Title :
A local linear modeling paradigm with a modified counterpropagation network
Author :
Cho, Jeongho ; Principe, Jose C. ; Motter, Mark A.
Author_Institution :
Comput. NeuroEngineering Lab., Florida Univ., Gainesville, FL, USA
Abstract :
The counter-propagation neural network (CPN) was selected to investigate the modeling problems because it integrates both supervised and unsupervised learning. The network is taught to have clusters that are described by codebook vectors in the training phase. The basic CPN algorithm is modified incorporating the local linear models (LLMs) to provide functional mappings and identify potentially nonlinear plants. The objective is a reduction of the approximation error in a CPN. In this framework the quantization error in the output space serves as a basis for the LLMs in the output space. The performance of the proposed algorithms is tested on the nonlinear dynamic system and shows the influence of this modification on the system identification quality.
Keywords :
nonlinear dynamical systems; self-organising feature maps; unsupervised learning; approximation errors; codebook vectors; counter-propagation neural network; functional mappings; local linear models; nonlinear dynamic system; quantization error; supervised learning; unsupervised learning; Approximation error; Buildings; Computer networks; Function approximation; Neural networks; Nonlinear dynamical systems; Predictive models; State-space methods; Topology; Vectors;
Conference_Titel :
Neural Networks, 2003. Proceedings of the International Joint Conference on
Print_ISBN :
0-7803-7898-9
DOI :
10.1109/IJCNN.2003.1223266
