针对极限学习机算法采用固定激活函数以及残差无法修正等缺陷,提出了基于残差预测修正的可调激活函数极限学习机算法(RV-ELM)。在学习过程中,采用粒子群优化算法选择陡度、位置和映射范围等参数,并使用ARMA模型对可调激活函数极限学习机(V-ELM)的预测值与实际值之间构成的残差序列进行建模、修正V-ELM的预测值。通过基准数据集仿真实验,验证了方法的有效性和可行性;并将其应用于地空导弹生存能力预测中,获得了满意的结果。
In this paper, a new learning algorithm called variable activation function extreme learning machine based on residual prediction compensation (RV-ELM) is proposed to solve the problem that ELM algorithm uses fixed activation function and has not residual compensation. In the learning process, it uses particle swarm optimization algorithm to optimize the steep degree, position and mapping scope simultaneously, and the ARMA model is used to model the residual errors between actual value and prediction value of variable activation function extreme learning machine (V-ELM), and the prediction of residual errors is used to rectify the prediction value of V-ELM. Simulation experiment is performed to show the effectiveness and feasibility of this method for benchmark datasets. And it is utilized to develop a soft sensor model for the surface-to-air missile survivability, and the result was satisfied.
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