3 edition of How to combine probabilistic and fuzzy uncertainties in fuzzy control found in the catalog.
How to combine probabilistic and fuzzy uncertainties in fuzzy control
by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va
Written in English
|Statement||Hung T. Nguyen, Vladik Kreinovich, Bob Lea.|
|Series||[NASA technical memorandum] -- NASA-TM-108746., NASA technical memorandum -- 108746.|
|Contributions||Kreinovich, Vladik., Lea, Bob., United States. National Aeronautics and Space Administration.|
|The Physical Object|
Fuzzy Logic and Probability Applications: Bridging the Gap makes an honest effort to show both the shortcomings and benefits of each technique, and even demonstrates useful combinations of the two. It provides clear descriptions of both fuzzy logic and probability, as well as the theoretical background, examples, and applications from both. to T of concepts and techniques drawn from fuzzy logic. Examples: fuzzy control, fuzzy linear programming, fuzzy probability theory and fuzzy topology. 2. Linguistic variables and fuzzy if–then rules. The formalism of linguistic variables and fuzzy if–then rules is, in eﬀect, a.
Uncertainty comes in many guises and is independent of the kind of fuzzy logic (FL), or any kind of methodology, one uses to handle it. One of the best sources for general discussions about uncertainty is the book Uncertainty-Based Information, by Klir and Wierman 1. Fuzzy and/or qualitative information regarding the decision-making process for which quantitative data is not available are evaluated in linguistic terms. Afterwards, Monte Carlo simulation is applied to combine these information and to generate a probabilistic decision matrix of management alternatives versus criteria in an uncertain by:
As far as aggregation goes, the measures of fuzziness and entropic measures of probabilistic uncertainty can sometimes be summed together to give total measures of uncertainty. To add another level of complexity. fuzzy logic, numbers and sets can all be aggregated, which can affect the amount of resulting uncertainty. The next issue as the fourth problem is combining fuzzy and stochastic uncertainties in a single task. Based on the above assumptions on criteria and as well as relative significances of criteria (or), there is the need to combine fuzzy and probabilistic representation of uncertainties in one initial decision-making matrix. Next comes the Cited by:
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In whatever fuzzy, natural-language terms they can, and then apply fuzzy control methodology (see, e.g., [1, 2, 4]) to translate these rules into the actual control strategy.
Both methodologies work fine. If we have enough rules, then we can apply fuzzy control. If we have a. In order to choose a procedure that transforms a fuzzy knowledge into a control, one needs, first, to choose a membership function for each of the fuzzy terms that the experts use, second, to.
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Traditional and fuzzy control correspond to two different cases: traditional control is applicable when we have a precise knowledge of the controlled system, and possible uncertainties are described in probabilistic terms (as a noise).
Fuzzy control (see, e.g., [S85, B91, L90]) is applicable in the situation when. COVID Resources. Reliable information about the coronavirus (COVID) is available from the World Health Organization (current situation, international travel).Numerous and frequently-updated resource results are available from this ’s WebJunction has pulled together information and resources to assist library staff as they consider how to handle coronavirus.
Traditional and fuzzy control correspond to two different cases: traditional control is applicable when we have a precise knowledge of the controlled system, and possible uncertainties are described in probabilistic terms (as a noise).
Fuzzy control (see, e.g., [S85, B91, L90]) is applicable in the situation when uncertainty is described in Author: Hung T. Nguyen, Vladik Kreinovich and Bob Lea. Abstract. Traditional and fuzzy control correspond to two different cases: traditional control is applicable when we have a precise knowledge of the controlled system, and possible uncertainties are described in probabilistic terms (as a noise).
Fuzzy set models in the treatment of some uncertainties in safety assessment complement probabilistic models and can be readily incorporated into the current analysis procedure.
This chapter shows that fuzzy set models can be used to treat various kinds of uncertainties and to complement probabilistic models in the safety assessment of structures.
The new FIER algorithm under both interval probabilistic and fuzzy uncertainties Based on the fuzzy assessment set HF, a FIER (Fuzzy Interval grade ER) recursive algorithm is developed as follows using the similar technique used in Yang and Singh and Yang, et al (the detailed proof is shown in the Appendix).
mH mH%Cited by: Fuzzy-representations Fuzzy and probability Uncertainty is closely connected to probability, which (directly or indirectly) constitutes the formal framework for machine learning and neural network mod-els.
