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.

Key words: neural networks, overfitting, blast furnace ironmaking, optimization, dynamic modeling

摘要：

针对传统增量型随机权神经网络(I-RVFLNs)存在网络参数难以优化确定、模型收敛速度慢和结构复杂的问题，提出一种优化增量型随机权神经网络算法，即O-I-RVFLNs。与传统I-RVFLNs不同，所提O-I-RVFLNs算法首先设定了一个期望的建模残差向量，然后在每次新增隐层节点时，选择可以达到或小于此节点期望残差的输入权值和偏置作为该节点的输入参数，进而提高网络的收敛速度。除此之外，考虑到算法在不断迭代更新过程中建模误差越来越小，下降趋势越来越不明显的问题，将各指标参数相邻两次迭代均方根误差的差值考虑在算法终止条件内，并借鉴统计过程控制中的西电规则制定了相应的算法收敛判定准则。最后，基于UCI能效数据和实际高炉工业数据，对所提O-I-RVFLNs算法进行了验证和应用。结果表明，相对于其他RVFLNs算法，所提算法建立的数据模型能够获得更紧凑的网络结构以及更好的泛化性能和预测精度。

关键词: 神经网络, 过拟合, 高炉炼铁, 优化, 动态建模