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Research and Development of Granular Neural Networks

Hui Li, Shifei Ding
2013 Applied Mathematics & Information Sciences  
Firstly, it introduces the basic model of GrC: word calculation model based on fuzzy sets theory, rough sets model, granular computing model based on quotient space theory and so on, summarizes the research  ...  progress of fuzzy neural networks(FNNs) and rough neural networks(RNNs), then analyses the ensemble-based methods of GNNs, researches their meeting point of three main GrC methods, and finally points  ...  The OWA neuron The OWA neuron is constructed by a set of OWA fuzzy aggregation operator, which can study by BP.  ... 
doi:10.12785/amis/070350 fatcat:rflsc3jtgncennn7lqgas7y33u

Guest Editorial for the Special Issue on Fuzzy Rough Sets for Big Data

Weiping Ding, Witold Pedrycz, Chin-Teng Lin
2020 IEEE transactions on fuzzy systems  
co-evolution for fuzzy attribute reduction by quantum leaping PSO with nearest-neighbor memeplexes," IEEE Trans.  ...  His current main research directions involve computational intelligence, fuzzy modeling, granular computing, and knowledge discovery. In 2009, Dr.  ...  He is the recipient of the IEEE Canada Computer Engineering Medal, and a Fuzzy Pioneer Award from the IEEE Computational Intelligence Society.  ... 
doi:10.1109/tfuzz.2020.2979204 fatcat:kau4a4xp2zfdtjbrx5jpfmg7vm

A Modal Characterization of Indiscernibility and Similarity Relations in Pawlak's Information Systems [chapter]

Dimiter Vakarelov
2005 Lecture Notes in Computer Science  
682 Discernibility-Based Variable Granularity and Kansei Representations Yuji Muto, Mineichi Kudo 692 Rough Set Approximation Based on Dynamic Granulation Jiye Liang, Yuhua Qian, Chengyuan Chu, Deyu Li  ...  Meybodi 441 Finding Minimal Rough Set Reducts with Particle Swarm Optimization Xianggang Wang, Jie Yang, Ningsong Peng, Xiaolong Teng 451 Table of of Combining Classifiers Based on OWA Operators  ... 
doi:10.1007/11548669_2 fatcat:izzj5s3t6nffzelnlkvdbn2qk4

A granular neural network: Performance analysis and application to re-granulation

Scott Dick, Andrew Tappenden, Curtis Badke, Olufemi Olarewaju
2013 International Journal of Approximate Reasoning  
Determining this "natural" granulation could be done by inductively learning and comparing multiple granular representations of the phenomenon, but this requires a dedicated learning architecture.  ...  The Granular Neural Network is based on the multiplayer perceptron architecture and the backpropagation learning algorithm with momentum.  ...  GrC arises from the fusion of several different reasoning formalisms, including fuzzy sets, rough sets [3-6], fuzzy-rough sets [7], interval analysis, shadowed sets [2], and their various hybridizations  ... 
doi:10.1016/j.ijar.2013.01.012 fatcat:mkpfkplh3jbtdlfkkmpnee7ete

Fuzzy granular approximation classifier [article]

Marko Palangetić, Chris Cornelis, Salvatore Greco, Roman Słowiński
2022 arXiv   pre-print
The classifier is based on the previously introduced concept of the granular approximation and its multi-class classification case.  ...  In this article, a new Fuzzy Granular Approximation Classifier (FGAC) is introduced.  ...  Fuzzy rough and granularly representable sets This subsection is based on [5] . Let U be a finite set of instances, A a fuzzy set on U and R a T -preorder relation on U .  ... 
arXiv:2206.01240v1 fatcat:4rm6w6x7g5a7tgogmvgyfwhc6m

A New Selection Process Based on Granular Computing for Group Decision Making Problems [chapter]

Francisco Javier Cabrerizo, Raquel Ureña, Juan Antonio Morente-Molinera, Witold Pedrycz, Francisco Chiclana, Enrique Herrera-Viedma
2015 Communications in Computer and Information Science  
As part of it, we use a method based on granular computing to increase the consistency of the opinions given by the decision makers.  ...  Finally, the importance of the decision makers' opinions in the aggregation step is modeled by means of their consistency.  ...  G(.) represents the specific granular formalism which is used, say intervals, probability density functions, fuzzy sets, rough sets, and alike.  ... 
doi:10.1007/978-3-319-17530-0_2 fatcat:33dc6nywifevlpnqvimvtisrki

Mathematical Foundations of AIML

2022 Zenodo  
Most of this will be compatible with graphs, partial orders, lattices and generalized order-based approaches.  ...  Compositionality is essential for reducing the complexity of AIML models. This is naturally related to knowledge representation and granularity. These, in turn, can be done in different ways.  ...  [Mani, 2013a] , numeric functions used in rough sets (and soft computing in general) and fuzzy representation of linguistic hedges.  ... 
doi:10.5281/zenodo.6255983 fatcat:nkb7apo6ubc3bpfyije54k6f6i

A comprehensive model and computational methods to improve Situation Awareness in Intelligence scenarios

