DocumentCode :
1752855
Title :
Designing Functional Networks Through Evolutionary Programming
Author :
Zhou, Yongquan ; Wang, Dongdong ; Zhang, Ming
Author_Institution :
Coll. of Comput. & Inf. Sci., Guangxi Univ. for Nationalities, Nanning
Volume :
1
fYear :
0
fDate :
0-0 0
Firstpage :
3250
Lastpage :
3254
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 modeling 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
Keywords :
evolutionary computation; neural nets; search problems; Lagrange multipliers; evolutionary programming; functional network; genetic algorithm; genetic programming designing modeling; global optimal search; hybrid identification; learning algorithm; neural network; neuron function; Algorithm design and analysis; Computer networks; Design methodology; Educational institutions; Electronic mail; Functional programming; Genetic algorithms; Genetic programming; Information science; Neural networks; Functional networks; Genetic programming; Lagrange multipliers technique; Learning algorithm; Neuron function;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on
Conference_Location :
Dalian
Print_ISBN :
1-4244-0332-4
Type :
conf
DOI :
10.1109/WCICA.2006.1712968
Filename :
1712968
Link To Document :
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