Skip to content
RDL
Network
Ekosistem
Uygulama değiştir
EN
Hakkımızda
SSS
Giriş yap
Başla
Design of Evolutionally Optimized Rule-Based Fuzzy Neural Networks Based on Fuzzy Relation and Evolutionary Optimization — Byoung‐Jun Park (2005) | RDL Network
Back
Cite
Save
Save for later
Share
Home
Publications
Design of Evolutionally Optimized Rule-Based Fuzzy Neural Networks Based on Fuzzy Relation and Evolutionary Optimization
Shared by
Witold Pedrycz
University of Alberta
Design of Evolutionally Optimized Rule-Based Fuzzy Neural Networks Based on Fuzzy Relation and Evolutionary Optimization
Chapter In A Book
2005
en
Authors
+1 more
BP
Byoung‐Jun Park
SO
Sung‐Kwun Oh
Witold Pedrycz
University of Alberta
Discussion
(0)
Sign in
to like and join the discussion.
No comments yet. Be the first to comment.
Related publications
Chapter in a book
2005
Evolutionally Optimized Fuzzy Neural Networks Based on Evolutionary Fuzzy Granulation
Sung‐Kwun Oh
,
Byoung‐Jun Park
,
Witold Pedrycz
,
Hyunki Kim
Article
2008
SIMPLIFIED FUZZY INFERENCE RULE-BASED GENETICALLY OPTIMIZED HYBRID FUZZY NEURAL NETWORKS
Byoung‐Jun Park
,
Witold Pedrycz
,
Sung‐Kwun Oh
Chapter in a book
2008
Genetically Optimized Hybrid Fuzzy Neural Networks: Analysis and Design of Rule-based Multi-layer Perceptron Architectures
Sung‐Kwun Oh
,
Witold Pedrycz
Chapter in a book
2007
Genetically Optimized Rule-Based Fuzzy Polynomial Neural Networks: Synthesis of Computational Intelligence Technologies
Sung‐Kwun Oh
,
James F. Peters
,
Witold Pedrycz
,
Tae-Chon Ahn
Article
2022
Design of stabilized fuzzy relation-based neural networks driven to ensemble neurons/layers and multi-optimization
Zheng Wang
,
Sung‐Kwun Oh
,
Witold Pedrycz
,
Eun-Hu Kim
,
Zunwei Fu
Discussion(0)
No comments yet. Be the first to comment.