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Use of linearity of the Sokoloff Model to improve performance of non-linear search.

机译:使用Sokoloff模型的线性来提高非线性搜索的性能。

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SUMMARY: The three-parameter Sokoloff equation is used to measure the rates of glucose consumption in the brain in vivo. This equation depends linearly on one of its parameters, k1, which is responsible for the glucose transport from plasma to tissue. By equating to zero the first derivative of the minimization function with respect to k1, it is possible to express this parameter as a function of the other two and reduce the non-linear search from three to two dimensions. This approach was examined by the Nelder-Mead simplex method and the Levenberg-Marquardt algorithm. In both cases the process of convergence was more robust and required fewer iterations to achieve a given accuracy than the direct three-parameter non-linear search.
机译:简介:三参数索科洛夫方程用于测量体内大脑中葡萄糖的消耗速率。该方程线性地取决于其参数之一k1,该参数负责葡萄糖从血浆向组织的运输。通过使最小化函数相对于k1的一阶导数等于零,可以将此参数表示为其他二维函数的参数,并将非线性搜索从3维减少到2维。通过Nelder-Mead单纯形法和Levenberg-Marquardt算法检查了该方法。在这两种情况下,收敛过程都比直接三参数非线性搜索更鲁棒,并且需要更少的迭代次数即可达到给定的精度。

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