Neural networks for control systems—A survey
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Aarts, 1989
Åström, 1989, Application of the robust and adaptive pole placement design technique, Int. J. of Adaptive Control and Signal Processing, 3, 167, 10.1002/acs.4480030209
Åström, 1989
Ackley, 1985, A learning algorithm for Boltzmann machines, Cognitive Science, 9, 147, 10.1207/s15516709cog0901_7
Aizerman, 1964, Theoretical foundation of the potential function method in pattern recognition learning, Automatika i Telemekhanika, 25, 1917
Albert, 1967
Albus, 1975, Data storage in the cerebellar model articulation controller (CMAC), Trans. of the ASME, J. of Dynamic Systems, Measurement, and Control, 97, 228, 10.1115/1.3426923
Albus, 1975, A new approach to manipulator control: The cerebellar model controller (CMAC), Trans. of the ASME, J. of Dynamic Systems, Measurement, and Control, 97, 220, 10.1115/1.3426922
Almeida, 1988, Backpropagation in perceptrons with feedback
Anderson, 1986
Anderson, 1989, Learning to control an inverted pendulum using neural networks, IEEE Control Systems Magazine, 9, 31, 10.1109/37.24809
Anderson, 1988
Atkenson, 1989, Using associative content-addressable memories to control robots, 1859
Ballard, 1988, Cortical connections and parallel processing: Structure and function, 563
Barron, 1991, Approximation and estimation bounds for artificial neural networks, 243
Barto, 1988, An approach to learning control surface by connectionist systems, 665
Barto, 1990, 5
Barto, 1983, Neuronlike adaptive elements that can solve difficult learning control problems, IEEE Trans. on System, Man, and Cybernetics, 13, 834, 10.1109/TSMC.1983.6313077
Bassi, 1990, Connectionist dynamic control of robotic manipulators
Bassi, 1989, Decomposition of neural network model of robot dynamics: a feasibility study, Simulation and AI, 220, 8
Bavarian, 1988, Introduction to neural networks for intelligent control, IEEE Control Systems Magazine, 8, 3, 10.1109/37.1866
Bhat, 1990, Use of neural nets for dynamical modelling and control of chemical process systems, Computers Chem. Engng., 14, 573, 10.1016/0098-1354(90)87028-N
Bhat, 1990, Modeling chemical process systems via neural computation, IEEE Control Systems Magazine, 10, 24, 10.1109/37.55120
Billings, 1985, Introduction to nonlinear system analysis and identification
Broomhead, 1988, Multivariable functional interpolation and adaptive networks, Complex Systems, 2, 321
Bruck, 1989, Computing with Networks of Threshold Elements
Bullock, 1983, How neural networks factor problems of sensory motor control, 2271
Burkill, 1970
Carrol, 1989, Construction of neural nets using the Radon transform
Casdagli, 1989, Non-linear system prediction of chaotic time series, Physica D, 35, 335, 10.1016/0167-2789(89)90074-2
Chen, 1989, Back-propagation neural network for nonlinear self-tuning adaptive control, 274
Chen, 1990, Practical identification of NARMAX models using radial basis functions, Int. J. Control, 52, 1327, 10.1080/00207179008953599
Chen, 1990, Non-linear system identification using neural networks, Int. J. Control, 51, 1191, 10.1080/00207179008934126
Chen, 1990, Parallel recursive prediction error algorithm for training layered neural networks, Int. J. Control, 51, 1215, 10.1080/00207179008934127
Chen, 1989, Learning control with neural networks, 1448
Chen, 1990, Problem-solving by using reinforcement learning neural nets, 583
Cheok, 1989, Lyapunov stability analysis for self-learning neural model with application to semiactive suspension control system, 329
Chester, 1990, Why two hidden layers are better than one, 265
Chi, 1990, Neural networks for system identification, IEEE Control Systems Magazine, 10, 31, 10.1109/37.55121
Chua, 1988, Cellular neural networks: Applications, IEEE Trans. on Circuits and Systems, 35, 1273, 10.1109/31.7601
Chua, 1988, Cellular neural networks: Theory, IEEE Trans. on Circuits and Systems, 35, 1257, 10.1109/31.7600
Ciliz, 1989, Time optimal control of mobile robot motion using neural nets, 368
Cluett, 1988, Robustness analysis of discrete-time adaptive control systems using input-output stability theory: A tutorial, 135, 133
Cohen, 1983, Absolute stability of global pattern formation and parallel memory storage by competitive neural networks, IEEE Trans. on System, Man, and Cybernetics, 13, 815, 10.1109/TSMC.1983.6313075
