摘要
为了解决随机配置网络(stochastic configuration network,SCN)隐含层参数的选择与分配会影响其预测精度的问题,提出一种基于混沌反馈乌燕鸥优化算法(chaotic feedback sooty tern optimization algorithm,CFSTOA)的SCN参数优化方法。首先,利用Tent映射、线性因子调节策略、劣势种群反馈原则来改进乌燕鸥优化算法(sooty tern optimization algorithm,STOA),以增强算法的局部搜索能力,得到一种具备更快收敛速度和更高收敛精度的CFSTOA;然后,将CFSTOA用于优化SCN的正则化参数和权重偏差的尺度因子,从而得到最优的隐含层参数;最后,利用10个基准函数和4个标准回归数据集分别对CFSTOA的性能进行了测试。结果表明,CFSTOA具有更快的收敛速度且不易陷入局部最优,可以提高SCN算法的预测精度和训练速度。
To solve the problem that the selection and assignment of hidden layer parameters of stochastic configuration network(SCN)affects its prediction accuracy,a SCN parameter optimization method was proposed based on the chaotic feedback sooty tern optimization algorithm(CFSTOA).First,the Tent mapping,linear factor adjustment strategy and disadvantaged population feedback principle were used to improve the sooty tern optimization algorithm(STOA)to enhance the local search capability and to obtain a CFSTOA with faster convergence speed and higher convergence accuracy.Then,the CFSTOA was used to optimize the regularization parameter and the scale factor of weight biases of the SCN to obtain the optimal hidden layer parameters.Finally,the performance of the CFSTOA was tested through 10 benchmark functions and 4 standard regression datasets.Results show that the CFSTOA has a faster convergence speed and is less likely to fall into a local optimum,which can improve the prediction accuracy and training speed of the SCN algorithm.
作者
严爱军
于小
YAN Aijun;YU Xiao(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Engineering Research Center of Digital Community,Ministry of Education,Beijing 100124,China;Beijing Laboratory for Urban Mass Transit,Beijing 100124,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2023年第7期746-757,共12页
Journal of Beijing University of Technology
基金
国家自然科学基金资助项目(61873009,62073006)
北京市自然科学基金资助项目(4212032)。
