Research on using genetic algorithms to optimize Elman neural networks
Research on using genetic algorithms to optimize Elman neural networks
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利用遗传算法优化Elman神经网络的研究
DOI：
10.1007/s00521-012-0896-3
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发表时间：
2013-08-01
影响因子：
6
通讯作者：
Jia, Weikuan
中科院分区：
文献类型：
--
作者：
Ding, Shifei;Zhang, Yanan;Jia, Weikuan
Elman神经网络在处理非线性复杂数据时具有动态映射功能。由于Elman神经网络在一定程度上继承了反向传播神经网络的特点，但它存在着易陷入局部极小值、学习速率固定、隐层神经元个数不确定等缺陷，影响了处理精度。因此，我们用遗传算法优化Elman网络的权值、阈值和隐层神经元数目。提高了Elman神经网络的训练速度和泛化能力，得到了最优的算法模型。实例分析表明，新算法在收敛速度、预测值误差、成功训练次数等方面均上级传统模型，表明了新算法的效果，值得进一步推广。
There is a function of dynamic mapping when processing non-linear complex data with Elman neural networks. Because Elman neural network inherits the feature of back-propagation neural network to some extent, it has many defects; for example, it is easy to fall into local minimum, the fixed learning rate, the uncertain number of hidden layer neuron and so on. It affects the processing accuracy. So we optimize the weights, thresholds and numbers of hidden layer neurons of Elman networks by genetic algorithm. It improves training speed and generalization ability of Elman neural networks to get the optimal algorithm model. It has been proved by instance analysis that new algorithm was superior to the traditional model in terms of convergence rate, predicted value error, number of trainings conducted successfully, etc. It indicates the effect of the new algorithm and deserves further popularization.