Neural Networks for Control

This paper starts by placing neural net techniques in a general nonlinear control framework. After that, several basic theoretical results on networks are surveyed.

[1]  L. Mcbride,et al.  Optimization of time-varying systems , 1965 .

[2]  G. Pisier.  Remarques sur un résultat non publié de B. Maurey , 1981 .

[3]  David Haussler,et al.  Classifying learnable geometric concepts with the Vapnik-Chervonenkis dimension , 1986, STOC '86.

[4]  Ronald L. Rivest,et al.  Training a 3-node neural network is NP-complete , 1988, COLT '88.

[5]  Eduardo D. Sontag,et al.  Backpropagation separates when perceptrons do , 1989, International 1989 Joint Conference on Neural Networks.

[6]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[7]  R. Sutton,et al.  Connectionist Learning for Control: An Overview , 1989 .

[8]  Judy A. Franklin,et al.  Historical perspective and state of the art in connectionist learning control , 1989, Proceedings of the 28th IEEE Conference on Decision and Control,.

[9]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..

[10]  S. M. Carroll,et al.  Construction of neural nets using the radon transform , 1989, International 1989 Joint Conference on Neural Networks.

[11]  Mahesan Niranjan,et al.  Neural networks and radial basis functions in classifying static speech patterns , 1990 .

[12]  J. Stephen Judd,et al.  Neural network design and the complexity of learning , 1990, Neural network modeling and connectionism.

[13]  Sholom M. Weiss,et al.  Computer Systems That Learn , 1990 .

[14]  Richard G. Priest,et al.  Pattern classification using projection pursuit , 1990, Pattern Recognit..

[15]  Eduardo D. Sontag,et al.  Mathematical Control Theory: Deterministic Finite Dimensional Systems , 1990 .

[16]  Eduardo D. Sontag,et al.  Feedback Stabilization Using Two-Hidden-Layer Nets , 1991, 1991 American Control Conference.

[17]  Anders Krogh,et al.  Introduction to the theory of neural computation , 1994, The advanced book program.

[18]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[19]  Jay A. Farrell,et al.  A Computationally Efficient Algorithm for Training Recurrent Connectionist Networks , 1992, 1992 American Control Conference.

[20]  Hava T. Siegelmann,et al.  Some recent results on computing with 'neural nets' , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[21]  Peter J. Gawthrop,et al.  Neural networks for control systems - A survey , 1992, Autom..

[22]  Ronald L. Rivest,et al.  Training a 3-node neural network is NP-complete , 1988, COLT '88.

[23]  Hava T. Siegelmann,et al.  Some results on computing with `neural nets , 1992 .

[24]  Martin Anthony,et al.  Computational learning theory: an introduction , 1992 .

[25]  Eduardo Sontag,et al.  For neural networks, function determines form , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[26]  Eduardo D. Sontag,et al.  Feedforward Nets for Interpolation and Classification , 1992, J. Comput. Syst. Sci..

[27]  Héctor J. Sussmann,et al.  Uniqueness of the weights for minimal feedforward nets with a given input-output map , 1992, Neural Networks.

[28]  Donald A. Sofge,et al.  Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches , 1992 .

[29]  David Haussler,et al.  Decision Theoretic Generalizations of the PAC Model for Neural Net and Other Learning Applications , 1992, Inf. Comput..

[30]  Eduardo D. Sontag,et al.  Rate of approximation results motivated by robust neural network learning , 1993, COLT '93.

[31]  Eduardo D. Sontag,et al.  UNIQUENESS OF WEIGHTS FOR NEURAL NETWORKS , 1993 .

[32]  Allan Pinkus,et al.  Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function , 1991, Neural Networks.

[33]  R. M. Sanner,et al.  Neural Networks for Adaptive Control and Recursive Identification: A Theoretical Framework , 1993 .

[34]  Eduardo D. Sontag,et al.  Finiteness results for sigmoidal “neural” networks , 1993, STOC.