The learning algorithm for a novel fuzzy neural network

Authors:
Puyin Liu;Qiang Luo;Wenqiang Yang;Dongyun Yi
Affiliations:
Department of Mathematics, National University of Defense Technology, Changsha, Hunan, P.R. China;Department of Mathematics, National University of Defense Technology, Changsha, Hunan, P.R. China;Department of Mathematics, National University of Defense Technology, Changsha, Hunan, P.R. China;Department of Mathematics, National University of Defense Technology, Changsha, Hunan, P.R. China
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 15
Cited 1

Fuzzy neural networks: a survey

Fuzzy Sets and Systems
A learning algorithm of fuzzy neural networks with triangular fuzzy weights

Fuzzy Sets and Systems - Special issue on fuzzy neural control
Identification of fuzzy relational equations by fuzzy neural networks

Fuzzy Sets and Systems
Improved fuzzy neural networks for solving relational equations

Fuzzy Sets and Systems
Can neural nets be universal approximators for fuzzy functions?

Fuzzy Sets and Systems
The fuzzy neural network approximation lemma

Fuzzy Sets and Systems
An efficient computational algorithm for min-max operations

Fuzzy Sets and Systems
Fuzzy number neural networks

Fuzzy Sets and Systems
Analyses of regular fuzzy neural networks for approximation capabilities

Fuzzy Sets and Systems
Numerical analysis of the learning of fuzzified neural networks from fuzzy if—then rules

Fuzzy Sets and Systems - Special issue on clustering and learning
Fuzzified neural network based on fuzzy number operations

Fuzzy Sets and Systems - Fuzzy intervals
Representation of digital image by fuzzy neural network

Fuzzy Sets and Systems
Fuzzy Neural Network Theory and Application

Fuzzy Neural Network Theory and Application
Iterative inversion of fuzzified neural networks

IEEE Transactions on Fuzzy Systems
The min-max function differentiation and training of fuzzy neural networks

IEEE Transactions on Neural Networks

On polygonal fuzzy sets and numbers

Fuzzy Sets and Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Symmetric polygonal fuzzy numbers are employed to construct a class of novel feedforward fuzzy neural networks (FNN’s)—the polygonal FNN’s. Their input–output (I/O) relationships are built upon a novel fuzzy arithmetic and extension principle for the polygonal fuzzy numbers. We build the topological architecture of a three layer polygonal FNN, and present its I/O relationship representation. Also the fuzzy BP learning algorithm for the polygonal fuzzy number connection weights and thresholds is developed based on calculus of max–min (∨– ∧) functions. At last some simulation examples are compared to show that our model possess strong I/O ability and generalization capability.