THE SELF-ORGANIZING POLYNOMIAL NETWORK ALGORITHM BASED ON THE HYPERBOLOID FUNCTION LINK ARTIFICIAL NEURAL NETWORKS

In this paper, a new hyperboloid function link artificial neural network（HFLANN） is presented, and a kind of hierarchical HFLANN is designed, and a learning algorithm is presented. The algorithm shows stronger superiority on nonliner fitting, which can approximate a given Volterra series. The algorithm has better precision compared with GMDH algorithm and SOP algorithm. The main advantage of fitting is always based on hyperboloid function transformation. The algorithm is superior to the GMDH algorithm in randomly choosing partial ploynomials and the algorithm is superior to the SOP algorithm in that designing SOP networks needs not too many hidden layers, thus accelerating the learning process and improving the approximate quality of neural network. HHFLANN is very suitable for application domains with hierarchical structures.