Total Least Squares Approach to Modeling: A Matlab Toolbox
Total Least Squares Approach to Modeling: A Matlab Toolbox
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发表时间:
2010
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通讯作者:
D. Bednárová
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作者:
D. Bednárová
This paper deals with a mathematical method known as total least squares or orthogonal regression or error-in-variables method. The mentioned method can be used for modeling of static and also dynamic processes. A wide area of other applications is in signal and image processing. We also present a Matlab toolbox which can solve basic problems related to the total least squares method in the modeling. Several illustrative examples are presented as well. In this paper we present the total least squares method (TLS), which is also known as error-in-variables method or orthogonal regression method. One could hardly name another method which is used as frequently as the method which is known as the least squares method. At the same time, it is difficult to name another method which was accompanied by such strong and long lasting controversy as the least squares method. It is also difficult to find another method, which is so easy and at the same time as artificial as the least squares method. The story of the birth of the least squares method is well covered in the literature and can be summarized as follows [4, 6, 12, 17]. The priority in publication definitely belongs to A. M. Legendre (1805), who also gave the method its famous name, but C. F. Gauss (1809) claimed that he knew and used this method much earlier, about 10 years before the Legendre's publications. Gauss's arguments for his priority were not perfect at all. His diaries with computations claimed to be made by the least squares method were lost. It is also known that when H. C. Schumacher suggested repeating Gauss's lost computations claimed to be done by the least squares method, Gauss totally rejected this idea with the words that such attempts would only suggest that he could not be trusted.