Nonlinear system identification by feedback GMDH-type neural network with architecture self-selecting function
Nonlinear system identification by feedback GMDH-type neural network with architecture self-selecting function
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DOI：
10.1109/isic.2010.5612889
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发表时间：
2010-10
期刊：
影响因子：
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通讯作者：
T. Kondo;J. Ueno
中科院分区：
文献类型：
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作者：
T. Kondo;J. Ueno
提出了反馈分组数据处理（GMDH）型神经网络算法，并将其应用于非线性系统辨识。在该反馈GMDH型神经网络算法中，从S形函数型神经网络、径向基函数（RBF）型神经网络和多项式型神经网络这三种类型的神经网络结构中自动选择最佳神经网络结构。此外，结构参数，如反馈回路的数量，在隐藏层中的神经元的数量和相关的输入变量被自动选择，以便最小化的预测误差标准定义为预测平方和（PSS）。辨识结果表明，反馈GMDH型神经网络算法是有用的非线性系统的识别，是理想的实际复杂问题，因为最佳的神经网络结构是自动组织。
The feedback Group Method of Data Handling (GMDH)-type neural network algorithm is proposed and is applied to the nonlinear system identification. In this feedback GMDH-type neural network algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures such as the sigmoid function type neural network, the radial basis function (RBF) type neural network and the polynomial type neural network. Furthermore, the structural parameters such as the number of feedback loops, the number of neurons in the hidden layers and the relevant input variables are automatically selected so as to minimize the prediction error criterion defined as Prediction Sum of Squares (PSS). The identification results show that the feedback GMDH-type neural network algorithm is useful for the nonlinear system identification and is ideal for practical complex problems since the optimum neural network architecture is automatically organized.