A neural network based factorization model for polynomials in several elements
De-Shuang Huang, Zhao Mingsheng · 2002
This paper proposes a new neural network based factorization model, which can perform factorization on polynomials in several elements using a one-layered linear neural network model extended by a difference-product unit. This model is of properties easily trained and simply structured. However, the numbers of the input nodes and the output nodes of the designed networks based on this model depend on the orders of the factorized polynomials. Finally, several given examples show that the proposed model is effective and practical.