|
Title:
|
HYBRID SYSTEM BASED ON ROUGH SETS AND WAVELET NEURAL NETWORKS |
|
Author(s):
|
Yasser F. Hassan |
|
ISBN:
|
978-972-8924-87-4 |
|
Editors:
|
António Palma dos Reis |
|
Year:
|
2009 |
|
Edition:
|
Single |
|
Keywords:
|
Wavelet Neural Networks, Rough Sets, Structure Adaptation, Classification. |
|
Type:
|
Full Paper |
|
First Page:
|
69 |
|
Last Page:
|
76 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
Wavelet neural networks have recently attracted great interest, because of their advantages over conventional neural
networks as they are universal approximations and achieve faster convergence. This paper introduces a new wavelet
neural network architecture based on rough neuron which are used cooperatively for decision and classification support.
The neurons of such networks instantiate approximate reasoning knowledge gleaned from input data. Each rough neuron
constructs upper and lower approximations as an aid to classifying inputs. The new model uses rough set theory to help in
decreasing the computational effort needed for building the network structure by using what is called reduct algorithm.
The reduct algorithm based on rough set approach attempts to remove redundant attributes without any classification
information loss. |
|
|
|
|
|
|
