Список литературы

И.В.Кисель, В.ННескоромный, Г.А.Ососков, Применение нейронных сетей в экспериментальной физике // ЭЧАЯ, т.24, вып.6, 1993, 1551-1595.

Д.Лоули, А.Максвелл, Факторный анализ как статистический Метод,// М.,Мир, 1967.

Уоссермен Ф. Нейрокомпьютерная техника : Теория и практика.//М.: Мир. 1992.

Лоскутов А.Ю., Михайлов А.С. Введение в синергетику. // М.: Наука, Гл. ред. физ.-мат. лит., 1990.

Новые физические принципы оптической обработки информации: // Сборник статей Под ред. С.А. Ахманова и М. А. Воронцова. - М.: Наука. Гл. ред. физ.-мат. лит., 1990.

Горбань А.Н. Обучение нейронных сетей. // М.: СП Параграф. 1991.

Барцев С.И., Гилев С.Е., Охонин В.А. Принцип двойственности в организации адаптивных сетей обработки информации // Динамика химических и биологических систем. Новосибирск: Наука, 1989, стр.655.

Барцев С.И., Охонин В.А. Адаптивные сети обработки информации.// Красноярск : Ин-т физики СО АН СССР, 1986. Препринт N 59Б, - 20с.

{barcev3} Барцев С.И. Некоторые свойства адаптивных сетей (Программная реализация). // Красноярск: Ин-т физики СО АН СССР, 1987. Препринт No.71B. - 17 с.

; Ю.К. Ахапкин, С.И. Барцев, Н.Н. Всеволодов и др. Биотехника - новое направление компьютеризации //М.: Наука, 1990. - 144 с.

Гилл Ф., Мюррей У., Райт М. Практическая оптимизация. // М.: Мир, 1985. - 509 с.

Евтихиев Н.Н., Оныкий Б.Н., Перепелица В.В., Щербаков И.Б. Многослойная нейронная сеть и ее реализация на основе оптического вектор-матричного перемножителя // Нейрокомпьютер, No. 1-2, 1994.

Евтихиев Н.Н., Оныкий Б.Н., Перепелица В.В., Щербаков И.Б. Математические модели и оптические реализации многослойных и полиномиальных нейронных сетей. // Препринт/МИФИ, 004-94,. 1994. - 32 с.

Копосов А.И., Щербаков, И.Б., Кисленко Н.А., Кисленко О.П., Варивода Ю.В. и др.," Создание аналитического обзора информационных источников по применению нейронных сетей для задач газовой технологии"; // ВНИИГАЗ, 1995.

N.M. Astafyeva, I.M. Dremin, К.А. Kotelnikov// Mod. Phys. Lett., 1997, V. A12, p. 1185; I.M. Dremin et al.// hep-ph/0007060; И.М. Дремин // УФН, 2000, т. 170, c. 1235.

В.В.Ужинский, Г.А.Ососков, А.Полянский и др. Вейвлет-анализ угловых распределений вторичных частиц в ядро-ядерных взаимодействиях при высоких энергиях // Сообщ.ОИЯИ, Дубна, 2001.

\ Компьютерра, N8 (236), 1998.

[ 1 В] S. Wolfram (ed.), Theory and applications of cellular automata // World Scientific, 1986.

C.Peterson et al, JETNET 3.0 - A versatile artificial neural network package // LU TP 93-29,1993; CERN-TH 7135/94

C.Lindsey et al, Experience with the IBM ZISC036 neural network chip, // Proc. of IV Intern. Workshop on Software Engineering and Artificial Intelligence and Expert Systems for High Energy and Nuclear Physics, Pisa, Italy, 3-8 April 1995, ed. by B.Denby & D.Perret-Gallix, World Sci., Singapore, (1995), pp 371-376.

CXindsey, Th.Lindblad, Review of hardware neural networks: a user's perspective, // Intern. Journ. of Neural Systems, v.6 (Suppl.) (1995).

Nucl. Instr. Meth. A 389, 1997.

Intern. Joum. of Neural Systems, v.6 (Suppl.) (1995) 269-274.

