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使用人工神经网络求解系数包含测量误差的方程的方法。

The method of solution of equations with coefficients that contain measurement errors, using artificial neural network.

作者信息

Zajkowski Konrad

机构信息

Division of Electrotechnics and Electronics, Technical University of Koszalin, 15-17 Raclawicka St., 75-620 Koszalin, Poland.

出版信息

Neural Comput Appl. 2014;24(2):431-439. doi: 10.1007/s00521-012-1239-0. Epub 2012 Nov 2.

DOI:10.1007/s00521-012-1239-0
PMID:26316677
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4544578/
Abstract

This paper presents an algorithm for solving -equations of -unknowns. This algorithm allows to determine the solution in a situation where coefficients in equations are burdened with measurement errors. For some values of (where  = 1,…, ), there is no inverse function of input equations. In this case, it is impossible to determine the solution of equations of classical methods.

摘要

本文提出了一种求解含未知量的 - 方程的算法。该算法能够在方程系数存在测量误差的情况下确定解。对于某些 的值（其中 = 1, …, ），输入方程不存在反函数。在这种情况下，用经典方法无法确定方程的解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/0e9a2853520c/521_2012_1239_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/0cf50ca8fe91/521_2012_1239_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/23c9ca8c94ad/521_2012_1239_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/d3a662de8224/521_2012_1239_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/f5f6cd58182f/521_2012_1239_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/c7b3ca9486ae/521_2012_1239_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/5b746316ee1e/521_2012_1239_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/917a162e9ff5/521_2012_1239_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/4680dd803c57/521_2012_1239_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/b1d20e73c842/521_2012_1239_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/1f138335ab6e/521_2012_1239_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/782f9031012a/521_2012_1239_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/0e9a2853520c/521_2012_1239_Fig13_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/0cf50ca8fe91/521_2012_1239_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/23c9ca8c94ad/521_2012_1239_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/d3a662de8224/521_2012_1239_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/f5f6cd58182f/521_2012_1239_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/c7b3ca9486ae/521_2012_1239_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/5b746316ee1e/521_2012_1239_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/917a162e9ff5/521_2012_1239_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/4680dd803c57/521_2012_1239_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/b1d20e73c842/521_2012_1239_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/1f138335ab6e/521_2012_1239_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/782f9031012a/521_2012_1239_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f1c6/4544578/0e9a2853520c/521_2012_1239_Fig13_HTML.jpg

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