71,238 research outputs found
Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis
In this paper, a neural network implementation for a fuzzy logic-based model of the diagnostic process is proposed as a means to achieve accurate student diagnosis and updates of the student model in Intelligent Learning Environments. The neuro-fuzzy synergy allows the diagnostic model to some extent "imitate" teachers in diagnosing students' characteristics, and equips the intelligent learning environment with reasoning capabilities that can be further used to drive pedagogical decisions depending on the student learning style. The neuro-fuzzy implementation helps to encode both structured and non-structured teachers' knowledge: when teachers' reasoning is available and well defined, it can be encoded in the form of fuzzy rules; when teachers' reasoning is not well defined but is available through practical examples illustrating their experience, then the networks can be trained to represent this experience. The proposed approach has been tested in diagnosing aspects of student's learning style in a discovery-learning environment that aims to help students to construct the concepts of vectors in physics and mathematics. The diagnosis outcomes of the model have been compared against the recommendations of a group of five experienced teachers, and the results produced by two alternative soft computing methods. The results of our pilot study show that the neuro-fuzzy model successfully manages the inherent uncertainty of the diagnostic process; especially for marginal cases, i.e. where it is very difficult, even for human tutors, to diagnose and accurately evaluate students by directly synthesizing subjective and, some times, conflicting judgments
Hardware/software codesign methodology for fuzzy controller implementation
This paper describes a HW/SW codesign methodology
for the implementation of fuzzy controllers on a platform
composed by a general-purpose microcontroller and specific
processing elements implemented on FPGAs or ASICs. The
different phases of the methodology, as well as the CAD tools
used in each design stage, are presented, with emphasis on the
fuzzy system development environment Xfuzzy. Also included is
a practical application of the described methodology for the
development of a fuzzy controller for a dosage system
Natural Language Generation and Fuzzy Sets : An Exploratory Study on Geographical Referring Expression Generation
This work was supported by the Spanish Ministry for Economy and Competitiveness (grant TIN2014-56633-C3-1-R) and by the European Regional Development Fund (ERDF/FEDER) and the Galician Ministry of Education (grants GRC2014/030 and CN2012/151). Alejandro Ramos-Soto is supported by the Spanish Ministry for Economy and Competitiveness (FPI Fellowship Program) under grant BES-2012-051878.Postprin
On the similarity relation within fuzzy ontology components
Ontology reuse is an important research issue. Ontology
merging, integration, mapping, alignment and versioning
are some of its subprocesses. A considerable research work has
been conducted on them. One common issue to these subprocesses
is the problem of defining similarity relations among ontologies
components. Crisp ontologies become less suitable in all domains
in which the concepts to be represented have vague, uncertain
and imprecise definitions. Fuzzy ontologies are developed to
cope with these aspects. They are equally concerned with the
problem of ontology reuse. Defining similarity relations within
fuzzy context may be realized basing on the linguistic similarity
among ontologies components or may be deduced from their
intentional definitions. The latter approach needs to be dealt
with differently in crisp and fuzzy ontologies. This is the scope
of this paper.ou
Spatial modelling of adaptation strategies for urban built infrastructures exposed to flood hazards
The recent 2010/2011 floods in the central and southern Queensland (Australia) prompted this research to investigate the application of geographical information system (GIS) and remote sensing in modelling the current flood risk, adaptation/coping capacity, and adaptation strategies. Identified Brisbane City as the study area, the study aimed to develop a new approach of formulating adaptation/coping strategies that will aid in addressing flood risk management issues of an urban area with intensive residential and commercial uses. Fuzzy logic was the spatial analytical tool used in the integration of flood risk components (hazard, vulnerability, and exposure) and in the generation of flood risk and adaptation capacity indices. The research shows that 875 ha, 566 ha, and 828 ha were described as areas with relatively low, relatively moderate, and relatively high risk to flooding, respectively. Identified adaptation strategies for areas classified as having relatively low (RL), relatively moderate (RM), relatively high (RH), and likely very high (LVH) adaptation/coping capacity were mitigation to recovery phases, mitigation to response phases, mitigation to preparedness phases, and mitigation phase, respectively. Integrating the results from the flood risk assessment, quantitative description of adaptation capacity, and identification of adaptation strategies, a new analytical technique identified as flood risk-adaptation capacity index-adaptation strategies (FRACIAS) linkage model was developed for this study
Derivation of diagnostic models based on formalized process knowledge
© IFAC.Industrial systems are vulnerable to faults. Early and accurate detection and diagnosis in production systems can minimize down-time, increase the safety of the plant operation, and reduce manufacturing costs. Knowledge- and model-based approaches to automated fault detection and diagnosis have been demonstrated to be suitable for fault cause analysis within a broad range of industrial processes and research case studies. However, the implementation of these methods demands a complex and error-prone development phase, especially due to the extensive efforts required during the derivation of models and their respective validation. In an effort to reduce such modeling complexity, this paper presents a structured causal modeling approach to supporting the derivation of diagnostic models based on formalized process knowledge. The method described herein exploits the Formalized Process Description Guideline VDI/VDE 3682 to establish causal relations among key-process variables, develops an extension of the Signed Digraph model combined with the use of fuzzy set theory to allow more accurate causality descriptions, and proposes a representation of the resulting diagnostic model in CAEX/AutomationML targeting dynamic data access, portability, and seamless information exchange
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