化工学报2019，Vol.70Issue(12)：4710-4721,12.DOI:10.11949/0438-1157.20190635
优化增量型随机权神经网络及应用
Optimized incremental random vector functional-link networks and its application
摘要
Abstract
Aiming at the problem that network parameters are difficult to be optimally determined, the model convergence speed is slow and the structure is complex in the traditional incremental random vector functional-link networks (I-RVFLNs), an optimized incremental random vector functional-link networks algorithm, namely O-I-RVFLNs, is proposed. Different from the traditional I-RVFLNs, the proposed O-I-RVFLNs algorithm sets a desired residual error vector, and then selects the input weights and biases that can reach or less than the expected residual error as the input parameters of the node each time a hiddennode is added, thereby improving the convergence rate of the network. In addition, considering that the modeling error of the algorithm is smaller and smaller and the downward trend is less obvious in the process of continuous iteration updating, the RMSE difference between adjacent iterations of each index parameter is considered in the termination condition of the algorithm, and the corresponding convergence criteria are formulated by referring to the Western Electricity Rules in statistical process control. Finally, based on UCI energy efficiency data and actual blast furnace industrial data, the proposed O-I-RVFLNs algorithm is verified and applied. The results show that compared with other RVFLNs algorithms, the data model built by the proposed algorithm can obtain more compact network structure, better generalization performance and prediction accuracy.关键词
神经网络/过拟合/高炉炼铁/优化/动态建模Key words
neural networks/ overfitting/ blast furnace ironmaking/ optimization/ dynamic modeling分类
化学化工引用本文复制引用
姜乐,周平..优化增量型随机权神经网络及应用[J].化工学报,2019,70(12):4710-4721,12.基金项目
国家自然科学基金项目(61890934, 61473064, 61790572, 61890930) （61890934, 61473064, 61790572, 61890930）
中央高校基本科研业务费专项资金(N180802003) （N180802003）
矿冶过程自动控制技术国家(北京市)重点实验室开放课题(BGRIMM-KZSKL-2017-04) （北京市）
