Title :
Rough Set theory to CP networks optimization
Author :
Dong, Min ; Li, XiangPeng ; Liu, Qing
Author_Institution :
Acad. of Sci., Wuhan Univ. of Sci. & Eng., Wuhan
Abstract :
A designing method for counter-propagation neural networks based on rough set theory is presented in this paper. Counter-propagation networks has been applied to various fields because of its topological construction closed to the mankindpsilas brain, while rough set theory has a powerful capability for qualitative analysis. By combining those advantages of the two theories, we can construct a kind of neural networks with good understandability, simple computation and exact accuracy. In this paper, the key of the algorithm is that the input amples are simplified and classified by using rough set theory before trained.
Keywords :
learning (artificial intelligence); neural nets; optimisation; rough set theory; CP network optimization; counter-propagation neural network training; rough set theory; Biological neural networks; Computer networks; Computer science; Design engineering; Design methodology; Design optimization; Information systems; Power engineering and energy; Power engineering computing; Set theory;
Conference_Titel :
Granular Computing, 2008. GrC 2008. IEEE International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-2512-9
Electronic_ISBN :
978-1-4244-2513-6
DOI :
10.1109/GRC.2008.4664662
