基于粒子滤波的神经网络学习算法

[1] Sequential Monte Carlo Methods for Optimisation ofNeural Network Models. JFG de Freitas,Niranjan M,Gee AH,Doucet A. http://www-sigproc.eng.cam.ac.uk/smc/papers.html,2002-12-06/ . 2005

[2] Extended Kalman filter learning al-gorithm for hyper-complex multi-layered neural net-works. Rughooputh H. IEEE Proc . 1999

[3] Issues in Bayesian analysis ofneural network models. Miiller P,Insua R. Neural Computation . 1998

[4] Sequential Monte Carlo formodel selection and estimation of neural networks. Andrieu C,,Freitas N D. IEEE International Conference on Acoustics Speech and Signal Processing Proceedings . 2000

[5] Novel ap-proach to nonlinear/non-Gussian Bayesian state esti-mation. Gordon N J,,Salmond D J,Smith A F M. IEEE Proc.F,Radar Signal Process . 1993

[6] Bayesian Learning for Neural Networks. Real R M. Lecture Notes in Statistics . 1996

[7] The Unscented Particle Fil-ter. Rudolph van der Merwe. http://speech.bme.ogi.edu/publi-cations/ps/merwe00.pdf,2000-08-12 . 2005

[1] Sequential Monte Carlo Methods for Optimisation ofNeural Network Models. JFG de Freitas,Niranjan M,Gee AH,Doucet A. http://www-sigproc.eng.cam.ac.uk/smc/papers.html,2002-12-06/ . 2005

[2] Extended Kalman filter learning al-gorithm for hyper-complex multi-layered neural net-works. Rughooputh H. IEEE Proc . 1999

[3] Issues in Bayesian analysis ofneural network models. Miiller P,Insua R. Neural Computation . 1998

[4] Sequential Monte Carlo formodel selection and estimation of neural networks. Andrieu C,,Freitas N D. IEEE International Conference on Acoustics Speech and Signal Processing Proceedings . 2000

[5] Novel ap-proach to nonlinear/non-Gussian Bayesian state esti-mation. Gordon N J,,Salmond D J,Smith A F M. IEEE Proc.F,Radar Signal Process . 1993

[6] Bayesian Learning for Neural Networks. Real R M. Lecture Notes in Statistics . 1996

[7] The Unscented Particle Fil-ter. Rudolph van der Merwe. http://speech.bme.ogi.edu/publi-cations/ps/merwe00.pdf,2000-08-12 . 2005