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Modelling and Indexing of Fuzzy Complex Shapes
[chapter]
2002
Visual and Multimedia Information Management
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We also discuss techniques for indexing and retrieving of fuzzy shapes in an object relational database with a fuzzy processing module. ...
Representation of fuzzy complex shapes There exist many representation schemes for modelling 3D shapes such as point clouds [11] ; Constructive Solid Geometry (CSG), Boundary Representation (B-Rep), sweeping ...
This paper focuses on the construction of fuzzy complex shapes based on descriptive terms that are related to shape, spatial location and orientation, and the indexing and retrieving of fuzzy complex shapes ...
doi:10.1007/978-0-387-35592-4_28
fatcat:t7mzr7relrchtkwzx26i3nhuly
Class-based recognition of 3D objects represented by volumetric primitives
1997
Image and Vision Computing
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and the nal valid hypotheses based on Possibility Theory, or Theory of Fuzzy Sets. ...
Test scenes are acquired by a laser striper as range images, and the objects are modelled using a composite volumetric representation of superquadrics and geons. ...
Results are given for a variety of complex 3D shapes, and identi cation and ranking are successfully achieved. ...
doi:10.1016/s0262-8856(97)00008-5
fatcat:5qumcgdjrrawvepwk7pq46issa
Class-based Recognition of 3D Objects Represented by Volumetric Primitives
1996
Procedings of the British Machine Vision Conference 1996
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and the nal valid hypotheses based on Possibility Theory, or Theory of Fuzzy Sets. ...
Test scenes are acquired by a laser striper as range images, and the objects are modelled using a composite volumetric representation of superquadrics and geons. ...
, search complexity, reliance on veri cation, and model exibility. ...
doi:10.5244/c.10.5
dblp:conf/bmvc/BorgesF96
fatcat:4vy5hsjdnrgxfce7ubeml45oa4
Adaptive-network-based Fuzzy Inference (anfis) Modelling of Particle Image Velocimetry (piv) Measurements in Stirred Tank Reactors
2020
Chemical Engineering Transactions
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The fitness of the produced models was scored by means of the fuzzy Goodness Index (GI), which combines the correlation coefficient (R2), index of agreement (IA) and relative root mean square error (RRMSE ...
The present work presents an innovative machine-learning modelling approach which uses the adaptive-network-based fuzzy inference system (ANFIS) on experimentally velocity fields data collected through ...
Coupling experimental measurements to soft-modelling techniques, such as fuzzy logic and neural networks, presents a great potential to process and model complex systems using expert knowledge or inference ...
doi:10.3303/cet2079001
doaj:58d318cad5e64602b1a42fb2812811bd
fatcat:i7wwirycbfhntoay46gqkonpl4
Fuzzy modeling of Petrophysical Properties Prediction Applying RBE-DSS and LESFRI
2007
2007 International Symposium on Logistics and Industrial Informatics
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This paper reports the generation of a fuzzy system, which models the relation between different oil well data aiming the prediction of petrophysical properties. ...
The applied rule base generation method is RBE-DSS [8] and the fuzzy inference was performed by the technique LESFRI [6]. ...
FUZZY MODELING
1) System Generation using RBE-DSS The rule base of the fuzzy model was generated by the method Rule Base Extension using Default Set Shapes (RBE-DSS) introduced in [8] . ...
doi:10.1109/lindi.2007.4343518
fatcat:mve55i7efzbytja74j653am7sm
Sparse Fuzzy Model Identification Matlab Toolox ¿ RuleMaker Toolbox
2008
2008 IEEE International Conference on Computational Cybernetics
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This paper presents a freely available Matlab toolbox called RuleMaker that supports the automatic generation of a fuzzy model with low complexity. ...
Fuzzy systems applying a sparse rule base and a fuzzy rule interpolation based reasoning method are popular solutions in cases with partial knowledge of the modeled area or cases when the full coverage ...
This paper presents a freely available Matlab toolbox called RuleMaker that supports the automatic generation of a fuzzy model with low complexity. ...
doi:10.1109/icccyb.2008.4721381
fatcat:czu24z6zu5bfzn6ymnx5zz5zb4
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems
2011
International Journal of Approximate Reasoning
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These fuzzy sets are defined in their respective universes of discourse U 1 , . . . , U n , V , and are characterized by their membership functions: Different fuzzy membership function shapes can be considered ...
Fig. 2 shows an example of a strong fuzzy partition (SFP) [139] with triangular-shaped membership functions. ...
Hence, the definition of more complex and plausible interpretability indexes has become a hot topic in the fuzzy modeling community in the last few years and some proposals have been made [16, 17, 74, ...
doi:10.1016/j.ijar.2011.03.004
fatcat:lpzqyhmvnzaq5b7a4qkle6toii
A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis
[chapter]
2017
Modern Fuzzy Control Systems and Its Applications
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[10, 11] Modern Fuzzy Control Systems and Its Applications 282 ...
