| 1. |
- Doherty, Patrick, 1957-, et al.
(author)
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Fuzzy if-then-unless rules and their implementation
- 1993
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In: International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. - : World Scientific. - 0218-4885. ; 1:2, s. 167-182
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Journal article (peer-reviewed)abstract
- We consider the possibility of generalizing the notion of a fuzzy If-Then rule to take into account its context dependent nature. We interpret fuzzy rules as modeling a forward directed causal relationship between the antecedent and the conclusion, which applies in most contexts, but on occasion breaks down in exceptional contexts. The default nature of the rule is modeled by augmenting the original If-Then rule with an exception part. We then consider the proper semantic correlate to such an addition and propose a ternary relation which satisfies a number of intuitive constraints described in terms of a number of inference rules. In the rest of the paper, we consider implementational issues arising from the unless extension and propose the use of reason maintenance systems, in particular TMS's, where a fuzzy If-Then-Unless rule is encoded into a dependency net. We verify that the net satisfies the constraints stated in the inference schemes and conclude with a discussion concerning the integration of qualitative IN-OUT labelings of the TMS with quantitative degree of membership labelings for the variables in question.
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| 2. |
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| 3. |
- Driankov, Dimiter, 1952-, et al.
(author)
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Chaining of fuzzy IF-THEN rules in Mamdani-controllers
- 1995
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In: Proceedings of 1995 IEEE International conference on fuzzy systems, Vols I-IV.. - : IEEE conference proceedings. - 0780324617 ; , s. 103-108
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Conference paper (peer-reviewed)abstract
- The problem of chaining of fuzzy IF-THEN rules has so far received a rather theoretic treatment in the literature on approximate reasoning. In particular, different types of composition operators, fuzzy implication operators, etc., have been identified such that the conclusion obtained via a chain of fuzzy rules coincides with the conclusion derived from the “abbreviated” version of the same chain. This “abbreviated” version is a single fuzzy rule which the rule-antecedent is the rule-antecedent of the first rule in the chain, and its rule-consequent is the rule-consequent of the last rule in the chain. However, in the case of more than one chain of rules and when the fuzzy sets defining the meaning of the rule-antecedents and rule-consequents from different chains overlap, then the above theoretical results do not hold in general. In the present paper we identify two major problems with the chaining of fuzzy rules in the case of more than one chain and overlapping rule-antecedents and rule-consequents that belong to different chains
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| 4. |
- Driankov, Dimiter, 1952-, et al.
(author)
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Fuzzy control with fuzzy inputs: the need for new rule semantics
- 1994
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In: Proceedings of the Third IEEE Conference on Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence, VOLS I-III. - : IEEE conference proceedings. - 078031896X ; , s. 111-114
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Conference paper (peer-reviewed)abstract
- The standard computation taking place in a fuzzy logic controller proceeds from crisp inputs and via the consecutive steps of fuzzification, inference, and defuzzification computes a crisp control output. However, this computational practice simplifies to an extent the actual developments taking place in the closed loop. In reality, the knowledge about the current values of the controller input is very often available via sensory measurements. In this case, one has to take into account the negative side effects that come up with the use of sensors, in particular the presence of noisy measurements. In the paper the authors consider one particular way of dealing with noisy controller inputs, namely transforming the noise-distribution into a fuzzy set and then feeding back the so obtained fuzzy signal to the controller input. Adopting this approach requires that the shape of the input fuzzy signal should be reflected as much as possible in the output fuzzy signal so that important noise characteristics are preserved. In the paper the authors describe the requirements on the shape of the fuzzy output signal given a certain fuzzy input signal and show that the existing semantics for fuzzy IF-THEN rules do not satisfy these requirements. The authors propose new semantics for such rules which together with max-min composition produces the desired results.
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| 5. |
- Driankov, Dimiter, 1952-, et al.
(author)
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Fuzzy logic with unless-rules
- 1992
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In: IEEE International Conference on Fuzzy Systems. - New York, USA : IEEE conference proceedings. - 0780302362 ; , s. 255-262
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Conference paper (peer-reviewed)abstract
- Unless-rules are intended to deal with problems of reasoning with incomplete information and/or resource constraints. An unless-rule is proposed to be of the form `if X is A then Y is B unless Z is C'. Such rules are employed in situations in which the conditional statement if X is A then Y is B usually holds and the assertion Z is C holds rarely. Thus, using a rule of this type the exception condition can be ignored when the resources needed to establish its presence are tight or there simply is no information available as to whether it holds or does not hold. In this case of incomplete information, since it is the case that if X is A then Y is B usually holds, one may be willing to jump to the conclusion Y is B given that X is A because no information as to whether Z is C holds is available
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| 6. |
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Fuzzy model identification : Selected approaches
- 1997
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Editorial collection (peer-reviewed)abstract
- This carefully edited volume presents a collection of recent works in fuzzy model identification. It opens the field of fuzzy identification to conventional control theorists as a complement to existing approaches, provides practicing control engineers with the algorithmic and practical aspects of a set of new identification techniques, and emphasizes opportunities for a more systematic and coherent theory of fuzzy identification by bringing together methods based on different techniques but aiming at the identification of the same types of fuzzy models. In control engineering, mathematical models are often constructed, for example based on differential or difference equations or derived from physical laws without using system data (white-box models) or using data but no insight (black-box models). In this volume the authors choose a combination of these models from types of structures that are known to be flexible and successful in applications. They consider Mamdani, Takagi-Sugeno, and singleton models, employing such identification methods as clustering, neural networks, genetic algorithms, and classical learning. All authors use the same notation and terminology, and each describes the model to be identified and the identification technique with algorithms that will help the reader to apply the presented methods in his or her own environment to solve real-world problems. Furthermore, each author gives a practical example to show how the presented method works, and deals with the issues of prior knowledge, model complexity, robustness of the identification method, and real-world applications.
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| 7. |
- Palm, Rainer, et al.
(author)
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Model Based Fuzzy Control Fuzzy Gain Schedulers and Sliding Mode Fuzzy Controllers : Fuzzy Gain Schedulers and Sliding Mode Fuzzy Controllers
- 1997
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Book (peer-reviewed)abstract
- Model Based Fuzzy Control uses a given conventional or fuzzy open loop model of the plant under control to derive the set of fuzzy rules for the fuzzy controller. Of central interest are the stability, performance, and robustness of the resulting closed loop system. The major objective of model based fuzzy control is to use the full range of linear and nonlinear design and analysis methods to design such fuzzy controllers with better stability, performance, and robustness properties than non-fuzzy controllers designed using the same techniques. This objective has already been achieved for fuzzy sliding mode controllers and fuzzy gain schedulers - the main topics of this book. The primary aim of the book is to serve as a guide for the practitioner and to provide introductory material for courses in control theory.
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