Higher Order Neural Networks Learning by Extended Kalman Filter
Abstract
Keywords
Full Text:
PDFReferences
Ronghui Zhan and Jianwei Wan - Neural Network-Aided Adaptive Unscented Kalman Filter for Nonlinear State Estimation, ieee signal processing letters, vol. 13, no. 7,july 2006, pp 445-448.
R.N. Yadav ,Prem K. Kalra, Joseph John-, Neural network learning with generalized-mean based neuron model, Soft computing, Springer-Verlag vol-10 no-3,pp 257-263 :Feb 2006.
Agya Mishra, R.N. yadav,D. K. Trivedi-noise cancellor based on Generalized neural network, IEEE inter. Conference on Advanced communication technologies proceedings ICACT 2007,S.Korea pp- pp- 1998-2000,feb.2007.
Simon Haykin -Kalman Filtering and Neural Networks, John Wiley & Sons, Inc.2001
A. Yadav, D. Mishra, R.N. Yadav, S. Ray, P.K.Kalra-Time series prediction with single integrate-and–fire neuron, Applied soft computing, science direct, Elsevier, pp,245-252,2006
Gintaras V. Puskorius and Lee A. Feldkamp-Parameter based kalman filter training theory and implementation ,Kalman filtering and neural network, S. Haykin, wiley-iterscience pub,pp23-68,2001.
John Sum,Chi-Sing Leung, Gilbert,H. Young and Wing-kay Kan-On the Kalman Filtering Method in Neural network training and pruning, IEEE transuction on neural networks vol-10,no.1, pp 161-166,January 1999.
Felix Heimes, Extended kalman filter neural network Training: Experimental results and algorithm Improvements,0-7803-4778-1/98,IEEE,pp 1639-1644,1998.
Youji Iiguni, Hideaki Sakai and Hidekatsu Tokumaru,- A real-time learning algorithm for multilayered neural network based on the extended kalman filter,IEEE Transaction on signal processing vol.40,no4,pp 959-966,April 1992.
Ronald J. Williams,- Training Recurrent Networks using the Extended Kalman Filter,0-7803-0559-0/92,IEEE, IV,pp 241-246, 1992.
W.K.T. Fukuda and S.G. Tzafestas, Learning algorithm of layered neural networks via extended kalman filters, Int.J.Syst.Sci vol22, no.4, pp 753-768, 1991.
S.Singhal and L.Wu, Training Multilayered Perceptron with the Extended Kalman algorithm, in D.S. Tourelzky(ed) ,Advances in Neural Information Processing system I,pp133-140,1989.
KDeergha , Rao, M.N.S.Swamy, E.I.Plotkin -complex ekf neural network for adaptive equalization , IEEE International Symposium on Circuits and Systems ISCAS 2000, , 28-31, Geneva, Switzerland, pp-11-349-352, May 2000.
Herbert Jaeger A tutorial on training recurring neural networks, covering BPPT,RTRL,EKF and the „echo state network approach‟, International university of Bermen,pp 17-33,2005.
R. N. Lobbia, S. C. Stubberud, and M. W. Owen, “Adaptive extended Kalman filter using artificial neural networks,” Int. J. Smart Eng. Syst. Des., vol. 1, pp. 207–221, 1998.
Iulian B. Ciocoiu ,RBF network training using a dual extended kalman filter, Neurocomputing 48, Elsevier,pp-602-622,2002.
Dan Simon, Training radial basis neural networks with the extended kalman filter, Neurocomputing 48, Elsevier, pp 455-475,2002.
A. Alessandri, M. Cuneo, S. Pagnan, M. Sanguineti, On the convergence of EKF-based parameters optimization for neural Networks, proceeding IEEE conference on decision and control USA, pp 6181-6186, December 2003.
Greg Welch, Gary Bishop, An introduction to kalman filter ,Department of computer science university of N. carolina at Chapel Hill, pp 1-16,2006
Michael C. Nechyba and Yangsheng Xu, Cascade Neural Networks with Node-Decoupled Extended Kalman Filtering, IEEE,pp 214-218, Pitsburgh, 1997.
Chuan Wang and Jose C. Principe, Training Neural Networks with Additive Noise in the Desired Signal, tutorials 1999.
Herbert Jaeger A tutorial on training recurrent neural networks, covering BPPT , RTRL, EKF and the echo state network approach international university Bremen ,2005.
L.A Feldkamp and G.V. Puskorius, A signal processing frame work based on dynamic neural networks with application to problems in adaptation, filtering and classification, proceedings of the IEEE,86,pp 2259-2277,1998.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.