Title :
A neural network based factorization model for polynomials in several elements
Author :
Huang, De-Shuang ; Zhao, Mingsheng
Author_Institution :
Beijing Inst. of Syst. Eng., China
Abstract :
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
Keywords :
iterative methods; perceptrons; polynomials; difference-product unit; factorized polynomials; input nodes; neural network based factorization model; one-layered linear neural network model; output nodes; polynomials; Computer networks; Computer simulation; Data engineering; Equations; Feedforward neural networks; Intelligent networks; Merging; Neural networks; Polynomials; Systems engineering and theory;
Conference_Titel :
Signal Processing Proceedings, 2000. WCCC-ICSP 2000. 5th International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-5747-7
DOI :
10.1109/ICOSP.2000.893411
