Polynomial Function Recurrent Neural Networks Model and Apply
Yong Zheng Zhou · Chinese Journal of Computers · 2003
A kind of polynomial function recurrent neural network (PFRNN) model is firstly proposed, which has characteristic of traditional RNN and the capability of function approximate. PFRNN is especially useful for recurrent computation problem. The PFRNN learning algorithm is also designed, which can perform approximate factorization of multivariate polynomials. This model has the properties such as easily trained and simply structured. However, the numbers of the hidden layer activation function are based on this model of the orders of the factorized polynomials. Finally, several examples and learning algorithm show that the proposed model is effective and practical. The learning algorithm is convergent quickly and stable, which can approximately calculate every polynomial factor. The results obtained in this paper are very important for study algebra symbolic approximate computation.