Опубликован: 25.12.2006 | Доступ: свободный | Студентов: 1807 / 331 | Оценка: 4.43 / 4.13 | Длительность: 15:29:00
Специальности: Программист, Экономист
  • 1.
    А.А.Веденов
    Моделирование элементов мышления
  • 2.
    Т. Кохонен
    Ассоциативная память
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    Принципы нейродинамики
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    Нейрокомпьютерная техника
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    Введение в нейрокомпьютинг
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    А.Н.Горбань
    Обучение нейронных сетей
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    А.Н.Горбань, Д.А.Россиев
    Нейронные сети на персональном компьютере
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    Ф.В. Широков
    Нейросети на шине VME. Краткая история нейроинформатики
  • 10.
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    Neurocomputing: Foundations of Research
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    Neurocomputing 2: Directions for Research
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    Bishop C.M
    Neural Networks and Pattern Recognition
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    Neural Networks, a Comprehensive Foundation
  • 15.
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    Neurocomputing
  • 16.
    Hopfield, J.J
    Neural networks and physical systems with emergent collective computational abilities
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    Hopfield, J.J
    Neurons with graded response have collective computational properties like those of two-state neurons
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    McCulloch, Pitts. W., W.S.
    A logical calculus of the ideas immanent in nervous activity
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    Analog VLSI and neural systems
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    Murray A.F
    Applications of Neural Nets
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    Adaptive Pattern Recognition and Neural Networks
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    Bishop C.M
    Neural Networks and Pattern Recognition
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    Fundamentals of Neural Networks: Architectures, Algorithms and Applications
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    Masters, T
    Practical Neural Network Recipes in C++
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    Bishop, C.M
    Neural Networks and Pattern Recognition
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    A., and Palmer, Hertz, J., Krogh, R
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  • 36.
    Kohonen, T
    Self-organized formation of topologically correct feature maps
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    Kohonen, T
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    Linsker, R
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    and Wangviwattana J, E., H., Ogawa, Oja
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    Crick, F. & Mitchison G.
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  • 42.
    Diderich, M, S. & Opper
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    Neural networks: general properties and particular applications
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    A., Ezhov
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    Hopfield, J.
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    Hopfield, J, J.
    Neurons with Graded Response Have Collective Computational Properties Like Those of Two-State Neurons
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    & 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
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    Kohonen, T
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    Кук, С
    Обзор вычислительной сложности. Тьюринговская лекция в: Лекции лауреатов премии Тьюринга за первые двадцать лет 1966-1985
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    and Ignizio, Burke, J, L., L.., P
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  • 56.
    A. and Unbehauen, Cichocki, R
    Neural Networks for Optimization and Signal Processing
  • 57.
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    B.S, Cooper
    A comparison of the number of stable points of oprimisation networks
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    and Gambardella, Dorigo, L., M, M.
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    A., Gee, H
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    and Peterson, B., C, Gislen, L., Soderberg
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    and Peterson, B., C, Gislen, L., Soderberg
    Teachers and classes with neural networks
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    & Tank, D., Hopfield J., J., W
    Neural computation of decisions in optimization problems
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    B., Lin, S. & Kernigan, W
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    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
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    J-Y, Potvin
    The travelling salesman problem - A neural network perspective
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    and Krishnamoorthy, K., M, Palaniswami. M., Smith
    Traditional heuristic versus Hopfield neural network approaches to car sequencing problem
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    and Ignizio, J., P, S., Vaithyanathan
    A stochastic neural network for resource constrained scheduling
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    G., S, V. and Pawley, Wilson
    On the stability of the Travelling Salesman Problem algorithm of Hopfield and Tank
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    Bishop C.M
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    Александер Г.Дж., Бэйли, Дж. В, У.Ф., Шарп
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    Abu-Mostafa, Y.S
    Financial market applications of learning from hints
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    A., and Terna, Beltratti, Margarita, P, S.
    Neural Networks for Economic and Financial Modeling
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    Chorafas, D.N
    Chaos Theory in the Financial Markets
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    Colby, Meyers, R.W., T.A
    The Encyclopedia of Technical Market Indicators
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    Ehlers, J.F
    MESA and Trading Market Cycles
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    G, Kaiser
    A Friendly Guide to Wavelets
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    and Lucas, C., D.W, LeBeau
    Technical traders guide to computer analysis of futures market
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    E.E, Peters
    Fractal Market Analysis
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    M.G, Pring
    Technical Analysis Explained
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    Plummer, T
    Forecasting Financial Markets
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    and Casdagli, J.A., M, Sauer, T., Yorke
    Embedology
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    and Rogers, eds, R.D., V.R., Vemuri
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    Times series prediction: Forecasting the future and understanding the past
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    Бэстенс, В.-М., Ван Ден Берг, Вуд, Д, Д.-Э.
    Нейронные сети и финансовые рынки. Принятие решений в торговых операциях
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    & Shavlik, Craven, J., M., W, W.
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    Lu Hongjun, R. and Liu Huan, Setiono
    NeuroRule: A connectionist approach to Data Mining
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    A., G, S. and Zimmerman H., Weigend
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    A., and Neuneier, G., H., R, S., Weigend, Zimmermann
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    Бэстенс, В.-М., Ван Ден Берг, Вуд, Д, Д.-Э.
    Нейронные сети и финансовые рынки. Принятие решений в торговых операциях
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    Александер, Бейли, Г., Д, У., Шарп
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    Altman, E. I
    Financial ratios, Discriminant analysis and the prediction of corporate bankruptcy
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    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
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    Horrigan, J.O
    The determination of long term credit standing with financial ratios
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    J, J. and Utans, Moody
    Architecture Selection Strategies for Neural Networks: Application to Corporate Bond Rating Prediction
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    A.V, and Yarovoy, S.A., Shumsky
    Self-Organizing Atlas of Russian Banks
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    and Turban, E., eds, R., Trippi
    Neural Networks in Finance and Investing
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    R.R, West
    An alternative approach predicting corporate bond ratings
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    C.Couvrer and P.Couvrer
    Neural Networks and Statistics: A Naive Comparison
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    W.S.Sarle
    Neural Networks and Statistical Models