Self-Adaptive Evolutionary Extreme Learning Machine
Self-Adaptive Evolutionary Extreme Learning Machine
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DOI：
10.1007/s11063-012-9236-y
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发表时间：
2012-12-01
影响因子：
3.1
通讯作者：
Huang, Guang-Bin
中科院分区：
文献类型：
--
作者：
Cao, Jiuwen;Lin, Zhiping;Huang, Guang-Bin
本文提出了一种改进的单隐层前向网络学习算法--自适应进化极端学习机（SaE-ELM）。在SaE-ELM中，网络隐节点参数采用自适应差分进化算法进行优化，其试探向量生成策略及其相关控制参数在策略池中通过学习以往生成有希望解的经验进行自适应，网络输出权值采用Moore-Penrose广义逆计算. SaE-ELM优于进化极端学习机（E-ELM）和不同的进化Levenberg-Marquardt方法，因为它可以自适应地确定合适的控制参数和发电策略。仿真结果表明，SaE-ELM不仅在几种人工选择发电策略和控制参数的情况下性能优于E-ELM，而且其泛化性能也优于几种相关方法。
In this paper, we propose an improved learning algorithm named self-adaptive evolutionary extreme learning machine (SaE-ELM) for single hidden layer feedforward networks (SLFNs). In SaE-ELM, the network hidden node parameters are optimized by the self-adaptive differential evolution algorithm, whose trial vector generation strategies and their associated control parameters are self-adapted in a strategy pool by learning from their previous experiences in generating promising solutions, and the network output weights are calculated using the Moore-Penrose generalized inverse. SaE-ELM outperforms the evolutionary extreme learning machine (E-ELM) and the different evolutionary Levenberg-Marquardt method in general as it could self-adaptively determine the suitable control parameters and generation strategies involved in DE. Simulations have shown that SaE-ELM not only performs better than E-ELM with several manually choosing generation strategies and control parameters but also obtains better generalization performances than several related methods.