Cybenko, 1988, Continuous valued networks with two hidden layers are sufficient
Cybenko, 1989, Approximation by superpositions of a sigmoidal function, Math. Control Signal Systems, 2, 303, 10.1007/BF02551274
Daunicht, 1990, Defanet—a deterministic approach to function approximation by neural networks, 161
Demircioglu, 1988, Continuous-time relay self-tuning control, Int. J. Control, 47, 1061, 10.1080/00207178808906075
Ditto, 1990, Experimental control of chaos, Physical Review Letters, 65, 3211, 10.1103/PhysRevLett.65.3211
Dzieliński, 1990, Cellular neural network application to moiré pattern filtering
Economou, 1986, Internal model control. 5. Extension to nonlinear systems, Ind. Eng. Chem. Process Des. Dev., 25, 403, 10.1021/i200033a010
Ersü, 1984, A new concept for learning control inspired by brain theory, 7, 245
Eykhoff, 1974
Fang, 1990, Faster learning for dynamic recurrent backpropagation, Neural Computation, 2, 270, 10.1162/neco.1990.2.3.270
Farmer, 1987, Predicting chaotic time series, Physical Review Letters, 59, 845, 10.1103/PhysRevLett.59.845
Fletcher, 1987
Franklin, 1989, Input space representation for refinement learning control, 115
Fu, 1970, Learning control systems—review and outlook, Trans. IEEE on Aut. Control, 16, 210
Funahashi, 1989, On the approximate realization of continuous mappings by neural networks, Neural Networks, 2, 183, 10.1016/0893-6080(89)90003-8
Garcia, 1982, Internal model control—1. A unifying review and some new results, Ind. Eng. Chem. Process Des. Dev., 21, 308, 10.1021/i200017a016
Gawthrop, 1987, Robust stability of a continuous-time self-tuning controller, Int. J. of Adaptive Control and Signal Processing, 1, 31, 10.1002/acs.4480010104
Gawthrop, 1990, Robust stability of multi-loop continuous-time self-tuning controllers, Int. J. of Adaptive Control and Signal Processing, 4, 359, 10.1002/acs.4480040504
Gawthrop, 1990, Stochastic approximation and multilayer perceptrons: The gain back-propagation algorithm, Complex System J., 4, 51
Giles, 1987, Learning, invariance, and generalization in high-order neural networks, Applied Optics, 26, 4972, 10.1364/AO.26.004972
Girosi, 1989, Representation properties of networks: Kolmogorov's theorem is irrelevant, Neural Computation, 1, 465, 10.1162/neco.1989.1.4.465
Girosi, 1990, Networks and the best approximation property, Biological Cybernetics, 63, 169, 10.1007/BF00195855
Goldberg, 1988, Using a neural network to learn the dynamic of the CMU direct-drive arm II
Goodwin, 1984
Graf, 1988, A neural controller for collision-free movement of general robot manipulators, 77
Grant, 1989, A neural net approach to supervised learning of pole balancing, 123
Grossberg, 1976, Adaptive pattern classification and universal recording: I. parallel development and coding of neural feature detectors, Biological Cybernetics, 23, 121, 10.1007/BF00344744
Grossberg, 1988
Guez, 1988, Neural network architecture for control, IEEE Control Systems Magazine, 8, 22, 10.1109/37.1869
Guez, 1990, A neurocontroller with guaranteed performance for rigid robots, 347
Guhua, 1990, Setpoint control based on reinforcement learning, 511
Haber, 1990, Structure identification of nonlinear dynamic systems—A survey on input/output approaches, Automatica, 26, 651, 10.1016/0005-1098(90)90044-I
Hartman, 1992, Predicting the future with semi-local units, Neural Computation
Hebb, 1949
Hecht-Nielsen, 1987, Kolmogorov's mapping neural network existence theorem
Hecht-Nielsen, 1988, Neurocomputer applications, 445
Helferty, 1989, A neural network learning strategy for the control of a one-legged hopping machine, 1604
Hopfield, 1982, Neural networks and physical systems with emergent collective computational abilities, 79, 2554
Hopfield, 1984, Neurons with graded response have collective computational properties like those of two-state neurons, 81, 3088
Hornik, 1989, Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359, 10.1016/0893-6080(89)90020-8
Hunt, 1991, Neural networks for non-linear internal model control, 138, 431
IEEE, 1988, Special issue on neural networks, IEEE Control Systems Magazine, 8
IEEE, 1989, Special issue on neural networks, IEEE Control Systems Magazine, 9