New Computing Techniques in Physics Research, Proc. of VI Intern. Workshop on Software Engineering, // Artificial Intelligence and Expert Systems, Heraclion Crete (Greece) April 12-16 1999, ed. by G.Athanasiu & D.Perret-Gallix, Papisianou, Athene (2000).

VIII International Workshop on Advanced Ccomputing and Analysis Techniques in Physics Research (ACAT2002) 24-28 June, 2002, Moscow, Russia, http://acat02.sinp.msu.ru

J.Hopfield, Learning Algorithms and Probability Distributions in Feedforward and Feed-back Networks// Proc.

Nat. Acad. Sci. USA, 84,1987, p.8429.

J.Hopfield, Neural Networks and Physical Systems with Emergent Collective Computational Abilities //Proc. Nat. Acad. Sci. USA, 79, 1982, p.2554.

J.Hopfield, Neurons with Graded Responces Have Collective Computational Properties Like Those of Two-State

Neurons // Proc.

Kirkpatrick S. et al: Optimization by simulated annealing // Science 220 (1983), 671-680.

CPeterson, Track finding with neural networks // Nucl. Instr. and Meth. A279,p.537,1986.

[31 ] I B.Denby, Neural networks and cellular automata in experimental high energy physics // Comput. Phys. Commun., 49, p.429, 1988.

G.Ososkov et al, Neural Network Applications for Efficiency Improving of Geometric Reconstruction of Events Detected in the EXCHARM Experiment at the JINR // Proc. of VI Intern. Workshop on Software Engineering, Artificial Intelligence and Expert Systems, Heraclion Crete (Greece) April 12-16 1999, ed. By G.Athanasiu \& D.Perret-Gallix, Parisianou, Athene (2000) 126-131.

C.Peterson, Preprint LU TP 90 - 6 // Lund (1990).

{18} S.Baginyan et al., Tracking by modified rotor model of neural network // Сотр. Phys. Comm., 79 (1994) 165

I.Galkin, P.Neshuba, G.Ososkov, B.W.Reinush, E.Zaznobina, Feedback neural networks for ARTIST ionogram processing // Radio Science v.31, n 5 (1996) 1119-1128.

G.Ososkov, Robust tracking by cellular automata and neural network with non-local weights, in Applications and Science of Artificial Neural Networks // S. K. Rogers, D. W. Ruck, Editors, Proc. SPIE 2492, (1995) 1180-1192.

Grozov V.P., Ososkov G.A., Zaznobina E.G. and Nosov V.E., Automatic Processing of Ionograms on the basis of the artifical neural network method tl Proceedings of 1997 International Symposium on Radio Propagation (ISRP'97), Qingdao, China (1997) 514 - 517.

M.Gyulassy and M.Harlander, Elastic tracking and neural networks algorithms for complex pattern recognition // Comp.Phys.Comm. 66, 1991, 31-46.

M. Ohlsson, C. Peterson, A. Yuille, Track finding with deformable templates - the elastic arms approach // Comput. Phys. Commun. 71, (1992), 77.

L. Muresan, R. Muresan, G. Ososkov, Yu. Panebratsev, Deformable Templates for Circle Recognition // JINR Rapid Communications, l[81]-97, Dubna, 1997,27-44.

S.Baginyan, G.Ososkov, Finding tracks detected by a drift tube system // Comp.Phys.Comm, v. 108 No 1 (1998), 20-28.

Hough P. V. C. A Method and Means for Recognizing Complex Patterns. II US Patent: 3,069,654,1962.

Toft P. The Radon Transform. Theory and Implementation. // Ph.D. Thesis,Department of Mathematical Modelling, Section for Digital Signal Processing, Technical University of Denmark, 1996. http://www.ei.dtu.dk/staff/ptofl/ptoft.html ;

Haykin, Neural networks: a comprehensive foundation // MacMillan Coll.Publ.Co.,N-Y, 1994.

G.Ososkov, A.Stadnik, Face recognition by a new type of neural networks // in: "Advances in Neural Networks and Applications", Ed. N.Mastorakis, WSES Press, Hellenic Naval Academy, Athene, Greece, (2001) 304-308;

RChellappa et al, // Proc.IEEE v.83 No.5,1995, pp. 704-740

; H.Liu et al, // Optand Lasers, in Engeneer., V.30,1998, pp. 305-314

K.S.Yoon at al, Pattern Recogn. Vol.3,1998, pp. 283-293

Cambridge face data base, ftp://ftp.uk.research. att.com:pub/data/att_faces .zip

ROOT Library, http://www.rootxern.ch

Lippman R.P. An introduction to computing with neural nets // IEEE ASSP Magazine. Apr. 1987. P.4-22.

