DocumentCode :
3600377
Title :
Extension Theory and the Application in Optimization of Immune Neural Network
Author :
Zhu, Xiaoyuan ; Yu, Yongquan ; Guo, Xueyan
Author_Institution :
Dept. of Comput., Guangdong Baiyun Univ., Guangzhou
Volume :
1
fYear :
2009
Firstpage :
842
Lastpage :
847
Abstract :
In the Immune Neural Network (INN), the key point is stability and convergence. The existing INN has shortages in the control of local optimization, so the paper bring forward INN algorithm which is based on extension theory. With the concepts of dependent function and matter-element, the improved algorithm firstly can optimize architecture and rule extraction of INN. And then, the new algorithm is applied in emulation experiment, which is used for computing of function. According to simulation results, the improved algorithm is compared with the existing INN. It indicates that extension theory has better advantage in optimization of INN. So the new algorithm has great reference value.
Keywords :
convergence; neural nets; optimisation; convergence; extension theory; immune neural network; optimization; rule extraction; stability; Application software; Artificial intelligence; Computer networks; Computer science; Computer science education; Educational technology; Immune system; Logic; Neural networks; Switches; Dependent function; Extension theory; Immune Neural Network;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Education Technology and Computer Science, 2009. ETCS '09. First International Workshop on
Print_ISBN :
978-1-4244-3581-4
Type :
conf
DOI :
10.1109/ETCS.2009.191
Filename :
4958896
Link To Document :
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