摘要
针对传统方法采用先训练后测试两阶段学习机制极易导致的过拟合或欠拟合问题,提出一种基于混合双层自组织径向基函数神经网络的优化学习(hybrid bilevel self-organizing radial basis function neural network optimization learning,Hb-SRBFNN-OL)算法。首先,将训练过程和测试过程集成到一个统一的框架中,规避过拟合或欠拟合问题。其次,基于进化学习机制,提出上下2层的交互式优化学习算法,上层基于网络复杂度和测试误差自组织调整网络结构,下层采用列文伯格-马夸尔特(Levenberg Marquardt,LM)算法作为优化器对自组织径向基函数神经网络(self-organizing radial basis function neural network,SO-RBFNN)的连接权值进行优化。最后,利用来自多个子网络的综合信息生成模型的最终输出,加速网络全局收敛。为验证所提方法的可行性,分别在多个分类和预测任务中进行了测试实验。结果表明,在与传统神经网络结构相似甚至更好的测试和分类精度下,该方法不仅能实现更快的训练收敛,而且能进化成更精简紧凑的径向基函数神经网络(radial basis function neural network,RBFNN)模型。尤其在污水处理过程中总磷的质量浓度预测实验中,测试集中均方根误差(root mean squared error,RMSE)最高可降低48.90%,实际场景实验结果验证了所提算法的精确性更佳且泛化能力更强。
Traditional methods adopt the two-stage learning mechanism of training before testing,which can easily lead to over fitting or under fitting.To solve this problem,an optimization learning algorithm based on hybrid bilevel self-organizing radial basis function neural network(Hb-SRBFNN-OL) was proposed.First,the training process and testing process were integrated into a unified framework to effectively balance the overfitting and underfitting problems.Second,an interactive learning algorithm with two layers was proposed.The upper layer organized and adjusted the network structure based on the network complexity and test error,and the lower layer used Levenberg Marquardt(LM) algorithm as the optimizer to optimize the connection weight of self-organizing radial basis function neural network(SO-RBFNN).Finally,the final output of the model was generated by using the linear combination information from multiple SO-RBFNNs to accelerate the global convergence of the network.To verify the feasibility of the proposed method in practical problems,and test experiments in multiple classification and prediction tasks were conducted,respectively.Results show that this method can not only achieve faster training convergence,but also produce a more concise and compact radial basis function neural network(RBFNN) model.In particular,in the prediction experiment of total phosphorus concentration in wastewater treatment process,the root mean squared error(RMSE) of the test set is reduced by up to 48.90%,achieving better accuracy and stronger generalization ability.
作者
杨彦霞
王普
高学金
高慧慧
齐泽洋
YANG Yanxia;WANG Pu;GAO Xuejin;GAO Huihui;QI Zeyang(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing University of Technology,Beijing 100124,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2024年第1期38-49,共12页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61640312,61803005)
北京市自然科学基金资助项目(4172007,4192011)。
