基于正则化RBF神经网络的软测量技术及其在质量预测中的应用(英文)

Regularized RBF Network-Based Inferential Sensor and Its Application in Product Quality Prediction

神经网络对噪声污染数据的过拟合是模型设计中主要考虑的问题。将Tiknonov正则化方法用于RBF神经元网络的设计,在网络学习中将正交最小二乘与前向选择相结合进行网络参数的估计,通过k均值聚类算法获得网络中心,采用L-曲线方法进行正则参数估计,并将该正则化RBF网络用于气体分馏装置产品质量的预测。仿真结果表明,该模型简单易行,并具有较快的计算速度和较好的泛化能力。

The risk of overfitting on noisy data is of major concern in neural network design. Regularization provides a stable solution to function approximation with a tradeoff between accuracy and smoothness of the solutions, k-means cluster algorithm is applied to determine the network centers at first and an approach based on L-curve is then proposed to estimate regularization parameter. These estimations are conbined with forward selection to update network parameters in training. Simulation results show that RBFN with a suitable regularization parameter can get a good generalization.