fuzzy sets and systems (Print). - : Elsevier. - 0165-0114 .- 1872-6801. ; 66:2, s. 195-205
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Tidskriftsartikel (refereegranskat)abstract
- This paper describes a clinical support systems workbench, DiagaiD, based on an efficient transfer of patient data between health care professionals and clinical subsystems. The DiagaiD workbench provides tools for decision support developments for open-loop systems.A leading ambition for our development work has been to establish a data analysis and knowledge elicitation workbench, in which medical professionals entirely by themselves can create stand-alone expert systems.
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| 9. |
- Hoffmann, Frank
(författare)
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Combining boosting and evolutionary algorithms for learning of fuzzy classification rules
- 2004
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Ingår i: Fuzzy sets and systems (Print). - 0165-0114 .- 1872-6801. ; 141:1, s. 47-58
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a novel boosting algorithm for genetic learning of fuzzy classification rules. The method is based on the iterative rule learning approach to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The boosting mechanism reduces the weight of those training instances that are classified correctly by the new rule. Therefore, the next rule generation cycle focuses on fuzzy rules that account for the currently uncovered or misclassified instances. The weight of a fuzzy rule reflects the relative strength the boosting algorithm assigns to the rule class when it aggregates the casted votes. The approach is compared with other classification algorithms for a number problem sets from the UCI repository.
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| 10. |
- Palm, R., et al.
(författare)
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Design of a fuzzy gain scheduler using sliding mode control principles
- 2001
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Ingår i: Fuzzy sets and systems (Print). - 0165-0114 .- 1872-6801. ; 121:1, s. 13-23
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Tidskriftsartikel (refereegranskat)abstract
- Fuzzy gain schedulers are designed on the basis of a conventional modeling of the nonlinear controlled system and the division of the state space into a finite number of fuzzy regions. Linearization of the nonlinear system at the center of each fuzzy region leads to the design of a set of linear control laws that locally stabilize the linearized system, and consequently the original nonlinear system at the corresponding operating point. Gain scheduling control of the original nonlinear system can be therefore realized along an a priori unknown, but slowly time varying desired trajectory. In this paper we analyze the stability and robustness of the gain-scheduled closed-loop system by adopting ideas from sliding mode control. It is shown that gain scheduling control of the original nonlinear system can be realized along an a priori unknown, but slowly time-varying desired trajectory. It is shown how the advantages of the sliding mode types of analysis of a fuzzy gain scheduler can also be used for its design. © 2001 Elsevier Science B.V.
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