References
- Watanabe , K. and Tzafestas , S. G. 1990 . Learning algorithms for neural networks with the Kalman Filters . Journal of Intelligent Robotic Systems , 3 : 305 – 319 .
- Bertsekas , D. P. 1996 . Incremental least squares methods and the extended Kalman Filter . SIAM Journal of Optimization , 6 : 807 – 822 .
- Bertsekas , D. P. 1995 . Nonlinear Programming , Belmont, MA : Athena Scientific .
- Jetto , L. , Longhi , S. and Venturini , G. 1999 . Development and experimental validation of an adaptive extended Kalman Filter for the localization of mobile robots . IEEE Transactions on Robotics and Automation , 15 : 219 – 229 .
- Jang , J. -S.R. , Sun , C. -T. and Mizutani , E. 1997 . Neurofuzzy and soft-computing. A Computational Approach to Learning and Machine Intelligence , Englewood Cliffs, NJ : Prentice-Hall .
- Haykin , S. 1991 . Adaptive Filter Theory , Englewood Cliffs, NJ : Prentice-Hall .
- Chak , C. K. , Feng , G. and Mia , J. 1998 . An adaptive fuzzy neural network for MIMO system model approximation in high dimensional spaces . IEEE Transactions on Systems, Man and Cybernetics—Part B: Cybernetics , 28 : 436 – 446 .
- Luo , R. C. and Kay , M. G. 1989 . Multisensor integration and fusion in intelligent systems . IEEE Transactions on Systems, Man and Cybernetics , 19 : 901 – 931 .
- Kamen , E. W. and Su , J. K. 1999 . Introduction to Optimal Estimation , London : Springer .
- Rigatos , G. G. , Tzafestas , S. G. and Evangelidis , G. A. 2001 . IEE Proceedings: Control Theory and Applications 169 – 179 .
- Rigatos , G. G. 2003 . Fuzzy stochastic automata for intelligent vehicle control . IEEE Transactions on Industrial Electronics , 50 : 76 – 79 .
- Oriolo , G. , De Luca , A. and Vendittelli , M. 2002 . WMR control via dynamic feedback linearization: Design, implementation and experimental validation . IEEE Transactions on Control Systems Technology , 10 : 835 – 852 .