Neural network methods to solve the Lane–Emden type equations arising in thermodynamic studies of the spherical gas cloud model
作者: Iftikhar AhmadMuhammad Asif Zahoor RajaMuhammad BilalFarooq Ashraf
作者单位: 1University of Gujrat
2COMSATS Institute of Information Technology
3University of Malaysia Pahang
刊名: Neural Computing and Applications, 2017, Vol.28 (1), pp.929-944
来源数据库: Springer Nature Journal
DOI: 10.1007/s00521-016-2400-y
关键词: Artificial neural networksNonlinear singular systemActive-set methodInterior-point methodSequential quadratic programmingIntelligent computingThermodynamics studies
原始语种摘要: In the present study, stochastic numerical computing approach is developed by applying artificial neural networks (ANNs) to compute the solution of Lane–Emden type boundary value problems arising in thermodynamic studies of the spherical gas cloud model. ANNs are used in an unsupervised manner to construct the energy function of the system model. Strength of efficient local optimization procedures based on active-set (AS), interior-point (IP) and sequential quadratic programming (SQP) algorithms is used to optimize the energy functions. The performance of all three design methodologies ANN-AS, ANN-IP and ANN-SQP is evaluated on different nonlinear singular systems. The effectiveness of the proposed schemes in terms of accuracy and convergence is established from the results of statistical...
全文获取路径: Springer Nature  (合作)
影响因子:1.168 (2012)

  • spherical 球的
  • singular 奇异的
  • solve 
  • model 模型
  • point 
  • construct 建设
  • programming 程序设计
  • efficient 有用的
  • neural 神经系统的
  • sequential 连续的