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42. Rumelhart D.E.; Hinton G.E.; Williams R.J. Learning internal representation by error propagation. // Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations., Rumelhart D.E.; McClelland J.L., Eds. MIT Press: Cambridge, MA. - 1986. - P. 318-362. 43. Widrow B.; Hoff M.E. Adaptive switching circuits. // 1960 IREWESCON Convention Record, IRE: New York. - 1960. - P. 96-104. 44. Lehtokangas M.; Saarinen J. Weight initialization with reference patterns. // Neurocomputing. - 1998. - V. 20, № 1-3. - P. 265-278. 45. Yam J.Y.F.; Chow T.W.S. A weight initialization method for improving training speed in feedforward neural network. // Neurocomputing. - 2000. - V. 30, № 1-4. - P. 219-232. 46. Patnaik L.M.; Rajan K. Target detection through image processing and resilient propagation algorithms. // Neurocomputing. - 2000. - V. 35, № 1-4. - P. 123- 135. 47. Riedmiller M.; Braun H. A direct adaptive method for faster backpropagation learning: The RPROP algorithm. // Proceedings of the IEEE International Conference on Neural Networks. - 1993. - P. 586-591. 48. Hagan M.T.; Demuth H.B.; Beale M.H. Neural Network Design. - PWS Publishing: Cambridge, MA. - 1996. - 252 p. 49. Медведев В.С.; Потемкин В.Г. Нейронные сети. MATLAB 6. - ДИАЛОГ- МИФИ: М. - 2002. - 496 c. 50. Charalambous C. Conjugate gradient algorithm for efficient training of artificial neural netwoks. // IEEE Proceedings. - 1992. - V. 139, № 3. - P. 301-310. 51. Fletcher R.; Reeves C.M. Function minimization by conjugate gradients. // Computer Journal. - 1964. - V. 7. - P. 149-154. 52. Dennis J.; Schnabel R.B. Numerical Methods for Unconstrained Optimization and Nonlinear Equations. - Prentice-Hall: Englewood Cliffs, NJ. - 1983. - 378 p. 53. Hagan M.T.; Menhaj M. Training feedforward networks with the Marquardt algorithm. // IEEE Transactions on Neural Networks. - 1994. - V. 5, № 6. - P. 989- 993. 318
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- Page 365: СПИСОК ОБОЗНАЧЕНИЙ
42. Rumelhart D.E.; Hinton G.E.; Williams R.J. Learning internal representation<br />
by error propagation. // Parallel Distributed Processing: Explorations in the Microstructure<br />
of Cognition, Volume 1: Foundations., Rumelhart D.E.; McClelland J.L.,<br />
Eds. MIT Press: Cambridge, MA. - 1986. - P. 318-362.<br />
43. Widrow B.; Hoff M.E. Adaptive switching circuits. // 1960 IREWESCON Convention<br />
Record, IRE: New York. - 1960. - P. 96-104.<br />
44. Lehtokangas M.; Saarinen J. Weight initialization with reference patterns. //<br />
Neurocomputing. - 1998. - V. 20, № 1-3. - P. 265-278.<br />
45. Yam J.Y.F.; Chow T.W.S. A weight initialization method for improving training<br />
speed in feedforward neural network. // Neurocomputing. - 2000. - V. 30, № 1-4. - P.<br />
219-232.<br />
46. Patnaik L.M.; Rajan K. Target detection through image processing and resilient<br />
propagation algorithms. // Neurocomputing. - 2000. - V. 35, № 1-4. - P. 123-<br />
135.<br />
47. Riedmiller M.; Braun H. A direct adaptive method for faster backpropagation<br />
learning: The RPROP algorithm. // Proceedings of the IEEE International Conference<br />
on Neural Networks. - 1993. - P. 586-591.<br />
48. Hagan M.T.; Demuth H.B.; Beale M.H. Neural Network Design. - PWS Publishing:<br />
Cambridge, MA. - 1996. - 252 p.<br />
49. Медведев В.С.; Потемкин В.Г. Нейронные сети. MATLAB 6. - ДИАЛОГ-<br />
МИФИ: М. - 2002. - 496 c.<br />
50. Charalambous C. Conjugate gradient algorithm for efficient training of artificial<br />
neural netwoks. // IEEE Proceedings. - 1992. - V. 139, № 3. - P. 301-310.<br />
51. Fletcher R.; Reeves C.M. Function minimization by conjugate gradients. //<br />
Computer Journal. - 1964. - V. 7. - P. 149-154.<br />
52. Dennis J.; Schnabel R.B. Numerical Methods for Unconstrained Optimization<br />
and Nonlinear Equations. - Prentice-Hall: Englewood Cliffs, NJ. - 1983. - 378 p.<br />
53. Hagan M.T.; Menhaj M. Training feedforward networks with the Marquardt<br />
algorithm. // IEEE Transactions on Neural Networks. - 1994. - V. 5, № 6. - P. 989-<br />
993.<br />
318
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