IEEE, 1990, Special issue on neural networks, IEEE Control Systems Magazine, 10
Johanson, 1990, Real-time identification of multilinear systems, Vol. 3, 119
Johnson, 1991, Globally stable saturable learning laws, Neural Networks, 4, 47, 10.1016/0893-6080(91)90030-9
Jordan, 1991, Forward models: supervised learning with a distal teacher, 49
Josin, 1988, Neural-space generalization of a topological transformation, Biological Cybernetics, 59, 283, 10.1007/BF00332917
Josin, 1990, Development of a neural network autopilot model for high performance aircraft, 547
Karsai, 1989, Dynamic modelling and control of nonlinear process using neural network techniques, 280
Kawato, 1988, Hierarchical neural network model for voluntary movement with application to robotics, IEEE Control Systems Magazine, 8, 8, 10.1109/37.1867
Keerthi, 1986, Moving-horizon approximations for a general class of optimal nonlinear infinite-horizon discrete-time systems, 301
Kelly, 1990, Stability in contractive nonlinear neural networks, IEEE Trans. on Biomedical Engineering, 37, 231, 10.1109/10.52325
Kohonen, 1987
Kollias, 1989, An adaptive least squares for efficient training of artificial neural networks, IEEE Trans. on Circuits and Systems, 36, 1092, 10.1109/31.192419
Kolmogorov, 1957, On the representation of continuous functions of several variables by superposition of continuous functions of one variable and addition, Dokl. Akad. Nauk SSSR, 114, 953
Kosikov, 1987, Design of a nonsearching self-adjusting system for nonlinear plant, Automatika i Telemekhanika, 4, 58
Kosut, 1985, Robust adaptive control: conditions for global stability, IEEE Trans. on Aut. Control, AC-30, 610, 10.1109/TAC.1985.1104020
Kosut, 1984, An input-output view of robustness in adaptive control, Automatica, 20, 569, 10.1016/0005-1098(84)90008-6
Kraft, 1990, A comparison between CMAC neural network control and two traditional adaptive control systems, IEEE Control Systems Magazine, 10, 36, 10.1109/37.55122
Kumar, 1990, Adaptive pole placement for neurocontrol, 397
Kurkova, 1991, Kolmogorov's theorem is relevant, Neural Computation, 3, 617, 10.1162/neco.1991.3.4.617
Lane, 1991, Higher order CMAC neural networks—theory and practice, 1579
Lapedes, 1987, Non-linear signal processing using neural networks: prediction and system modelling
Lee, 1989, An intelligent controller based on approximate reasoning and reinforcement learning, 200
Li, 1988, Qualitative analysis and synthesis of a class of neural networks, IEEE Trans. on Circuits and Systems, 35, 976, 10.1109/31.1844
Lin, 1990, Adaptive nonlinear digital filter with canonical piecewise-linear structure, IEEE Trans. on Circuits and Systems, 37, 347, 10.1109/31.52728
Lippmann, 1987, An introduction to computing with neural nets, IEEE ASSP Magazine, 4, 4, 10.1109/MASSP.1987.1165576
Ljung, 1987
Ljung, 1983
Lorentz, 1976, Vol. 2, 419
Martinez, 1988, Three-dimensional neural net for learning visuomotor-coordination of a robot arm, 351
Mayne, 1990, Receding horizon control of nonlinear systems, Trans. IEEE on Aut. Control, 35, 814, 10.1109/9.57020
McCulloch, 1943, A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, 9, 127
Metropolis, 1953, Equation of state calculations by fast computing machines, J. Chemical Physics, 21, 1087, 10.1063/1.1699114
Michel, 1989, Qualitative analysis of neural networks, IEEE Trans. on Circuits and Systems, 36, 229, 10.1109/31.20200
Miller, 1987, Application of a general learning algorithm to the control of robotic manipulators, The Int. J. of Robotic Research, 6, 84, 10.1177/027836498700600207
Miller, 1990
Minsky, 1969
Minsky, 1988
Moody, 1989, Fast-learning in networks of locally-tuned processing units, Neural Computation, 1, 281, 10.1162/neco.1989.1.2.281
Morari, 1989
Narendra, 1990, 115
Narendra, 1989
Narendra, 1990, Identification and control for dynamic systems using neural networks, IEEE Trans. on Neural Networks, 1, 4, 10.1109/72.80202
Park, 1990, Neural computation for collision-free path planning
Pearlmutter, 1988, Learning state space trajectories in recurrent neural networks, 10.1162/neco.1989.1.2.263
Pearlmutter, 1989, Learning state space trajectories in recurrent neural networks, Neural Computation, 1, 263, 10.1162/neco.1989.1.2.263
Pearlmutter, 1990, Dynamic recurrent neural networks