Agent-based and multiagent systems Agent-based and multiagent systems (MAS) recently show a variety of applications: they range from simulation of complex systems such as social systems [9] and ecosystems ...
-2-class "Rectangular Fit") AND
NOT(operator-2-class "Border Index
OR Shape Index"
A Fuzzy Belief-Desire-Intention Model for Agent-Based Image Analysis http://dx.doi.org/10.5772/67899
Modern ...
doi:10.5772/67899
fatcat:64kcficd4fc45ldehde4f52pz4
A simple Mathematical Fuzzy Model of Brain Emotional Learning to Predict Kp Geomagnetic Index
2014
International Journal of Intelligent Systems and Applications in Engineering
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Numerical results show that the correlation of the results obtained from the proposed fuzzy model is higher than non-fuzzy models. ...
In this paper, we propose fuzzy mathematical model of brain limbic system (LS) which is responsible for emotional stimuli. ...
Conclusions In this paper we presented fuzzy model of limbic system named FDBEL and utilized to predict Kp geomagnetic index. ...
doi:10.18201/ijisae.85494
fatcat:enz43av3gbayzeoqtsgcm2ztym
Fuzzy Evaluation Model for Enhancing E-Learning Systems
2019
Mathematics
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Furthermore, the fuzzy membership function of the discriminant index was constructed based on the confidence interval, thereby solving the problems of sampling error and the complexity of collecting fuzzy ...
This approach maintained the simple response model of Likert scales, which increases the efficiency and accuracy of data collection. ...
Furthermore, the fuzzy membership function of the discriminant index was constructed based on the confidence interval, which solves the problems of sampling error and the complexity of collecting fuzzy ...
doi:10.3390/math7100918
fatcat:wanlu4lyajcwxlmrvavwa6fxwu
Decision-making Tool for Moving from 3-axes to 5-axes CNC Machine-tool
2016
Procedia Computer Science
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The tool was built using the Fuzzy Logic Toolbox and the fuzzy graphical user interface (GUI) within Matlab software package. ...
This paper presents a decision support tool for aiding the process of selecting between using and/or buying either a 3-axes CNC milling machine or a 5axes milling machine, based upon fuzzy logic. ...
provided and for facilitating contacts with a large number of manufacturing engineers which shared their valuable experience in the field of using CNC machine-tools. ...
doi:10.1016/j.procs.2016.07.056
fatcat:avzzwke5gjawdk3sqr3vg3pbji
Rainfall-runoff prediction using a Gustafson-Kessel clustering based Takagi-Sugeno Fuzzy model
[article]
2021
arXiv
pre-print
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Takagi-Sugeno (TS) Fuzzy models are systems-based approaches and a popular modeling choice for hydrologists in recent decades due to several advantages and improved accuracy in prediction over other existing ...
Our proposed TS Fuzzy model predicts surface runoff using: (i) observed rainfall in a drainage basin and (ii) previously observed precipitation flow in the basin outlet. ...
Therefore, this validity index may not favorable for highly complex dataset. ...
arXiv:2108.09684v1
fatcat:nrarf74a5zb7hdxgbskb3mic3e
Design and analysis of experiments in ANFIS modeling for stock price prediction
2011
International Journal of Industrial Engineering Computations
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This neuro-fuzzy modeling approach has preference to explain solutions over completely black-box models, such as ANN. ...
ANN learning algorithms can be employed for optimization of parameters in a fuzzy system. ...
of fuzziness, training epochs, and cluster validity index. ...
doi:10.5267/j.ijiec.2011.01.001
fatcat:hks4fefyrjhmzckvrmu7lgkone
Entropy-Cloud Model of Heavy Metals Pollution Assessment in Farmland Soils of Mining Areas
2016
Polish Journal of Environmental Studies
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Certainty degrees of each level are calculated by the entropy-cloud model, and the fuzzy entropy of certainty degrees is calculated to indicate the complexity of heavy metal pollution. ...
from two aspects of level and complexity. ...
Social Sciences Youth Fund (11YJCZH119); and the Food Economics and Management Research Base, which is a College of Humanities and Social Science research base in Shandong Province. ...
doi:10.15244/pjoes/61883
fatcat:cvlzzfyn7jba7g5ydtmizksp6y
Neuro fuzzy based for prediction quality of a rubber curing process on a compression machine under uncertainty circumstances
2020
Materials Today: Proceedings
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A compression mould with many cavities is hold and heated under the specific standard process procedure and characteristics of the rubber. ...
Four influence factors are input to the fuzzy inference system. After the fuzzification process, the output of finished good can be predicted. ...
Neuro-Fuzzy based modelling is suitable to deal with complexity, imprecision and uncertainty environment. Fuzzy logic provides a solution for modelling uncertainty. ...
doi:10.1016/j.matpr.2020.02.610
fatcat:4iygpahnnffpfbe5hqs6mhwbnm
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