Research on RBFNN Modeling Based on ICA Feature Extraction

机译：基于ICA特征提取的RBFNN建模研究

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In order to accurately depict the complicated characteristics of nonlinear system by modeling, a Radial Basis Function Network (RBFNN) modeling method based on Independent Component Analysis (ICA) is proposed. First ICA is perform for extracting basic features of the training samples, and then the extracted basic features is used to establish to RBFNN model. The simulation indicates that, the hybrid modeling method proposed is better than that of another 2 methods with simple model structure, and is effective and feasible to establish for the nonlinear modeling system.

机译：为了准确地描绘通过建模的非线性系统的复杂特性，提出了一种基于独立分量分析（ICA）的径向基函数网络（RBFNN）建模方法。第一个ICA是为了提取训练样本的基本特征，然后提取的基本功能用于建立RBFNN模型。仿真表明，建议的混合建模方法优于一个简单的模型结构的另一种方法，并且为非线性建模系统建立有效且可行。

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《International Conference on Applied Mechanics and Manufacturing System》|2014年||共4页
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作者
Jie Liu; Ya-Rong Wu; Ke Qiao;
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中图分类 TH16-53;
关键词
Radial basis function neural network; Independent components analysis; Feature extraction; Nonlinear system;

机译：径向基函数神经网络;独立组分分析;特征提取;非线性系统;

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Research on RBFNN Modeling Based on ICA Feature Extraction

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