A recurrent neural network controller and learning algorithm for the on-line learning control of autonomous underwater vehicles

[1]  B. Pasik-Duncan,et al.  Adaptive Control , 1996, IEEE Control Systems.

[2]  Abhijit S. Pandya,et al.  An improved scheme for direct adaptive control of dynamical systems using backpropagation neural networks , 1995 .

[3]  Abhijit S. Pandya,et al.  A Stochastic Parallel Algorithm for Supervised Learning in Neural Networks (Special Issue on Neurocomputing) , 1994 .

[4]  J. Yuh,et al.  Control of underwater robotic vehicles , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

[5]  Junku Yuh,et al.  An intelligent control system for remotely operated vehicles , 1993 .

[6]  R Krishnapuram,et al.  Implementation of parallel thinning algorithms using recurrent neural networks , 1993, IEEE Trans. Neural Networks.

[7]  E. Micheli-Tzanakou,et al.  Unsupervised global optimization: applications on classification of handwritten digits and visual evoked potentials , 1992, [Proceedings] 1992 IEEE International Conference on Systems, Man, and Cybernetics.

[8]  Abhijit S. Pandya,et al.  On-line learning control of autonomous underwater vehicles using feedforward neural networks , 1992 .

[9]  Abhijit S. Pandya,et al.  Alopex algorithm for adaptive control of dynamical systems , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[10]  K. P. Unnikrishnan,et al.  Learning in connectionist networks using the Alopex algorithm , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[11]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[12]  Takayuki Yamada,et al.  Neural network controller characteristics with regard to adaptive control , 1992, IEEE Trans. Syst. Man Cybern..

[13]  A. Karakasoglu,et al.  Neural network-based identification and adaptive control of nonlinear systems: a novel dynamical network architecture and training policy , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[14]  A. S. Pandya,et al.  Alopex algorithm for training multilayer neural networks , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[15]  Kumpati S. Narendra,et al.  Gradient methods for the optimization of dynamical systems containing neural networks , 1991, IEEE Trans. Neural Networks.

[16]  Weiping Li,et al.  Applied Nonlinear Control , 1991 .

[17]  W. T. Miller,et al.  CMAC: an associative neural network alternative to backpropagation , 1990, Proc. IEEE.

[18]  K. R. Goheen,et al.  On the adaptive control of remotely operated underwater vehicles , 1990 .

[19]  Junku Yuh,et al.  A neural net controller for underwater robotic vehicles , 1990 .

[20]  Abhijit S. Pandya,et al.  SIMD architecture for the Alopex neural network , 1990, Other Conferences.

[21]  Tamaki Ura,et al.  Development of motion control system for AUV using neural nets , 1990, Symposium on Autonomous Underwater Vehicle Technology.

[22]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[23]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[24]  Michael I. Jordan Attractor dynamics and parallelism in a connectionist sequential machine , 1990 .

[25]  Richard S. Sutton,et al.  Neural networks for control , 1990 .

[26]  R.M. Sanner,et al.  Neuromorphic pitch attitude regulation of an underwater telerobot , 1989, IEEE Control Systems Magazine.

[27]  W. Thomas Miller,et al.  Real-time application of neural networks for sensor-based control of robots with vision , 1989, IEEE Trans. Syst. Man Cybern..

[28]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[29]  Ronald J. Williams,et al.  A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.

[30]  Anuradha M. Annaswamy,et al.  Stable Adaptive Systems , 1989 .

[31]  D C Ricks,et al.  A PROJECT TO DEVELOP AND TEST LAYERED CONTROL SYSTEMS FOR AUTONOMOUS UNDERWATER VEHICLES , 1989 .

[32]  L. B. Lmeida Backpropagation in perceptrons with feedback , 1988 .

[33]  E. Harth,et al.  Dynamics of Alopex Process: Application to Optimization Problems , 1988 .

[34]  Fernando J. Pineda,et al.  GENERALIZATION OF BACKPROPAGATION TO RECURRENT AND HIGH-ORDER NETWORKS. , 1987 .

[35]  Pineda,et al.  Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.

[36]  E Harth,et al.  The inversion of sensory processing by feedback pathways: a model of visual cognitive functions. , 1987, Science.

[37]  D. R. Broome,et al.  ROBUST SELF-DESIGNING CONTROLLERS FOR UNDERWATER VEHICLES , 1987 .

[38]  Hagen Schempf,et al.  Robust trajectory control of underwater vehicles , 1985, Proceedings of the 1985 4th International Symposium on Unmanned Untethered Submersible Technology.

[39]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[40]  J. Feldman,et al.  DTNSRDC Revised Standarrd Submarine Equations of Motion , 1979 .

[41]  E Harth,et al.  Alopex: a stochastic method for determining visual receptive fields. , 1974, Vision research.

[42]  Bernard Widrow,et al.  Punish/Reward: Learning with a Critic in Adaptive Threshold Systems , 1973, IEEE Trans. Syst. Man Cybern..

[43]  J. Albus A Theory of Cerebellar Function , 1971 .