Specifying the probability of an event also gives information about. Nguyen, H.T., Kreinovich, V., Lea, B.: How to combine probabilistic and fuzzy uncertainties in fuzzy control. In: Proceedings of the Second International Workshop on Industrial Applications of Fuzzy Control and Intelligent Systems, College Cited by: 7.
Fuzzy control is a methodology that translates natural-language rules, formulated by expert controllers, into the actual control strategy that can be implemented in an automated controller. The equivalent triangular fuzzy sets are determined using the different approaches discussed in Section 3, and are given in Table 1.
(The details regarding the optimisation procedure adopted are given in Appendix A.)The probabilistic fuzzy sets and the equivalent triangular fuzzy sets obtained for lognormal and gamma distributions using the different approaches are shown in Fig. 3, Fig.
by: Index Terms— Fuzzy time control, grinding and mixing system, industrial application and time control systems. INTRODUCTION The fuzzy logic and fuzzy set theory deal with non-probabilistic uncertainties issues. The fuzzy control system is based on the theory of fuzzy sets and fuzzy logic.
We discuss the use of fuzzy set theory and semantic unification for fuzzy clustering and the use of fuzzy rules in knowlege bases. The paper provides a unification with probability theory and probabilistic fuzzy rules are discussed.
Fuzzy sets are used to provide generalisation in clustering and pattern recognition by: Uncertainties due to randomness and fuzziness comprehensively exist in control and decision support systems.
In the present study, we introduce notion of occurring probability of possible values into hesitant fuzzy linguistic element (HFLE) and define hesitant probabilistic fuzzy linguistic set (HPFLS) for ill structured and complex decision making by: 9.
By fuzzy set theory and fuzzy logic, we can not only represent such knowledge, but also utilize it to its full extent, taking the kind and the form of the uncertainties into account.
This does not mean than fuzzy logic renders classical logic and probability theory obsolete. On the contrary, though fuzzy sets and fuzzy logic extend membershipFile Size: 72KB.
Probabilistic record linkage, also called fuzzy matching, takes a different approach to the record linkage problem. It takes a wider range of potential identifiers into accounts, and computes weights for each of them based on its estimated ability to correctly identify a match or a non-match.
During the past several years, fuzzy control has emerged as one of the most active and fruitful areas for research in the applications of fuzzy set theory, especially in the realm of industrial processes which do not lead them selves to control by conventional methods because of a lack of quantitative data regarding the input-output relations.
Fuzzy control is based on fuzzy logic a logical. What Is Fuzzy Probability Theory. Gudder1 Received March 4, ; revised July 6, The article begins with a discussion of sets and fuzzy sets. It is observed that iden-tifying a set with its indicator function makes it clear that a fuzzy set is a direct and natural generalization of a set.
Making this identification also provides sim. 1 Introduction. In power system expansion planning problem, determination of an optimal generation mix and transmission upgrades have a vital role in economic and secure operation of system .In order to keep the security of the power system, usually a large portion of the total capacity of generating units has been formed by steam and gas units , which is inconsistent with the Kyoto Cited by: 4.
It introduces the reader to the topic of rough sets. This book's companion volume, Volume 2: Fuzzy Reasoning and Fuzzy Control, will move forward from here to discuss several advanced features of soft computing and application methodologies.
Discusses the present state of art of soft computin. Includes the existing.Most remarkable area of application is 'fuzzy control', where fuzzy logic was first applied to plant control systems and its use is expanding to consumer products.
Most of fuzzy control systems uses fuzzy inference with max-min or max-product composition, similar to the algorithm that first used by Mamdani in s.fuzzy logic and probability theory, here we propose a fuzzy logic of probability for which completeness results (in a probabilistic sense) are provided.
The main idea behind this approach is that probability values of crisp propositions can be understood as truth values of some suitable fuzzy propositions as sociated to the crisp : Petr Hajek, Lluis Godo, Francesc Esteva.