Fuzzy rough granular neural networks, fuzzy granules, and classification
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TLDR
A fuzzy rough granular neural network model based on the multilayer perceptron using a back-propagation algorithm for the fuzzy classification of patterns is introduced and the effectiveness of the proposed FRGNN is demonstrated on several real-life data sets.About:
This article is published in Theoretical Computer Science.The article was published on 2011-09-01 and is currently open access. It has received 54 citations till now. The article focuses on the topics: Fuzzy classification & Fuzzy number.read more
Citations
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Granular computing, computational intelligence, and the analysis of non-geometric input spaces
Lorenzo Livi,Alireza Sadeghian +1 more
TL;DR: The fundamental, conceptual problems underlying the process of data granulation are elaborate over, which drive the quest for a sound theory of granular computing.
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A study of granular computing in the agenda of growth of artificial neural networks
Mingli Song,Yongbin Wang +1 more
TL;DR: A comprehensive review of articles that involve a comparative study of different types of granular neural networks and their application is carried out to give useful insight into the capability of granularity neural networks.
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Applications of Fuzzy Rough Set Theory in Machine Learning: a Survey
TL;DR: A thorough review on the use of fuzzy rough sets in machine learning applications is presented and the interaction between theoretical advances on fuzzy rough set and practical machine learning tools that take advantage of them are highlighted.
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Fuzzy rough sets, and a granular neural network for unsupervised feature selection
TL;DR: A granular neural network for identifying salient features of data, based on the concepts of fuzzy set and a newly defined fuzzy rough set, which is found to be statistically more significant than related methods in 28 instances of 40 instances, i.e., 70% of instances, using the paired t-test.
References
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Book
Fuzzy sets
TL;DR: A separation theorem for convex fuzzy sets is proved without requiring that the fuzzy sets be disjoint.
Book
Neural Networks: A Comprehensive Foundation
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Journal ArticleDOI
ANFIS: adaptive-network-based fuzzy inference system
TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.