Title :
An improvement of artificial neural network and the comparison with the previous
Author :
Qin, Zunyang ; Hu, Yikun
Author_Institution :
Comput. Sci. & Technol, South China Univ. of Technol., Guangzhou, China
Abstract :
Recently, artificial intelligence plays a more and more important role in our study and daily lives. People use it to forecast or make the best decisions. Artificial neural network (ANN) is the most important model in the intelligence forecast. However, the model is not satisfying enough that the accuracy is just 80%-92%. So we need to strengthen the model to make it do a better job. In this paper, we come up with a way to improve the artificial neural network, through which users can do forecasting, classifying and other work more exactly. To implement this model, we add a new parameter to the activation function so that the whole model may function more accurately. And at last, to test the improvement of the model we have got, we do an additional experiment in order that the model makes the accuracy rise by 0.5%.
Keywords :
forecasting theory; neural nets; transfer functions; activation function; artificial intelligence; artificial neural network; intelligence forecast; Accuracy; Artificial neural networks; Brain modeling; Computer science; Humans; Predictive models; Training; Activation function; Artificial neural network;
Conference_Titel :
Computer Science and Automation Engineering (CSAE), 2012 IEEE International Conference on
Conference_Location :
Zhangjiajie
Print_ISBN :
978-1-4673-0088-9
DOI :
10.1109/CSAE.2012.6272684