一种基于量子粒子群优化的极限学习机（英文）
发布时间：2018-09-17 16:11
【摘要】：极限学习机(ELM)是一种新型的单隐含层神经网络的训练方法,同传统的基于梯度的网络训练方法相比,具有快速的学习速度和更好的泛化性能。ELM在实际应用中往往需要大量的隐含层神经元,由于随机设定输入权值和偏置值,容易导致病态问题的出现。为解决上述问题,提出一种应用量子粒子群(QPSO)优化包括隐含层节点个数在内的网络参数的方法。这种优化基于验证集的均方根误差,考虑到了输入权值矩阵的范数。在典型的回归和分类问题上进行试验证明了算法的有效性。
[Abstract]:Extreme learning machine (ELM) is a new training method of single hidden layer neural network, which is compared with the traditional training method based on gradient. ELM has fast learning speed and better generalization performance. In practical applications, a large number of hidden layer neurons are often required. Due to random input weights and bias values, pathological problems may occur. In order to solve the above problems, a method of optimizing the network parameters including the number of hidden layer nodes by using quantum particle swarm optimization (QPSO) is proposed. This optimization is based on the root mean square error of the verification set and takes into account the norm of the input weight matrix. Experiments on typical regression and classification problems demonstrate the effectiveness of the algorithm.
【作者单位】： 鲁东大学信息与电气工程学院;海军航空工程学院飞行器工程系;
【基金】：National Natural Science Foundation of China(61602229) Natural Science Foundation of Shandong Province(ZR2016FQ19)
【分类号】：TP18
，
本文编号：2246446
[Abstract]:Extreme learning machine (ELM) is a new training method of single hidden layer neural network, which is compared with the traditional training method based on gradient. ELM has fast learning speed and better generalization performance. In practical applications, a large number of hidden layer neurons are often required. Due to random input weights and bias values, pathological problems may occur. In order to solve the above problems, a method of optimizing the network parameters including the number of hidden layer nodes by using quantum particle swarm optimization (QPSO) is proposed. This optimization is based on the root mean square error of the verification set and takes into account the norm of the input weight matrix. Experiments on typical regression and classification problems demonstrate the effectiveness of the algorithm.
【作者单位】： 鲁东大学信息与电气工程学院;海军航空工程学院飞行器工程系;
【基金】：National Natural Science Foundation of China(61602229) Natural Science Foundation of Shandong Province(ZR2016FQ19)
【分类号】：TP18
，
本文编号：2246446
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