Gan Min, Chen Guang-Yong, Chen Long, Chen C L Philip
IEEE Trans Neural Netw Learn Syst. 2020 Feb;31(2):445-451. doi: 10.1109/TNNLS.2019.2904952. Epub 2019 Apr 11.
In this paper, we consider the term selection problem for a class of separable nonlinear models. The strategy is a two-step process in which the nonlinear parameters of the model are first optimized by a variable projection method, and then the least absolute shrinkage and selection operator are adopted to obtain a sparse solution by picking out the critical terms automatically. This process may be repeated several times. The proposed algorithm is tested on parameter estimation problems for an exponential model and a neural network-based model. The numerical results show that the proposed algorithm can pick out the appropriate terms from the overparameterized model and the obtained parsimonious model performs better than other methods.
在本文中，我们考虑一类可分离非线性模型的项选择问题。该策略是一个两步过程，其中首先通过变量投影法对模型的非线性参数进行优化，然后采用最小绝对收缩和选择算子通过自动挑选关键项来获得稀疏解。此过程可能会重复几次。所提出的算法在指数模型和基于神经网络的模型的参数估计问题上进行了测试。数值结果表明，所提出的算法能够从参数过多的模型中挑选出合适的项，并且所得到的简约模型比其他方法表现更好。
