NDLI: Finding multiple real roots by neural networks based on complete discrimination system of polynomial

Content Provider	IEEE Xplore Digital Library
Author	Xinli Zhang Dayong Zhu Wang Hu
Copyright Year	2008
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu (Dayong Zhu; Wang Hu) \|\| Dept. of Comput. Sci., Chengdu Univ. of Inf. Technol., Chengdu (Xinli Zhang)
Abstract	A new method of solving the multiple real roots of polynomial by neural networks is proposed in this paper. This method combines the symbolic method with the numerical method. Based on the complete discrimination system of polynomial, the number and multiplicities of the distinct real roots of polynomials can be explicit determined. According to the number of the distinct real roots, a neural networks model for finding the multiple real roots of polynomial is established. From the description of the new model, it is not difficult to find that the existent neural networks for finding real roots of polynomial is the special case of the new one, where all of the real roots are treated as different values. Through training the new model by the gradient descent method, the approximate real roots of polynomial can be obtained. From the simulation results, it is shown that, comparing to the existent neural networks of finding real roots, the new method is not only more effective, but also can avoid the inequality between two or more equal real roots after finishing to solve the polynomial.
Starting Page	236
Ending Page	241
File Size	356782
Page Count	6
File Format	PDF
ISBN	9781424416738
DOI	10.1109/ICCIS.2008.4670918
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Polynomials Equations Computer networks Finishing Intelligent systems Computational intelligence Intelligent networks Laboratories Computer science
Content Type	Text
Resource Type	Article

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