Holland J. Adaptation in Natural and Artificial Systems // University of Michigan Press, 1975.

Holland J. The dynamics of searches directed by Genetic Algorithms. // In: Lee Y.S. (ed.) Evolution, Learning and Cognition. - Word Scientific, Singapore, 1988.

Goldberg D. Genetic Algorithms in Machine Learning, Optimization, and Search. // Addison-Wesley, 1988.

Montana D.J. and Davis L. Training feedforward neural networks using genetic algorithms. // Preprint, BBN Systems and Technologies, Cambridge, Mass., 1989.

International workshop on combination of genetic algorithms and neural networks (1992; Baltimore, Md), June 6,1992. li COGANN-92; Ed. L.P. Whitley, J.P. Schoffer, Los Alamatic (Ca) et al.: IEEE computer, soc. press, 1992. - VIII, 262p.

H.M. Астафьева // УФН, 1996, т. 166, с. 1145.

Jones A .J. Genetic algorithms and their applications to the design of neural networks // Neural computing and applications, v.l, no. 1, 1993. (Есть в фонде ГПНТБ.)

Radcliffe N.J. Genetic set recombination and its

application to neural network topology optimization // Neural computing and

applications, v.l,no. l,pp. 67-90. 1993.

Haines K., Hecht-Nielsen R. A BAM with increased information storage capacity // Proceedings of the International Conference on Neural Networks,vol. 1, San Diego, CA: SOS Printing, 1988, pp. 181-190.

Hecht-Nielsen R. Theory of the backpropagation neural network // International joint conference on neural networks, Sheraton Washington Hotel, Washington D.C., June 18-22, vol. 1, 1989, p. 593-606.

Ackley D.H., Hinton G.E., Sejnowski T.J. A Learning Algorithm for Boltzmann Machines.// Cognitive Science, 9, 1985, pp. 147-169.

; Almeida L.B. A learning rule for asynchronous perceptions with feedback in a combinatorial environment // Proc. 1st IEEE Intl. Conf. on Neural Networks, vol. 2, pp. 609-618, San Diego, CA, June 1987.

Burr D.J. Experiments with a connectionist text reader // In Proceddings of the IEEE First International Conference on Neural Networks, eds. Caudill M., Butler C. vol 4, 1987, pp. 717-724. San Diego, CA: SOS Printing.

Cottrell G.W., Munro P. and Zipser D. Learning Internal Representation from Gray-Scale Images: An Example of Extensional Programming. // In Proc. 9th Annual Conference of the Cognitive Science Society, 1987, pp. 461473.

^ Dennis J., Schnabel R. Numerical Methods for Unconstrained; Optimization and Nonlinear Equations. // Englewood Cliffs, NJ: Prentice- Hall, 1983.

Gilev S.E., Gorban A.N., Mirkes E.M. Several methods for acceleration the training process of neural networks in pattern recognition. // USSR Academy of Sciences, Siberian Branch, Institute of Biophysics, Krasnoyarsk, 1990. Preprint N146 Б.

Gorman R.P., Sejnowski T. J. Analysis of Hidden Units in a Layered Network Trained to Classify Sonar Targets.

Guyon I., Poujaud I., Personnaz L., Dreyfus G., Denker J. and Le Cun Y. Comparing different neural network architectures for classifying handwritten digits. //In Proc. IEEE Int. Joint Conf. Neural Networks, June 1989.

Jones W.P., Hoskins J. Back-Propagation, A Generalized Delta Learning Rule // BYTE Magazine. Oct. 1987.

Jordan M. Generic constraints on underspecified target trajectories. // In Proc. IEEE Int. Joint Conf. Neural Networks, June 1989.

Kawato M. Computational schemes and neural network models for formation and control of multijoint arm trajectory. // In W.T. Miller, R. Sutton, and P. Werbos, Eds. Neural Networks for Robotics and Control. Cambridge, MA: M.I.T. Press, 1990.

