CAJ | 학술논문

为提高煤灰熔点的预测精度,提出了一种基于构造-剪枝混合优化RBF网络的煤灰熔点预测方法.该方法融合了神经网络构造算法和剪枝算法的优点,分为“粗调”和“精调”2个阶段.粗调阶段动态增加隐节点数目直至满足相应的停止准则；精调阶段对粗调得到的RBF网络的结构和参数作进一步调整.基于煤灰的化学组成成分建立相应的构造-剪枝混合优化RBF网络预测煤灰熔点.预测结果表明:所建模型在具有较高精度的同时,具有较小的结构、较好的泛化能力和较强的鲁棒性.
위제고매회용점적예측정도,제출료일충기우구조-전지혼합우화RBF망락적매회용점예측방법.해방법융합료신경망락구조산법화전지산법적우점,분위“조조”화“정조”2개계단.조조계단동태증가은절점수목직지만족상응적정지준칙；정조계단대조조득도적RBF망락적결구화삼수작진일보조정.기우매회적화학조성성분건립상응적구조-전지혼합우화RBF망락예측매회용점.예측결과표명:소건모형재구유교고정도적동시,구유교소적결구、교호적범화능력화교강적로봉성.
A constructive-pruning hybrid method (CPHM) for radial basis function (RBF) networks is proposed to improve the prediction accuracy of ash fusion temperatures (AFT). The CPHM incorporates the advantages of the construction algorithm and the pruning algorithm of neural networks, and the training process of the CPHM is divided into two stages: rough tuning and fine tuning. In rough tuning, new hidden units are added to the current network until some performance index is satisfied. In fine tuning, the network structure and the model parameters are further adjusted. And, based on components of coal ash, a model using the CPHM is established to predict the AFT. The results show that the CPHM prediction model is characterized by its high precision, compact network structure, as well as strong generalization ability and robustness.