| Title |
A Study on the Optimal Design of Polynomial Neural Networks Structure |
| Authors |
오성권(O, Seong-Gwon) ; 김동원(Kim, Dong-Won) ; 박병준(Park, Byeong-Jun) |
| Keywords |
PNN(Polynomial Neural Networks) ; GMDH(Group Method of Data Handing) ; high-order polynomial ; multi-variable inputs ; time series data ; approximation and prediction capabilities |
| Abstract |
In this paper, we propose a new methodology which includes the optimal design procedure of Polynomial Neural Networks(PNN) structure for model identification of complex and nonlinear system. The proposed PNN algorithm is based on GMDA(Group Method of Data handling) method and its structure is similar to Neural Networks. But the structure of PNN is not fixed like in conventional Neural Networks and can be generated. The each node of PNN structure uses several types of high-order polynomial such as linear, quadratic and cubic, and is connected as various kinds of multi-variable inputs. In other words, the PNN uses high-order polynomial as extended type besides quadratic polynomial used in GMDH, and the number of input of its node in each layer depends on that of variables used in the polynomial. The design procedure to obtain an optimal model structure utilizing PNN algorithm is shown in each stage. The study is illustrated with the aid of pH neutralization process data besides representative time series data for gas furnace process used widely for performance comparison, and shows that the proposed PNN algorithm can produce the model with higher accuracy than previous other works. And performance index related to approximation and prediction capabilities of model is evaluated and also discussed. |