Muller В., Reinhardt J. Neural networks. // Springer-Verlag. 1990. 267p.

Narendra K.S., Parthasarathy K. Identification and control of dynamical systems using neural networks. // IEEE Trans. Neural Networks, vol.1, pp.427, Mar. 1990.

Narendra R. Adaptive control using neural networks, it In W.T., Miller, R. Sutton, and P. Werbos, Eds. Neural Networks for Robotics and Control. Cambridge, MA: M.I.T. Press, 1990.

Neural Computing: Neural Works Professional II/Plus and Neural Works Explorer. // NeuralWare, Inc., 1991.355 p.

Nguyen D., Widrow B. The truck backer-upper:

An example of self-learning in neural networks, it In W.T. Miller, R. Sutton, and P. Werbos, Eds. Neural Networks for Robotics and Control. Cambridge, MA:; M.I.T. Press, 1990.

Pearlmutter B. Learning state space trajectories in recurrent neural network // In Proc. 1988 Connectionist Models Summer School, D. Touretzky, G. Hinton, and T. Sejnowski, Eds. June 17-26, 1988, pp. 113-117. San Mateo, CA: Morgan Kaufmann. And in Proc. Int. Joint. Conf. Neural Networks, June 1989.

Pineda F.J. Generalization of backpropagation to reccurent neural. networks. // In Phys. Rev. Lett., vol. 18, pp. 2229-2232,1987.

Pineda F.J. Generalization of backpropagation to reccurent and higher order networks. // In Proc. IEEE Conf. Neural Inform. Processing Syst., 1987, and in Neural Information Processing Systems, ed. D.Z. Anderson, pp. 602611. New York: American Institute of Phisycs. 1988.

Rosenberg C.R. Revealing the structure of NETtalk's Internal: Representations. // In Proc. 9th Annual Conference of the Cognitive Science Society, 1987, pp. 537-554.

Rumelhart D.E., Hinton G.E., Williams R.J. Learning Internal Representations by Error Propagation. // In Parallel Distributed Processing,, vol. 1, pp. 318-362. Cambridge, MA, MIT Press. 1986.

Rumelhart D.E., HintonG.E., Williams R.J. Learning Representations by Back-propagating Errors, // Nature vol. 323, p. 533. 1986.

Sawai H., Waibel A., Haffner P., Miyatake M. and Shikano K... Parallelism, hierarchy, scaling in time-delay neural networks for spotting Japanese phonemes CV-syllables. // In Proc. IEEE Int. Joint Conf. Neural Networks, June 1989.

Sejnowski T.J, Rosenberg C.R. Parallel Networks that Learn to Pronounce English Text. // Complex Systems, 1,1987, p.145-168.

Shanno D. Conjugate-gradient methods with inexact searches. // Math. Oper. Res., vol. 3, Aug. 1978.

Shanno D. Recent advances in numerical techniques for large-scale optimization. // In W.T. Miller, R. Sutton, and P. Werbos, Eds. Neural Networks for Robotics and Control. Cambridge, MA: M.I.T. Press, 1990.

Stornetta W.S., Huberman B.A. An improved three-layer,, backpropagation algorithm. // In Proceedings of the IEEE First Conference on

Neural Networks, eds. M. Caudill and C. Butler. San Diego, CA: SOS Printing. 1987.

Wasserman P.D. Combined backpropagation Cauchy machine. // Proceedings of the International Neural Network Society. New York: Pergamon Press, 1988.

Wasserman P.D. Experiments in transtating Chinese characters using backpropagation. // Proceedings of the Thirty-Third IEEE Computer Society International Conference. Washington, D.C.: Computer Society Press of the IEEE. 1988.

Watrous R., Shastri L. Learning phonetic features using connectionist networks: an experiment in speech recognition. // In Proc. 1st IEEE Int. Conf. Neural Networks, June 1987.

Werbos P. Applications of advances in nonlinear sensitivity analysis. // In R. Drenick and F. Kozin, Eds., Systems Modelling and Optimization: Proc. 10th IFIP Conf. (1981). New York: Springer-Verlag, 1982.

