Dilation method for finding close roots of polynomials based on constrained learning neural networks

被引:45
作者
Huang, DS
Ip, HHS
Chi, ZR
Wong, HS
机构
[1] Chinese Acad Sci, Hefei Inst Intelligent Machines, Hefei 230031, Anhui, Peoples R China
[2] City Univ Hong Kong, AlMech Ctr, Kowloon Tong, Hong Kong, Peoples R China
[3] City Univ Hong Kong, Dept Comp Sci, Kowloon Tong, Hong Kong, Peoples R China
[4] Hong Kong Polytech Univ, Ctr Multimedia Signal Proc, Hong Kong, Hong Kong, Peoples R China
关键词
feedforward neural networks; polynomials; close roots; dilation; root-finder; complex constrained learning algorithm;
D O I
10.1016/S0375-9601(03)00216-0
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
In finding roots of polynomials, often two or more roots that are close together in solution space are very difficult to be resolved by a root-finder. To solve this problem, this Letter proposes a dilation method to transform the positions of roots in space so that all roots in space are pulled further apart. As a result, those close (including complex) roots can be readily resolved efficiently by a root-finder. In addition, in this Letter a complex version of constrained learning algorithm is derived. Moreover, our previously proposing feedforward neural network (FNN) root-finder is adopted to address the root finding issue. Finally, some satisfactory results that support our approach are presented. (C) 2003 Elsevier Science B.V. All rights reserved.
引用
收藏
页码:443 / 451
页数:9
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