Comparison of Granular Computing Models in a Set-Theoretic Framework

2012 ◽  
pp. 332-337 ◽  
Author(s):  
Ying Wang ◽  
Duoqian Miao
Keyword(s):  
Granular Computing ◽  
Computing Models

scholarly journals Granular Computing Models and Methods Based on the Spatial Granulation

2017 ◽  
Vol 103 ◽  
pp. 295-302 ◽  
Author(s):  
S. Butenkov ◽  
A. Zhukov ◽  
A. Nagorov ◽  
N. Krivsha
Keyword(s):  
Granular Computing ◽  
Computing Models

Granular Computing Models for Knowledge Uncertainty

2011 ◽  
Vol 22 (4) ◽  
pp. 676-694 ◽  
Author(s):  
Guo-Yin WANG ◽  
Qing-Hua ZHANG ◽  
Xi-Ao MA ◽  
Qing-Shan YANG
Keyword(s):  
Granular Computing ◽  
Knowledge Uncertainty ◽  
Computing Models

scholarly journals Isolation-based hyperbox granular classification computing

2016 ◽  
Vol 11 (2) ◽  
pp. 110-125
Author(s):  
Hongbing Liu ◽  
Fan Zhang ◽  
Ran Li ◽  
Chang-an Wu
Keyword(s):  
Granular Computing ◽  
The Other ◽  
Support Vector ◽  
Inclusion Relation ◽  
Top Down ◽  
Computing Model ◽  
Vector Machines ◽  
Speed Up ◽  
Model Algorithm ◽  
Computing Models

Bottom-up and top-down are two main computing models in granular computing by which the granule set including granules with different granularities. The top-down hyperbox granular computing classification algorithm based on isolation, or IHBGrC for short, is proposed in the framework of top-down computing model. Algorithm IHBGrC defines a novel function to measure the distance between two hyperbox hgranules, which is used to judge the inclusion relation between two hyperbox granules, the meet operation is used to isolate the ith class data from the other class data, and the hyperbox granule is partitioned into some hyperbox granules which include the ith class data. We compare the performance of IHBGrC with support vector machines and HBGrC, for a number of two-class problems and multiclass problems. Our computational experiments showed that IHBGrC can both speed up training and achieve comparable generalization performance.

Reversible Computing: An Introduction

2019 ◽  
Author(s):  
Federica Eftimiadi ◽  
Enrico Pugni Trimigliozzi
Keyword(s):  
Laws Of Nature ◽  
Physical Property ◽  
Computing Systems ◽  
Reversible Computing ◽  
Physical Laws ◽  
Structural Aspects ◽  
Computing Models

Reversible computing is a paradigm where computing models are defined so that they reflect physical reversibility, one of the fundamental microscopic physical property of Nature. Also, it is one of the basic microscopic physical laws of nature. Reversible computing refers tothe computation that could always be reversed to recover its earlier state. It is based on reversible physics, which implies that we can never truly erase information in a computer. Reversible computing is very difficult and its engineering hurdles are enormous. This paper provides a brief introduction to reversible computing. With these constraints, one can still satisfactorily deal with both functional and structural aspects of computing processes; at the same time, one attains a closer correspondence between the behavior of abstract computing systems and the microscopic physical laws (which are presumed to be strictly reversible) that underlay any implementation of such systems Available online at https://int-scientific-journals.com

scholarly journals Applications of soft computing models for predicting sea surface temperature: a comprehensive review and assessment

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Masoud Haghbin ◽  
Ahmad Sharafati ◽  
Davide Motta ◽  
Nadhir Al-Ansari ◽  
Mohamadreza Hosseinian Moghadam Noghani
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Soft Computing ◽  
Sea Surface ◽  
Linear Processes ◽  
Comprehensive Review ◽  
Droughts And Floods ◽  
Input Variables ◽  
And Performance ◽  
Computing Models

AbstractThe application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

Conversion-based aggregation algorithms for linear ordinal rankings combined with granular computing

2021 ◽  
Vol 219 ◽  
pp. 106880
Author(s):  
Nana Liu ◽  
Zeshui Xu ◽  
Hangyao Wu ◽  
Peijia Ren
Keyword(s):  
Granular Computing
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