scholarly journals Fuzzy Knowledge Representation Using Probability Measures of Fuzzy Events

10.5772/6113 ◽  
2008 ◽  
Author(s):  
Anna Walaszek-Babiszewska
Keyword(s):  
Knowledge Representation ◽  
Probability Measures ◽  
Fuzzy Knowledge

scholarly journals A Fuzzy Control Heuristic Applied to Non-linear Dynamic System Using a Fuzzy Knowledge Representation

2010 ◽  
Vol 5 (5) ◽  
pp. 664 ◽  
Author(s):  
Felisa M. Cordova ◽  
Guillermo Leyton
Keyword(s):  
Knowledge Representation ◽  
Fuzzy Control ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Systems ◽  
System State ◽  
Specific Level ◽  
Base Matrix ◽  
Different Levels ◽  
Fuzzy Knowledge

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.

Fuzzy Knowledge Representation for Leukemia Diagnosis in Children Oncology

1990 ◽  
pp. 22-34
Author(s):  
A. Barreiro ◽  
J. Mira ◽  
R. Marín ◽  
A. E. Delgado ◽  
J. M. Couselo
Keyword(s):  
Knowledge Representation ◽  
Leukemia Diagnosis ◽  
Fuzzy Knowledge

scholarly journals Fuzzy Membrane Computing: Theory and Applications

2015 ◽  
Vol 10 (6) ◽  
pp. 144 ◽  
Author(s):  
Tao Wang ◽  
Gexiang Zhang ◽  
Mario J. Pérez-Jiménez
Keyword(s):  
Knowledge Representation ◽  
Membrane Computing ◽  
Fuzzy Theory ◽  
Research Direction ◽  
P Systems ◽  
Future Research ◽  
Knowledge Representation And Reasoning ◽  
Spiking Neural P Systems ◽  
Comprehensive Survey ◽  
Fuzzy Knowledge

<p>Fuzzy membrane computing is a newly developed and promising research direction in the area of membrane computing that aims at exploring the complex in- teraction between membrane computing and fuzzy theory. This paper provides a comprehensive survey of theoretical developments and various applications of fuzzy membrane computing, and sketches future research lines. The theoretical develop- ments are reviewed from the aspects of uncertainty processing in P systems, fuzzifica- tion of P systems and fuzzy knowledge representation and reasoning. The applications of fuzzy membrane computing are mainly focused on fuzzy knowledge representation and fault diagnosis. An overview of different types of fuzzy P systems, differences between spiking neural P systems and fuzzy reasoning spiking neural P systems and newly obtained results on these P systems are presented.</p>

Fuzzy Knowledge Representation Based on Fuzzy Linguistic Variable Ontology and SWRL on the Semantic Web

2011 ◽  
Vol 58-60 ◽  
pp. 1707-1711
Author(s):  
Yan Ling Li ◽  
Yi Duo Liang ◽  
Jun Zhai
Keyword(s):  
Semantic Web ◽  
Knowledge Representation ◽  
Web Applications ◽  
Fuzzy Rule ◽  
Linguistic Variables ◽  
Semantic Relationships ◽  
Rule Management ◽  
Fuzzy Linguistic Variable ◽  
Fuzzy Linguistic ◽  
Fuzzy Knowledge

Ontology is adopted as a standard for knowledge representation on the Semantic Web, and Ontology Web Language (OWL) is used to add structure and meaning to web applications. In order to share and resue the fuzzy knowledge on the Semantic Web, we propose the fuzzy linguistic variables ontology (FLVO), which utilizes ontology to represent formally the fuzzy linguistic variables and defines the semantic relationships between fuzzy concepts. Then fuzzy rules are described in Semantic Web Rule Language (SWRL) on the basis of FLVO model. Taking a sample case for students’ performance in physics for example, the fuzzy rule management system is built by using the tool protégé and SWRLTab, which shows that this research enables distributed fuzzy applications on the Semantic Web.

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