30 years of adaptive neural networks: perceptron, Madaline, and backpropagation
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[1] G. TEMPLE,et al. Relaxation Methods in Engineering Science , 1942, Nature.
[2] Norbert Wiener,et al. Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications , 1949 .
[3] H. W. Bode,et al. A Simplified Derivation of Linear Least Square Smoothing and Prediction Theory , 1950, Proceedings of the IRE.
[4] D. Whitteridge,et al. Learning and Relearning , 1959, Science's STKE.
[5] Louise Hay,et al. THE NUMBER OF ORTHANTS IN N-SPACE INTERSECTED BY AN S-DIMENSIONAL SUBSPACE , 1960 .
[6] Lawrence W. Stark,et al. Computer pattern recognition techniques: electrocardiographic diagnosis , 1962, CACM.
[7] B. Widrow,et al. Generalization and information storage in network of adaline 'neurons' , 1962 .
[8] H. D. Block. The perceptron: a model for brain functioning. I , 1962 .
[9] J. S. Koford,et al. Real‐Time Adaptive Speech‐Recognition System , 1963 .
[10] Karl Steinbuch,et al. Learning Matrices and Their Applications , 1963, IEEE Trans. Electron. Comput..
[11] Frank Rosenblatt,et al. PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS , 1963 .
[12] R. E. Kalman,et al. Optimum Seeking Methods. , 1964 .
[13] Norbert Wiener,et al. Extrapolation, Interpolation, and Smoothing of Stationary Time Series , 1964 .
[14] F. K. Becker,et al. Automatic equalization for digital communication , 1965 .
[15] F. K. Becker,et al. Automatic equalization for digital communication , 1965 .
[16] Filson Henry Glanz,et al. Statistical extrapolation in certain adaptive pattern-recognition systems , 1965 .
[17] D F Specht,et al. Vectorcardiographic diagnosis using the polynomial discriminant method of pattern recognition. , 1967, IEEE transactions on bio-medical engineering.
[18] Donald F. Specht,et al. Generation of Polynomial Discriminant Functions for Pattern Recognition , 1967, IEEE Trans. Electron. Comput..
[19] and C.L. Coates Lewis,et al. Threshold Logic , 1967 .
[20] M. M. Sondhi,et al. An adaptive echo canceller , 1967 .
[21] B. Widrow,et al. Adaptive antenna systems , 1967 .
[22] R W Lucky,et al. Principles of data communication , 1968 .
[23] A. G. Ivakhnenko,et al. Polynomial Theory of Complex Systems , 1971, IEEE Trans. Syst. Man Cybern..
[24] Bernard Widrow,et al. Punish/Reward: Learning with a Critic in Adaptive Threshold Systems , 1973, IEEE Trans. Syst. Man Cybern..
[25] Thomas Kailath,et al. A view of three decades of linear filtering theory , 1974, IEEE Trans. Inf. Theory.
[26] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[27] B. Widrow,et al. Adaptive noise cancelling: Principles and applications , 1975 .
[28] James S. Albus,et al. New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .
[29] D. Casasent,et al. Position, rotation, and scale invariant optical correlation. , 1976, Applied optics.
[30] K. Senne,et al. Performance advantage of complex LMS for controlling narrow-band adaptive arrays , 1981 .
[31] J J Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.
[32] Richard S. Sutton,et al. Neuronlike adaptive elements that can solve difficult learning control problems , 1983, IEEE Transactions on Systems, Man, and Cybernetics.
[33] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.
[34] David G. Luenberger,et al. Linear and nonlinear programming , 1984 .
[35] Bernard Widrow,et al. The least mean fourth (LMF) adaptive algorithm and its family , 1984, IEEE Trans. Inf. Theory.
[36] Bernard Widrow,et al. Adaptive Signal Processing , 1985 .
[37] Yaser S. Abu-Mostafa,et al. Information capacity of the Hopfield model , 1985, IEEE Trans. Inf. Theory.
[38] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[39] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[40] Richard P. Lippmann,et al. An introduction to computing with neural nets , 1987 .
[41] Colin Giles,et al. Learning, invariance, and generalization in high-order neural networks. , 1987, Applied optics.
[42] Pineda,et al. Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.
[43] C. Lee Giles,et al. Encoding Geometric Invariances in Higher-Order Neural Networks , 1987, NIPS.
[44] Eric B. Baum,et al. Supervised Learning of Probability Distributions by Neural Networks , 1987, NIPS.
[45] W. Thomas Miller,et al. Sensor-based control of robotic manipulators using a general learning algorithm , 1987, IEEE J. Robotics Autom..
