基于Gaussian型RBF神经网络的一元函数逼近性能研究 |
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引用本文: | 丁硕,常晓恒,巫庆辉. 基于Gaussian型RBF神经网络的一元函数逼近性能研究[J]. 渤海大学学报(自然科学版), 2013, 0(3): 300-304 |
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作者姓名: | 丁硕 常晓恒 巫庆辉 |
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作者单位: | 渤海大学工学院,辽宁锦州121013 |
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基金项目: | 国家自然科学基金(No:61104071). |
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摘 要: | 为了研究Gaussian型RBF神经网络对于一元非线性函数的逼近能力,编程建立了Gaussian型RBF神经网络和BP神经网络,并以正弦函数、指数函数、阶跃函数三种典型的一元非线性函数为例,分别用两种神经网络对其进行逼近.仿真结果表明,相对于传统BP神经网络而言,Gaussian型RBF神经网络对于一元非线性函数的逼近精度更高、收敛速度更快,具有良好的逼近能力,为解决一元非线性函数的逼近问题提供了良好的解决手段.
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关 键 词: | Gaussian函数 RBF神经网络 BP神经网络 函数逼近 仿真 |
Study of approximation performance of simple function based on Gaussian- RBF neural networks |
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Affiliation: | Ding Shuo, Chang Xiao- heng, Wu Qing- hui (College of Engineering, Bohai University, Jinzhou 121013, China) |
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Abstract: | In order to study Gaussian - RBF neural networksj approximation ability of single - variable non- linearity function, Gaussian - RBF neural networks and BP neural networks are designed. And three typical sin- gle- variable nonlinearity functions, namely sine function, exponential function and step function are taken as examples to be approximated via two kinds of neutral networks. Simulation results show that for single - variable nonlinearity functions, Gaussian - RBF neural networks are superior to BP neural networks in approximation pre- cision, convergence rate as well as approximation performance. Thus they provide an ideal method for the solu- tion of single -variable nonlinearity function approximation. |
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Keywords: | Gaussian fun ction RBF neural networks BP reural networks Function approximation Simnlation |
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