Pineda, 1987, Generalization of back-propagation to recurrent neural networks, Physical Review Letters, 59, 2229, 10.1103/PhysRevLett.59.2229
Pineda, 1989, Recurrent backpropagation and the dynamical approach to adaptive neural computation, Neural Computation, 1, 161, 10.1162/neco.1989.1.2.161
Poggio, 1982, 128
Poggio, 1990, Networks for approximation and learning, 78, 1481
Porcino, 1990, An application of neural networks to the guidance of free-swimming submersibles, 417
Poteryaiko, 1991, On Liapunov function for neural networks models with multi-unit interaction, 21
Psaltis, 1988, A multilayered neural network controller, IEEE Control Systems Magazine, 8, 17, 10.1109/37.1868
Ritter, 1986, Topology conserving mappings for learning motor tasks, 151, 376
Ritter, 1988, Extending Kohonen's self-organizing mapping algorithm to learn ballistic movements, 393
Robinson, 1989, Dynamic error propagation networks
Robinson, 1987, Static and dynamic error propagation networks with application to speech coding
Rohrs, 1985, Robustness of continuous-time adaptive control in the presence of unmodeled dynamics, Trans. IEEE, AC-30, 881
Rosenblatt, 1958, The perceptron: a probabilistic model for information storage and organization in the brain, Psychological Review, 65, 386, 10.1037/h0042519
Rudin, 1976
Rumelhart, 1986, Learning internal representations by error propagation
Rumelhart, 1986
Sanner, 1990, Neuromorphic pitch attitude regulation of an underwater telerobot, IEEE Control Systems Magazine, 10, 62, 10.1109/37.55126
Sastry, 1989
Sato, 1990, Real time learning algorithm for recurrent analog neural networks, Biological Cybernetics, 62, 237, 10.1007/BF00198098
Savic, 1989, A new class of neural networks suitable for intelligent control, 418
Sbarbaro, 1991, Self-organization and adaptation in Gaussian networks, 454
Scalero, 1990, A fast algorithm for neural networks, 77
Sekiguchi, 1989, Behaviour control for mobile robot by multi-hierachical neural network, 1578
Shamma, 1989, Spatial and temporal processing in central auditory networks, 247
Söderström, 1989
Sudharsanan, 1990, Equilibrium uniqueness and global exponential stability of a neural network for optimization applications
Tan, 1990, Remarks on the stability of asymmetric dynamical neural networks
Troudet, 1989, Neoromorphic learning continuous valued mappings in presence of noise, 312
Tsypkin, 1971
Ungar, 1990, Adaptive networks for fault diagnosis and process control, Computers Chem. Engng., 14, 561, 10.1016/0098-1354(90)87027-M
Weigand, 1990, Predicting the future: a connectionist approach, Int. J. Neural Systems, 3, 193, 10.1142/S0129065790000102
Werbos, 1974, Beyond regression: new tools for prediction and analysis in the behavior sciences
Werbos, 1989, Maximizing long-term gas industry profits in two minutes in lotus using neural network methods, IEEE Trans. on Systems, Man, and Cybernetics, 19, 315, 10.1109/21.31036
Werbos, 1990, Backpropagation through time: what it does and how to do it?, 78, 1550
Widrow, 1986, Adaptive inverse control, 1
Widrow, 1960, Adaptive switching circuits, 1960 IRE WESCON Convention Record, New York: IRE, 96
Widrow, 1990, 30 years of adaptive neural networks: Perceptron, medaline, and backpropagation, 78, 1415
Widrow, 1985
Widrow, 1988, Neural nets for adaptive filtering and adaptive pattern recognition, IEEE Computer, 21, 25, 10.1109/2.29
Wiener, 1948
Williams, 1989, Experimental analysis of the real-time recurrent learning algorithm, Connection Science, 1, 87, 10.1080/09540098908915631
Williams, 1989, A learning algorithm for continually running fully recurrent neural networks, Neural Computation, 1, 270, 10.1162/neco.1989.1.2.270
Williams, 1990, Gradient-based learning algorithms for recurrent connectionist networks
Willis, 1991, On artificial neural networks in process engineering, 138, 256
Wolfram, 1986
International Workshop. Cellular neural networks and their applications. Proc. CNNA'90.
Ydstie, 1990, Forecasting and control using adaptive connectionist networks, Computers Chem. Engng., 14, 583, 10.1016/0098-1354(90)87029-O
Yeung, 1989, Using a context-sensitive learning network for robot arm control, 1441
Zak, 1990, Robust tracking control of dynamic systems with neural networks, 563
Zhao, 1988, An artificial neural minimum-variance estimator, 499