[46] B Kosko,et al. Adaptive bidirectional associative memories. , 1987, Applied optics.
[47] Stephen Grossberg,et al. A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..
[48] S. Venkatesh. Epsilon capacity of neural networks , 1987 .
[49] Bernard Widrow,et al. Adaptive inverse control , 1987, Proceedings of 8th IEEE International Symposium on Intelligent Control.
[50] Terrence J. Sejnowski,et al. Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..
[51] Y S Abu-Mostafa,et al. Neural networks for computing , 1987 .
[52] Charles M. Newman,et al. Memory capacity in neural network models: Rigorous lower bounds , 1988, Neural Networks.
[53] Teuvo Kohonen,et al. Self-Organization and Associative Memory , 1988 .
[54] Richard Fozzard,et al. A Connectionist Expert System that Actually Works , 1988, NIPS.
[55] Eric B. Baum,et al. On the capabilities of multilayer perceptrons , 1988, J. Complex..
[56] Esther Levin,et al. Accelerated Learning in Layered Neural Networks , 1988, Complex Syst..
[57] Bernard Widrow,et al. Adaptive switching circuits , 1988 .
[58] Terrence J. Sejnowski,et al. NETtalk: a parallel network that learns to read aloud , 1988 .
[59] Alireza Khotanzad,et al. Rotation invariant pattern recognition using Zernike moments , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.
[60] D. G. Bounds,et al. A multilayer perceptron network for the diagnosis of low back pain , 1988, IEEE 1988 International Conference on Neural Networks.
[61] J. Shynk,et al. The LMS algorithm with momentum updating , 1988, 1988., IEEE International Symposium on Circuits and Systems.
[62] Christoph von der Malsburg,et al. Pattern recognition by labeled graph matching , 1988, Neural Networks.
[63] Yann LeCun,et al. A theoretical framework for back-propagation , 1988 .
[64] Robert A. Jacobs,et al. Increased rates of convergence through learning rate adaptation , 1987, Neural Networks.
[65] Alberto L. Sangiovanni-Vincentelli,et al. Efficient Parallel Learning Algorithms for Neural Networks , 1988, NIPS.
[66] B. Irie,et al. Capabilities of three-layered perceptrons , 1988, IEEE 1988 International Conference on Neural Networks.
[67] Bernard Widrow,et al. Neural nets for adaptive filtering and adaptive pattern recognition , 1988, Computer.
[68] Eduardo D. Sontag,et al. Backpropagation Can Give Rise to Spurious Local Minima Even for Networks without Hidden Layers , 1989, Complex Syst..
[69] Yoh-Han Pao,et al. Functional link nets: removing hidden layers , 1989 .
[70] David Haussler,et al. What Size Net Gives Valid Generalization? , 1989, Neural Computation.
[71] Barak A. Pearlmutter. Learning State Space Trajectories in Recurrent Neural Networks , 1989, Neural Computation.
[72] B. Widrow,et al. The truck backer-upper: an example of self-learning in neural networks , 1989, International 1989 Joint Conference on Neural Networks.
[73] A. Owens,et al. Efficient training of the backpropagation network by solving a system of stiff ordinary differential equations , 1989, International 1989 Joint Conference on Neural Networks.
[74] P. M. Shea,et al. Detection of explosives in checked airline baggage using an artificial neural system , 1989, International 1989 Joint Conference on Neural Networks.
[75] Sontag,et al. Backpropagation separates when perceptrons do , 1989 .
[76] S. Tam,et al. An electrically trainable artificial neural network (ETANN) with 10240 'floating gate' synapses , 1990, International 1989 Joint Conference on Neural Networks.
[77] Geoffrey E. Hinton,et al. Phoneme recognition using time-delay neural networks , 1989, IEEE Trans. Acoust. Speech Signal Process..
[78] Ronald J. Williams,et al. A Learning Algorithm for Continually Running Fully Recurrent Neural Networks , 1989, Neural Computation.
[79] H. White,et al. Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions , 1989, International 1989 Joint Conference on Neural Networks.
[80] Kumpati S. Narendra,et al. Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.
[81] L. B. Almeida. A learning rule for asynchronous perceptrons with feedback in a combinatorial environment , 1990 .
[82] M. W. Roth. Survey of neural network technology for automatic target recognition , 1990, IEEE Trans. Neural Networks.
[83] Stephen Grossberg,et al. ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures , 1990, Neural Networks.
[84] Bernard Widrow,et al. Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[85] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..