Опубликован: 25.12.2006 | Доступ: свободный | Студентов: 1832 / 361 | Оценка: 4.43 / 4.13 | Длительность: 15:29:00
Специальности: Программист, Экономист
  • 1.
    Моделирование элементов мышления
  • 2.
    Т. Кохонен
    Ассоциативная память
  • 3.
    Принципы нейродинамики
  • 4.
    Ф. Уоссерман
    Нейрокомпьютерная техника
  • 5.
    Введение в нейрокомпьютинг
  • 6.
    Arbib M., ed
    The Handbook of Brain Theory and Neural Networks
  • 7.
    Обучение нейронных сетей
  • 8.
    А.Н.Горбань, Д.А.Россиев
    Нейронные сети на персональном компьютере
  • 9.
    Ф.В. Широков
    Нейросети на шине VME. Краткая история нейроинформатики
  • 10.
    Anderson, E, J. A. and Rosenfeld
    Neurocomputing: Foundations of Research
  • 11.
    A. and Rosenfeld. E, Anderson, J. A., Pellionisz
    Neurocomputing 2: Directions for Research
  • 12.
    Beltratti A., Margarita S., Terna P
    Neural Networks for Economic and Financial Modeling
  • 13.
    Bishop C.M
    Neural Networks and Pattern Recognition
  • 14.
    Haykin, S
    Neural Networks, a Comprehensive Foundation
  • 15.
    Hecht-Nielsen. R
  • 16.
    Hopfield, J.J
    Neural networks and physical systems with emergent collective computational abilities
  • 17.
    Hopfield, J.J
    Neurons with graded response have collective computational properties like those of two-state neurons
  • 18.
    McCulloch, Pitts. W., W.S.
    A logical calculus of the ideas immanent in nervous activity
  • 19.
    A., C. and Pap. R, Harston, Maren
    Handbook of Neural Computing Applications
  • 20.
    C, Mead
    Analog VLSI and neural systems
  • 21.
    M. and Papert. S, Minsky
    Perceptrons. MIT Press
  • 22.
    B. and Reinhardt, J, М. Muller
    Neural Networks, An Introduction. Springer-Verlag. 1990.Rosenblatt, F. (1969). Principles of Neurodynamics
  • 23.
    Murray A.F
    Applications of Neural Nets
  • 24.
    Pao, Y. H
    Adaptive Pattern Recognition and Neural Networks
  • 25.
    and Wiliams, D.E., G.E., Hinton, R.J, Rumelhart
    Learning internal representations by error propagation, in: McClelland, J. L. and Rumelhart, D. E. (Eds.). Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Volume 1, 318-362
  • 26.
    Bishop C.M
    Neural Networks and Pattern Recognition
  • 27.
    Fausett, L.V
    Fundamentals of Neural Networks: Architectures, Algorithms and Applications
  • 28.
    A., and Palmer, Hertz, J., Krogh, R
    Introduction to the Theory of Neural Computation
  • 29.
    Masters, T
    Practical Neural Network Recipes in C++
  • 30.
    M. and Illingworth, McCord Nelson, W.T
    A Practical Guide to Neural Nets
  • 31.
    A.J., Bell, Sejnowsky, T.J
    An information-maximization approach to Blind Separation and Blind Deconvolution
  • 32.
    Bishop, C.M
    Neural Networks and Pattern Recognition
  • 33.
    Deboeck, G. and Kohonen, T. (Eds)
    Visual Explorations in Finance with Self-Organizing Maps
  • 34.
    D.O, Hebb
    The Organization of Behavio
  • 35.
    A., and Palmer, Hertz, J., Krogh, R
    Introduction to the Theory of Neural Computation
  • 36.
    Kohonen, T
    Self-organized formation of topologically correct feature maps
  • 37.
    Kohonen, T
    Self-Organizing Maps. Springer, 1997 (2-nd edition)
  • 38.
    Linsker, R
    Local synaptic learning rules suffice to maximize mutual information in linear network
  • 39.
