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一种改进的RAN学习算法

, PP. 220-226

Keywords: RAN学习算法,径向基函数,隐层神经元,GivensQR分解,删除策略

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Abstract:

提出一种资源分配网络(ResourceAllocatingNetwork,RAN)的新的学习算法,称为IRAN算法.该算法通过一个包含4部分的新颖性准则来增加网络中的隐层神经元,通过误差下降速率来删除冗余神经元并采用基于GivensQR分解的递归最小二乘算法进行输出层权值的更新.通过函数逼近领域中2个Benchmark问题的仿真结果表明,与RAN,RANEKF,MRAN算法相比,IRAN算法不但学习速度快,而且可以得到更为精简的网络结构.

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