DocumentCode
2992158
Title
Research on the Prediction of Breath Period Signal Based on RFN Network of Self-Adaptive Genetic Algorithm
Author
Junjie, Su ; Qiuhai, Zhong
Author_Institution
Coll. of Autom. Control, Beijing Inst. of Technol., Beijing, China
fYear
2010
fDate
25-27 June 2010
Firstpage
1798
Lastpage
1801
Abstract
A hybrid algorithm -RFN network of self-adaptive genetic algorithm was introduced, which combined the excellences of BP network, RFN network and genetic algorithm. The hybrid algorithm adopts the learning rule of RFN network and combines self-adaptive genetic algorithm and gradient descent method. The capability of prediction can be optimized using the hybrid algorithm and the shortcoming of the learning rule of RFN network was overcomed. At the same time, the problem that Global Optimal Solution always cann´t be found only with genetic algorithm was solved. Simulation results show that hybrid algorithm can obtain better forecasting precision.
Keywords
backpropagation; genetic algorithms; gradient methods; prediction theory; recurrent neural nets; BP network; RFN network; breath period signal prediction; global optimal solution; gradient descent method; hybrid algorithm; learning rule; self-adaptive genetic algorithm; Companies; Educational institutions; Gallium; Home appliances; Medical services; Prediction algorithms; Simulation; Breath period signal; RFN network; Self-adaptive genetic algorithm; prediction;
fLanguage
English
Publisher
ieee
Conference_Titel
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-6880-5
Type
conf
DOI
10.1109/iCECE.2010.442
Filename
5630480
Link To Document