Integration of Fuzzy Knowledge

Author(s):  
Tzung-Pei Hong ◽  
Ching-Hung Wang ◽  
Shian-Shyong Tseng
Keyword(s):  
2018 ◽  
Vol 8 (6) ◽  
pp. 864 ◽  
Author(s):  
Murat Luy ◽  
Volkan Ates ◽  
Necaattin Barisci ◽  
Huseyin Polat ◽  
Ertugrul Cam

2013 ◽  
Vol 12 (1) ◽  
pp. 069-076
Author(s):  
Janusz Szelka ◽  
Zbigniew Wrona

The IT tools that are widely used for aiding information and decision-making tasks in engineering activities include classic database systems, and in the case of problems with poorly-recognised structure – systems with knowledge bases. The uniqueness of these categories of systems allows, however, neither to represent the approximate or imprecise nature of available data or knowledge nor to process fuzzy data. Since so far there have been no solutions related to the use of fuzzy databases or fuzzy knowledge bases in engineering projects, it seems necessary to make an attempt to assess the possible employment of these technologies to aid analytical and decision-making processes.


Author(s):  
Felisa M. Cordova ◽  
Guillermo Leyton

This paper presents the design of a fuzzy control heuristic that can be applied for modeling nonlinear dynamic systems using a fuzzy knowledge representation. Nonlinear dynamic systems have been modeled traditionally on the basis of connections between the subsystems that compose it. Nevertheless, this model design does not consider some of the following problems: existing dynamics between the subsystems; order and priority of the connection between subsystems; degrees of influence or causality between subsystems; particular state of each subsystem and state of the system on the basis of the combination of the diverse states of the subsystems; positive or negative influences between subsystems. In this context, the main objective of this proposal is to manage the whole system state by managing the state combination of the subsystems involved. In the proposed design the diverse states of subsystems at different levels are represented by a knowledge base matrix of fuzzy intervals (KBMFI). This type of structure is a fuzzy hypercube that provides facilities operations like: insert, delete, and switching. It also allows Boolean operations between different KBMFI and inferences. Each subsystem in a specific level and its connectors are characterized by factors with fuzzy attributes represented by membership functions. Existing measures the degree of influence among the different levels are obtained (negatives, positives). In addition, the system state is determined based on the combination of the statements of the subsystems (stable, oscillatory, attractor, chaos). It allows introducing the dynamic effects in the calculation of each output level. The control and search of knowledge patterns are made by means of a fuzzy control heuristic. Finally, an application to the co-ordination of the activities among different levels of the operation of an underground mine is developed and discussed.


1995 ◽  
Vol 45 (2) ◽  
pp. 135-143 ◽  
Author(s):  
Terhi Siimes ◽  
Pekka Linko ◽  
Camilla von Numers ◽  
Mikio Nakajima ◽  
Isao Endo

2018 ◽  
Vol 44 (4) ◽  
pp. 3061-3082
Author(s):  
Soumaya Moussa ◽  
Saoussen Bel Hadj Kacem
Keyword(s):  

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