Title :
Sample selection and training of self-organizing map neural network in multiple models approximation
Author :
Gao, Dayuan ; Zhu, Hai ; Liu, Xijing ; Wang, Chao
Author_Institution :
Dept. of Navig. & Commun., Navy Submarine Acad., Qingdao, China
Abstract :
The self-organizing map (SOM) neural network has been used widely in multiple models approximation (MMA). However, the clustering property of SOM may not be fit for MMA. This paper introduces the idea of active learning into the training of SOM, especially for MMA. The neural network selects actively the training samples according to the approximation error of local models. As a result, the distribution of the neural nodes is changed so that the performance of MMA is improved. The process of this training method and the performance improvement are illustrated by a simulation example.
Keywords :
approximation theory; learning (artificial intelligence); self-organising feature maps; MMA; SOM training; active learning; approximation error; clustering property; multiple models approximation; neural nodes distribution; self-organizing map neural network; Approximation error; Computational modeling; Data models; Neural networks; Training; Training data; Multiple Models Approximation; Neural Network; Self-Organizing Map;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1397-1
DOI :
10.1109/WCICA.2012.6358395