    E, Oja
    A simplified neuron model as a Principal Component Analyzer
  • 40.
    and Wangviwattana J, E., H., Ogawa, Oja
    Learning in nonlinear constrained Hebbian networks, in Artificial Neural Networks (Proc. ICANN-91), T.Kohonen et al. (Eds.)
  • 41.
    Crick, F. & Mitchison G.
    The function of dream sleep
  • 42.
    Diderich, M, S. & Opper
    Learning of Correlated Patterns in Spin-Glass Networks by Local Learning Rules
  • 43.
    A., A. & Knizhnikova, Ezhov, Kalambet, L.A. (1990) ., Yu.
    Neural networks: general properties and particular applications
  • 44.
    A., Ezhov
    Empty classes, predictive and clustering thinking networks
  • 45.
    A., A. & Vvedensky V.L, Ezhov
    Object generation with neural networks (when spurious memories are useful)
  • 46.
    Hassoun M.H. ed
    Associative Neural Memories: Theory and Implementation
  • 47.
    Hopfield, J.
    Neural Networks and Physical Systems with Emergent Collective Computational Abilities
  • 48.
    Hopfield, J, J.
    Neurons with Graded Response Have Collective Computational Properties Like Those of Two-State Neurons
  • 49.
    & Palmer, D., Feinstein, Hopfield, I., J., R.G
    Unlearning has a stabilizing effect in collective memories
  • 50.
    Kinzel, W
    Learning and pattern recognition in spin glass models
  • 51.
    Kohonen, T
    Self-organization and Associative Memory
  • 52.
    & Strikland M., B., J, Muller, Reinhardt, T
    Neural Networks. An Introduction. 2nd edition
  • 53.
    & Levchenko, A., B, E., Ezhov, Kamchatnov, Knizhnikova, L., M., Vedenov
  • 54.
    Кук, С
    Обзор вычислительной сложности. Тьюринговская лекция в: Лекции лауреатов премии Тьюринга за первые двадцать лет 1966-1985
  • 55.
    and Ignizio, Burke, J, L., L.., P
    Neural networks and operations research: An overview
  • 56.
    A. and Unbehauen, Cichocki, R
    Neural Networks for Optimization and Signal Processing
  • 57.
    B., Cooper, S
    Higher order neural networks - can they help us optimise?
  • 58.
    B.S, Cooper
    A comparison of the number of stable points of oprimisation networks
  • 59.
    and Gambardella, Dorigo, L., M, M.
    Ant colonies for the traveling salesman problem
  • 60.
    and Willshaw, D, Durbin, R.
    An analogue approach to the travelling salesman problem using an elastic net method
  • 61.
    F. and Walker, Favata, R
    A study of the application of Kohonen-type neural network to the travelling salesman problem
  • 62.
    B. and Wilke, Fritzke, P
    FLEXMAP - A neural network for the travelling salesman problem with linear time and space complexity
  • 63.
    D, Fogel
    Applying evolutionary programming to selected traveling salesman problems. Cybernetics and Systems
  • 64.
    A., Gee, H
    Problem solving with optimization networks
  • 65.
    and Peterson, B., C, Gislen, L., Soderberg
    Complex scheduling with Potts neural networks
  • 66.
    and Peterson, B., C, Gislen, L., Soderberg
    Teachers and classes with neural networks
  • 67.
    & Tank, D., Hopfield J., J., W
    Neural computation of decisions in optimization problems
  • 68.
    B., Lin, S. & Kernigan, W
    An effective heuristic algorithm for the travelling-salesman problem
  • 69.
    C, Looi
    Neural networks methods in combinatorial optimization
  • 70.
    A., E., H., J, M., Metropolis, N., Rosenbluth, Teller, W.
  • 71.
    J, von Neumann
    A certain zero-sum two-person game equivalent to the optimal assignment problem
  • 72.
    A, D., G. and Beyer, Ogier, R.
    Neural network solution to the link scheduling problem using convex relaxation
  • 73.
    and Soderberg, B, C., M., Ohlsson, Peterson
    Neural networks for optimization problems with inequality constraints - the knapsack problem
  • 74.