Angelo Gaeta, Vincenzo Loia, Francesco Orciuoli
2021 Applied intelligence (Boston)  
These three perspectives are instantiated on the basis of the principles and methods of Granular Computing, mainly based on the theories of fuzzy and rough sets, and with the help of further structures  ...  The main result presented in the paper stems from a work of refinement and abstraction of previous results of the authors related to the use of Situation Awareness and Granular Computing for the development  ...  The advantages of granular computing are multiple: the possibility to adopt a plethora of formal settings (e.g., Rough Set Theory, Fuzzy Logic, etc.), its flexibility (as mentioned before) to be deployed  ... 
doi:10.1007/s10489-021-02673-z pmid:34764614 pmcid:PMC8325623 fatcat:3bp24lz7nzgixoljmlle6kevs4

Preface to the Special Issue on "Applications of Fuzzy Optimization and Fuzzy Decision Making"

Vassilis C. Gerogiannis
2021 Mathematics  
During the last decades, fuzzy optimization and fuzzy decision making have gained significant attention, aiming to provide robust solutions for problems in making decisions and achieving complex optimization  ...  Conflicts of Interest: The authors declare no conflict of interest.  ...  [13] examines the decision-theoretical rough sets (DTRSs). The proposed model is based on the loss function of DTRSs.  ... 
doi:10.3390/math9233009 fatcat:4kxvatzkejacrdvrjs54x2hfhi

2020 Index IEEE Transactions on Fuzzy Systems Vol. 28

2020 IEEE transactions on fuzzy systems  
., An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS; TFUZZ June 2020 1062-1072 Weinstein, A., see Veloz, A., TFUZZ Jan. 2020 100-111  ...  ., +, TFUZZ Nov. 2020 2702-2710 Hypotheses Analysis and Assessment in Counterterrorism Activities: A Method Based on OWA and Fuzzy Probabilistic Rough Sets.  ...  ., +, TFUZZ May 2020 806-817 Active Incremental Feature Selection Using a Fuzzy-Rough-Set-Based Information Entropy.  ... 
doi:10.1109/tfuzz.2020.3048828 fatcat:vml5fun6szcqbhpceebk3xfg2u

Comparative Approaches to Granularity in General Rough Sets [chapter]

A. Mani
2020 Lecture Notes in Computer Science  
This expository paper is intended to explain basic aspects of these from a critical perspective, their range of applications and provide directions relative to general rough sets and related formal approaches  ...  A number of nonequivalent perspectives on granular computing are known in the literature, and many are in states of continuous development.  ...  used in rough sets (and soft computing in general) and fuzzy representation of linguistic hedges.  ... 
doi:10.1007/978-3-030-52705-1_37 fatcat:mll3vrtu7rbnvm4km5tacw2q5y

Water Quality Failures in Distribution Networks-Risk Analysis Using Fuzzy Logic and Evidential Reasoning

Rehan Sadiq, Yehuda Kleiner, Balvant Rajani
2007 Risk Analysis  
The E-OWA weights are defined as follows: w 1 = β; w 2 = β (1 -β); … w n-1 = β (1 -β) n -2 and w n = (1 -β) n -1 ; 0 ≤ β ≤ 1 (10) where n is the granularity of fuzzy risk.  ...  set X, where membership μ p of X L is transformed to μ p N of X by dividing each μ p by the cardinality C (sum of all memberships in a fuzzy set). ( 2 ) In the example of Figure 2 , the fuzzy set X is  ... 
doi:10.1111/j.1539-6924.2007.00972.x pmid:18076503 fatcat:uhupgnhbjnhxrcq2nj72r6tjxu


Eva Rakovská
2021 Proceedings of CBU in Economics and Business  
The article shows how to treat non-heterogeneous data to prepare them for a ranking process using fuzzy sets theory.  ...  The article aims at offering several types of ranking methods based on different inputs and preferences of the user and describes appropriate fuzzy aggregations for solving the ranking problem.  ...  Ministry of Education, Science, Research and Sport of the Slovak Republic.  ... 
doi:10.12955/peb.v2.258 fatcat:zrn5e5eujzgq3oeeepgtw56w7m

The posterity of Zadeh's 50-year-old paper

James C. Bezdek, Didier Dubois, Henri Prade
2015 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)  
OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible.  ...  Prade, "Rough fuzzy sets and fuzzy rough sets," IJGS, 17(2-3), 1990, pp. 191-209. This paper shows that fuzzy sets and rough sets address different issues and are complementary.  ...  It applies for the first time the machinery of rough sets to fuzzy sets, thus yielding upper and lower fuzzy approximations, and replaces the equivalence relation underlying rough sets by a fuzzy similarity  ... 
doi:10.1109/fuzz-ieee.2015.7337858 dblp:conf/fuzzIEEE/BezdekDP15 fatcat:jeydd2agx5eppps2bpy5z32mcm

Granular fuzzy models: a study in knowledge management in fuzzy modeling

Witold Pedrycz, Mingli Song
2012 International Journal of Approximate Reasoning  
We focus on the design of granular fuzzy models considering that the locally available models are those fuzzy rule-based.  ...  It is also shown how the construction of information granules completed through the use of the principle of justifiable granularity becomes advantageous in the realization of granular fuzzy models and  ...  Acknowledgments Support from the Natural Sciences and Engineering of Canada (NSERC) and Canada Research Chair (CRC) Program is gratefully acknowledged.  ... 
doi:10.1016/j.ijar.2012.05.002 fatcat:x7tzcgoiorhztmbz7umgnxv5oy
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