Fast automatic two-stage nonlinear model identification based on the extreme learning machine
Fast automatic two-stage nonlinear model identification based on the extreme learning machine
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DOI：
10.1016/j.neucom.2010.11.035
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发表时间：
2011-09
期刊：
影响因子：
6
通讯作者：
Jing Deng;Kang Li;G. Irwin
中科院分区：
文献类型：
--
作者：
Jing Deng;Kang Li;G. Irwin
用具有参数线性（LITP）结构的模型求解非线性问题是方便有效的。然而，每个模型项的非线性参数（例如，高斯函数的宽度）需要根据专家经验或通过穷举搜索来预先确定。另一种方法是通过基于梯度的技术（例如牛顿法）来优化它们。不幸的是，所有这些方法仍然需要大量的计算。近年来，极端学习机（ELM）在从数据中快速学习方面显示出其优势，但不能保证所构造模型的稀疏性。提出了一种基于极限学习机的非线性系统模型自动构造算法。这是通过有效地将ELM和留一法（LOO）交叉验证与我们的两阶段逐步构建程序[1]相结合来实现的。主要目的是提高ELM方法所构造模型的紧致性和推广能力。数值分析表明，该算法的计算量仅为正交最小二乘（OLS）方法的一半左右。仿真例子证实了所提出的技术的有效性和优越性。
It is convenient and effective to solve nonlinear problems with a model that has a linear-in-the-parameters (LITP) structure. However, the nonlinear parameters (e.g. the width of Gaussian function) of each model term needs to be pre-determined either from expert experience or through exhaustive search. An alternative approach is to optimize them by a gradient-based technique (e.g. Newton's method). Unfortunately, all of these methods still need a lot of computations. Recently, the extreme learning machine (ELM) has shown its advantages in terms of fast learning from data, but the sparsity of the constructed model cannot be guaranteed. This paper proposes a novel algorithm for automatic construction of a nonlinear system model based on the extreme learning machine. This is achieved by effectively integrating the ELM and leave-one-out (LOO) cross validation with our two-stage stepwise construction procedure [1]. The main objective is to improve the compactness and generalization capability of the model constructed by the ELM method. Numerical analysis shows that the proposed algorithm only involves about half of the computation of orthogonal least squares (OLS) based method. Simulation examples are included to confirm the efficacy and superiority of the proposed technique.