    B, G. and Soderberg, Peterson
    A new method for mapping optimization problems onto neural networks
  • 75.
    J-Y, Potvin
    The travelling salesman problem - A neural network perspective
  • 76.
    and Krishnamoorthy, K., M, Palaniswami. M., Smith
    Traditional heuristic versus Hopfield neural network approaches to car sequencing problem
  • 77.
    and Ignizio, J., P, S., Vaithyanathan
    A stochastic neural network for resource constrained scheduling
  • 78.
    G., S, V. and Pawley, Wilson
    On the stability of the Travelling Salesman Problem algorithm of Hopfield and Tank
  • 79.
    Bishop C.M
    Neural Networks and Pattern Recognition
  • 80.
    Rissanen J
    Complexity of Models, in Complexity, Entropy and the Physics of Information
  • 81.
    Александер Г.Дж., Бэйли, Дж. В, У.Ф., Шарп
  • 82.
    Abu-Mostafa, Y.S
    Financial market applications of learning from hints
  • 83.
    A., and Terna, Beltratti, Margarita, P, S.
    Neural Networks for Economic and Financial Modeling
  • 84.
    Chorafas, D.N
    Chaos Theory in the Financial Markets
  • 85.
    Colby, Meyers, R.W., T.A
    The Encyclopedia of Technical Market Indicators
  • 86.
    Ehlers, J.F
    MESA and Trading Market Cycles
  • 87.
    G, Kaiser
    A Friendly Guide to Wavelets
  • 88.
    and Lucas, C., D.W, LeBeau
    Technical traders guide to computer analysis of futures market
  • 89.
    E.E, Peters
    Fractal Market Analysis
  • 90.
    M.G, Pring
    Technical Analysis Explained
  • 91.
    Plummer, T
    Forecasting Financial Markets
  • 92.
    and Casdagli, J.A., M, Sauer, T., Yorke
  • 93.
    and Rogers, eds, R.D., V.R., Vemuri
    Artificial Neural Networks. Forecasting Time Series
  • 94.
    A and Gershenfield, eds, Weigend
    Times series prediction: Forecasting the future and understanding the past
  • 95.
    Бэстенс, В.-М., Ван Ден Берг, Вуд, Д, Д.-Э.
    Нейронные сети и финансовые рынки. Принятие решений в торговых операциях
  • 96.
    & Shavlik, Craven, J., M., W, W.
    Extracting tree-structured representations of trained networks
  • 97.
    Lu Hongjun, R. and Liu Huan, Setiono
    NeuroRule: A connectionist approach to Data Mining
  • 98.
    A., G, S. and Zimmerman H., Weigend
  • 99.
    A., and Neuneier, G., H., R, S., Weigend, Zimmermann
    Clearning. In Neural Networks in Financial Engineering
  • 100.
    Бэстенс, В.-М., Ван Ден Берг, Вуд, Д, Д.-Э.
    Нейронные сети и финансовые рынки. Принятие решений в торговых операциях
  • 101.
    Александер, Бейли, Г., Д, У., Шарп
  • 102.
    Altman, E. I
    Financial ratios, Discriminant analysis and the prediction of corporate bankruptcy
  • 103.
    Altman, E. I.
    Defaults and returns on high-yield bonds through thr first half of 1991
  • 104.
    and Shekhar, Dutta, S, S.
    Bond Rating: A Non-Conservative Application of Neural Networks
  • 105.
    Horrigan, J.O
    The determination of long term credit standing with financial ratios
  • 106.
    J, J. and Utans, Moody
    Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Prediction
  • 107.
    A.V, and Yarovoy, S.A., Shumsky
    Self-Organizing Atlas of Russian Banks
  • 108.
    and Turban, E., eds, R., Trippi
    Neural Networks in Finance and Investing
  • 109.
    R.R, West
    An alternative approach predicting corporate bond ratings
  • 110.
    C.Couvrer and P.Couvrer
    Neural Networks and Statistics: A Naive Comparison
  • 112.
    Neural Networks and Statistical Models