Werbos P. Learning how the word works: Specifications for predictive networks in robots and brains. // In Proc. 1987 IEEE Int. Conf. Syst, Ma, Cybern., 1987.

Werbos P. Consistency of HDP applied to a simple reinforcement learning problem// Neural Networks, Mar. 1990.

Werbos P. Generalization of backpropagation with application to a recurrent gas market model, (I Neural Networks, Oct, 1988.

Werbos P.J. Backpropagation through time: what it does and how to do it // Proceedings of the IEEE, vol. 78, No. 10, October, 1990, p. 1550-1560.

Werbos P. Maximizing long-term gas industry profits in two minutes in Lotus using neural networks methods. // IEEE Trans. Syst., Man, Cybern., Mar./Apr. 1989.

Widrow В., Lehr M.A. 30 years of adaptive neural networks: perceptron, madaline, and backpropagation // Proceedings of the IEEE, vol. 78, No. 9, September, 1990, p. 1415-1442.

Williams R. Adaptive state representation and

estimation using recurrent connectionist networks. // In W.T. Miller, R. Sutton,

and P. Werbos, Eds. Neural Networks for Robotics and Control.

Cambridge, MA: M.I.T. Press, 1990.

Kosko B.Bi-directional associative memories // IEEE Transactions on Systems, Man and Cybernetics 18(1), 1987, pp.49-60.

Kosko B. Competitive adaptive bi-directional associative memories. // In Proceedings of the IEEE First International Conference on Neural Networks, eds. M. Caudill and C. Butler, vol. 2, San Diego, CA: SOS Printing, 1987, pp. 759-766.

[ 102] Kosko B., Guest C. Optical bi-directional associative memories // SPIE Proceedings: Image Understanding, 758:11 -18,1987.

Kosko B. Unsupervised learning in noise // IJCNN Proc. vol. . 1, San Diego, CA: SOS Printing, 1989, pp. 7-18.

Neural Computing: Neural Works Professional II/Plus and NeuralWorks Explorer. NeuralWare, Inc., 1991. 355 p.

Hecht-Nielsen R. Counterpropagation networks К Applied Optics, 26(23), 1987, pp. 4979-84.

Hecht-Nielsen R. Applications of counterpropagation networks // Neural Networks, 1, 1988, pp. 131-39.

S.Thurner, M.Feurstein, M.Teich Multiresolution Wavelet Analysis of Heartbeat Intervals Discriminates Healthy Patients from Those with Cardiac Pathology. // Physical Review Letters, vol.80 (1998), pp. 1544-1547.

W.Sweldens, I.Daubechies Factoring Wavelet Transforms into Lifting Steps. // Fourier Analysis Applications, vol .4 (1998), pp.247-269.

W.Sweldens. Wavelets and Lifting Scheme: A 5 minute tour, // Zeitschnift far Angewandte Mathematik und Mechanik, vol.76(Suppl.2) (1996), pp. 41-44.

Mallat S. A theory for multiresolution signal decomposition: the wavelet representation. // IEEE Trans. Pattern Analysis and Machine Intelligence, 1989, N7, pp.674-693.

I. Daubechies Editor, Proc.of Symp. in Appl. Math, V47,1993.

W. Sweldens, P. Schroder. Building your own wavelets at home in "Wavelets in Computer Graphics", // ACM SIGGRAPH Course Notes, 1996, pp. 15-87.

: G.Ososkov, A.Shitov Gaussian Wavelet Features and Their Applications for Analysis of Discretized Signals // Comp.Phys.Comm, v. 126/1-2, (2000) 149-157, см.также Сообщ. ОИЯИ PI 1-97-347, Дубна, 1997.

M.V.Altaisky, E.A.Kolganova V.E.Kovalenko, G.A.Ososkov // The InternationalSociety for Optical Engineering, Proc. SPIE'96, v.2847 (1996) 656-664.

E.Kolganova, E.Kosarev, G.Ososkov Superresolution algorithms for data analysis of discrete detectors in nuclear physics // Nucl.Instr.Meth. A443/2-3 (2000) 464-477.

G. Ososkov, A.Shitov, A. S tadnik, Comparative study of some of wavelets of the first and the second generations // Comm.JINR El 1-2001-38, Dubna, 2001.