Research on structure dynamic neural networks Online publication date: Thu, 01-Apr-2010
by Hong-gui Han, Jun-fei Qiao, Xin-yuan Li
International Journal of Modelling, Identification and Control (IJMIC), Vol. 9, No. 1/2, 2010
Abstract: A model of structure dynamic neural network, which simulates the learning skills such as human beings and animals, is proposed in this paper. This model contains two main steps: 1) the structure learning phase possesses the ability of online generation and ensures the number of the neural nodes of the neural network; 2) the parameter learning phase adjusts the interconnection weights of neural network to achieve favourable approximation performance. The structure learning algorithm consists of growing and pruning methods, and then, the Lyapunov stability theory is used to analyse the stability of this new algorithm. Finally, this new dynamic neural network is used to track the non-linear functions; simulation results show that this new algorithm can achieve favourable performance.
Online publication date: Thu, 01-Apr-2010
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Modelling, Identification and Control (IJMIC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com