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
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;
Conference_Titel :
Electrical and Control Engineering (ICECE), 2010 International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-6880-5
DOI :
10.1109/iCECE.2010.442