Title :
Application of Improved Method Combined Elman NN with SVM in Tender Offer
Author :
Jing-min, Wang ; Xin-heng, Liang ; Zou, Yu
Author_Institution :
Econ. & Manage. Dept., North China Electr. Power Univ., Baoding, China
Abstract :
Traditional method of tender offer is subjective and arbitrary, and the ARIMA Accuracy can´t satisfy the tenderer. We have combined the Elman NN with the SVM model to establish a new hybrid optimization algorithm, which are presented to the bidding tender offer in a project. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature. we can draw the conclusion from the comparison between ENN and SVM network forecast that accuracy of the model is much higher than ARIMA.
Keywords :
financial management; neural nets; optimisation; support vector machines; ARIMA accuracy; Elman NN; SVM; hybrid optimization algorithm; tender offer; Artificial neural networks; Biological system modeling; Context modeling; Neurons; Optimization; Power systems; Support vector machines; Elman NN; Optimization algorithm; SVM; Tender offer;
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.2010.120