Functional network is a recently introduced extension
of neural networks. Like neural networks, nowadays,
there is no system designing method for designing
approximation functional networks structure. A new
genetic programming designing modelling method,
combining genetic programming and genetic algorithm,
was proposed for hybrid identification of model
structure and functional parameters by performing
global optimal search in the complex solution space
where the structures and parameters coexist and
interact. These results also show that the proposed
method in this paper can produce very compact network
structure and the functional networks convergent
precision are improved greatly
%0 Conference Paper
%1 Zhou:2006:WCICA
%A Zhou, Yongquan
%A Wang, Dongdong
%A Zhang, Ming
%B The Sixth World Congress on Intelligent Control and
Automation, WCICA 2006
%C Dalian
%D 2006
%I IEEE
%K algorithms, genetic programming
%P 3250--3254
%R doi:10.1109/WCICA.2006.1712968
%T Designing Functional Networks Through Evolutionary
Programming
%V 1
%X Functional network is a recently introduced extension
of neural networks. Like neural networks, nowadays,
there is no system designing method for designing
approximation functional networks structure. A new
genetic programming designing modelling method,
combining genetic programming and genetic algorithm,
was proposed for hybrid identification of model
structure and functional parameters by performing
global optimal search in the complex solution space
where the structures and parameters coexist and
interact. These results also show that the proposed
method in this paper can produce very compact network
structure and the functional networks convergent
precision are improved greatly
%@ 1-4244-0332-4
@inproceedings{Zhou:2006:WCICA,
abstract = {Functional network is a recently introduced extension
of neural networks. Like neural networks, nowadays,
there is no system designing method for designing
approximation functional networks structure. A new
genetic programming designing modelling method,
combining genetic programming and genetic algorithm,
was proposed for hybrid identification of model
structure and functional parameters by performing
global optimal search in the complex solution space
where the structures and parameters coexist and
interact. These results also show that the proposed
method in this paper can produce very compact network
structure and the functional networks convergent
precision are improved greatly},
added-at = {2008-06-19T17:46:40.000+0200},
address = {Dalian},
author = {Zhou, Yongquan and Wang, Dongdong and Zhang, Ming},
biburl = {https://www.bibsonomy.org/bibtex/2798a3fec4d20dfec3c92f4c40d80a482/brazovayeye},
booktitle = {The Sixth World Congress on Intelligent Control and
Automation, WCICA 2006},
doi = {doi:10.1109/WCICA.2006.1712968},
interhash = {507b77a297edfa14cbe484e507ad2b38},
intrahash = {798a3fec4d20dfec3c92f4c40d80a482},
isbn = {1-4244-0332-4},
keywords = {algorithms, genetic programming},
notes = {Coll. of Comput. & Inf. Sci., Guangxi Univ. for
Nationalities, Nanning},
pages = {3250--3254},
publisher = {IEEE},
timestamp = {2008-06-19T17:55:53.000+0200},
title = {Designing Functional Networks Through Evolutionary
Programming},
volume = 1,
year = 2006
}