Nonlinear system modelling via optimal design of neural trees.
This paper introduces a flexible neural tree model. The model is computed as a flexible multi-layer feed-forward neural network. A hybrid learning/evolutionary approach to automatically optimize the neural tree model is also proposed. The approach includes a modified probabilistic incremental program evolution algorithm (MPIPE) to evolve and determine a optimal structure of the neural tree and a parameter learning algorithm to optimize the free parameters embedded in the neural tree. The performance and effectiveness of the proposed method are evaluated using function approximation, time series prediction and system identification problems and compared with the related methods.
Chen
Yuehui
Y
School of Information science and Engineering, Jinan University, Jiwei Road 106, Jinan, 250022 P. R. China. yhchen@ujn.edu.cn
Yang
Bo
B
Dong
Jiwen
J
D000465:Algorithms; D001185:Artificial Intelligence; D005075:Biological Evolution; D003663:Decision Trees; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics